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1
Kechkhoshvili, Erekle, and Irina Khutsishvili. "For Flood Forecasting Issues." Works of Georgian Technical University, no. 2(532) (June 10, 2024): 265–72. http://dx.doi.org/10.36073/1512-0996-2024-2-265-272.
Повний текст джерелаАнотація:
. Global climate change has caused sharp increasing of natural calamities, including floods. In the course of recent period, over the entire world, every year there are occurring tens of cases of disastrous floods and waterflows characterized by damages worth of several millions and human losses. The issue of forecasting waterflows and floods, in general, is discussed in the article. There are given basic differentiating features-characteristics existing between spring floods and rain-caused waterflows. The methodology of forecasting related decision-making based on the Statistical Fuzzy Analysis is developed at Ivane Javakhishvili Tbilisi State University, which methodology can be used for flood forecasting. The methodology consists of two stages. At the first stage one and the same prognostic event is assessed using three methods, which allow to make independent forecast. At the second stage, according to the mentioned forecast, the final decision is made. The factors suggested for application of this methodology for flood forecasting are directly related to the climatic parameters of the territory and the state of a river bed and lower terrace. The basic goal of the methodology suggested for flood forecasting is timely reporting on an anticipated disaster.
2
Xu, Wei, and Yong Peng. "Research on classified real-time flood forecasting framework based on K-means cluster and rough set." Water Science and Technology 71, no. 10 (March 20, 2015): 1507–15. http://dx.doi.org/10.2166/wst.2015.128.
Повний текст джерелаАнотація:
This research presents a new classified real-time flood forecasting framework. In this framework, historical floods are classified by a K-means cluster according to the spatial and temporal distribution of precipitation, the time variance of precipitation intensity and other hydrological factors. Based on the classified results, a rough set is used to extract the identification rules for real-time flood forecasting. Then, the parameters of different categories within the conceptual hydrological model are calibrated using a genetic algorithm. In real-time forecasting, the corresponding category of parameters is selected for flood forecasting according to the obtained flood information. This research tests the new classified framework on Guanyinge Reservoir and compares the framework with the traditional flood forecasting method. It finds that the performance of the new classified framework is significantly better in terms of accuracy. Furthermore, the framework can be considered in a catchment with fewer historical floods.
3
Langdon, M. "Forecasting flood." Engineering & Technology 4, no. 7 (April 25, 2009): 40–42. http://dx.doi.org/10.1049/et.2009.0706.
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Ren, Juanhui, Bo Ren, Qiuwen Zhang, and Xiuqing Zheng. "A Novel Hybrid Extreme Learning Machine Approach Improved by K Nearest Neighbor Method and Fireworks Algorithm for Flood Forecasting in Medium and Small Watershed of Loess Region." Water 11, no. 9 (September 5, 2019): 1848. http://dx.doi.org/10.3390/w11091848.
Повний текст джерелаАнотація:
Sudden floods in the medium and small watershed by a sudden rainstorm and locally heavy rainfall often lead to flash floods. Therefore, it is of practical and theoretical significance to explore appropriate flood forecasting model for medium and small watersheds for flood control and disaster reduction in the loess region under the condition of underlying surface changes. This paper took the Gedong basin in the loess region of western Shanxi as the research area, analyzing the underlying surface and floods characteristics. The underlying surface change was divided into three periods (HSP1, HSP2, HSP3), and the floods were divided into three grades (great, moderate, small). The paper applied K Nearest Neighbor method and Fireworks Algorithm to improve the Extreme Learning Machine model (KNN-FWA-ELM) and proposed KNN-FWA-ELM hybrid flood forecasting model, which was further applied to flood forecasting of different underlying surface conditions and flood grades. Results demonstrated that KNN-FWA-ELM model had better simulation performance and higher simulation accuracy than the ELM model for flood forecasting, and the qualified rate was 17.39% higher than the ELM model. KNN-FWA-ELM model was superior to the ELM model in three periods and the simulation performance of three flood grades, and the simulation performance of KNN-FWA-ELM model was better in HSP1 stage floods and great floods.
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Mustamin, Muhammad Rifaldi, Farouk Maricar, Rita Tahir Lopa, and Riswal Karamma. "Integration of UH SUH, HEC-RAS, and GIS in Flood Mitigation with Flood Forecasting and Early Warning System for Gilireng Watershed, Indonesia." Earth 5, no. 3 (July 8, 2024): 274–93. http://dx.doi.org/10.3390/earth5030015.
Повний текст джерелаАнотація:
A flood forecasting and early warning system is critical for rivers that have a large flood potential, one of which is the Gilireng watershed, which floods every year and causes many losses in Wajo Regency, Indonesia. This research also introduces an integration model between UH SUH and HEC-RAS in flood impact analysis, as a reference for flood forecasting and early warning systems in anticipating the timing and occurrence of floods, as well as GIS in the spatial modeling of flood-prone areas. Broadly speaking, this research is divided into four stages, namely, a flood hydrological analysis using UH SUH, flood hydraulic tracing using a 2D HEC-RAS numerical model, the spatial modeling of flood-prone areas using GIS, and the preparation of flood forecasting and early warning systems. The results of the analysis of the flood forecasting and early warning systems obtained the flood travel time and critical time at the observation point, the total time required from the upstream observation point to level 3 at Gilireng Dam for 1 h 35 min, Mamminasae Bridge for 4 h 35 min, and Akkotengeng Bridge for 8 h 40 min. This is enough time for people living in flood-prone areas to evacuate to the 15 recommended evacuation centers.
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Brilly, M., and M. Polic. "Public perception of flood risks, flood forecasting and mitigation." Natural Hazards and Earth System Sciences 5, no. 3 (April 18, 2005): 345–55. http://dx.doi.org/10.5194/nhess-5-345-2005.
Повний текст джерелаАнотація:
Abstract. A multidisciplinary and integrated approach to the flood mitigation decision making process should provide the best response of society in a flood hazard situation including preparation works and post hazard mitigation. In Slovenia, there is a great lack of data on social aspects and public response to flood mitigation measures and information management. In this paper, two studies of flood perception in the Slovenian town Celje are represented. During its history, Celje was often exposed to floods, the most recent serious floods being in 1990 and in 1998, with a hundred and fifty return period and more than ten year return period, respectively. Two surveys were conducted in 1997 and 2003, with 157 participants from different areas of the town in the first, and 208 in the second study, aiming at finding the general attitude toward the floods. The surveys revealed that floods present a serious threat in the eyes of the inhabitants, and that the perception of threat depends, to a certain degree, on the place of residence. The surveys also highlighted, among the other measures, solidarity and the importance of insurance against floods.
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Anafi, Nurin Fadhlina Mohd, Norzailawati Mohd Noor, and Hasti Widyasamratri. "A Systematic Review of Real-time Urban Flood Forecasting Model in Malaysia and Indonesia -Current Modelling and Challenge." Jurnal Planologi 20, no. 2 (October 31, 2023): 150. http://dx.doi.org/10.30659/jpsa.v20i2.30765.
Повний текст джерелаАнотація:
Several metropolitan areas in tropical Southeast Asia, mainly in Malaysia and Indonesia have lately been witnessing unprecedentedly severe flash floods owing to unexpected climate change. The fast water flooding has caused extraordinarily serious harm to urban populations and social facilities. In addition, urban Southeast Asia generally has insufficient capacity in drainage systems, complex land use patterns, and a largely susceptible population in confined urban regions. To lower the urban flood risk and strengthen the resilience of vulnerable urban populations, it has been of fundamental relevance to create real-time urban flood forecasting systems for flood disaster prevention agencies and the urban public. This review examined the state-of-the-art models of real-time forecasting systems for urban flash floods in Malaysia and Indonesia. The real-time system primarily comprises the following subsystems, i.e., rainfall forecasting, drainage system modeling, and inundation area mapping. This review described the current urban flood forecasting modeling for rainfall forecasting, physical-process-based hydraulic models for flood inundation prediction, and data-driven artificial intelligence (AI) models for the real-time forecasting system. The analysis found that urban flood forecasting modeling based on data-driven AI models is the most applied in many metropolitan locations in Malaysia and Indonesia. The analysis also evaluated the existing potential of data-driven AI models for real-time forecasting systems as well as the challenges towards it
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Thiemig, V., B. Bisselink, F. Pappenberger, and J. Thielen. "A pan-African Flood Forecasting System." Hydrology and Earth System Sciences Discussions 11, no. 5 (May 27, 2014): 5559–97. http://dx.doi.org/10.5194/hessd-11-5559-2014.
Повний текст джерелаАнотація:
Abstract. The African Flood Forecasting System (AFFS) is a probabilistic flood forecast system for medium- to large-scale African river basins, with lead times of up to 15 days. The key components are the hydrological model LISFLOOD, the African GIS database, the meteorological ensemble predictions of the ECMWF and critical hydrological thresholds. In this paper the predictive capability is investigated in a hindcast mode, by reproducing hydrological predictions for the year 2003 where important floods were observed. Results were verified with ground measurements of 36 subcatchments as well as with reports of various flood archives. Results showed that AFFS detected around 70% of the reported flood events correctly. In particular, the system showed good performance in predicting riverine flood events of long duration (>1 week) and large affected areas (>10 000 km2) well in advance, whereas AFFS showed limitations for small-scale and short duration flood events. The case study for "Save flooding" illustrated the good performance of AFFS in forecasting timing and severity of the floods, gave an example of the clear and concise output products, and showed that the system is capable of producing flood warnings even in ungauged river basins. Hence, from a technical perspective, AFFS shows a large potential as an operational pan-African flood forecasting system, although issues related to the practical implication will still need to be investigated.
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Li, Jingyu, Yangbo Chen, Yanzheng Zhu, and Jun Liu. "Study of Flood Simulation in Small and Medium-Sized Basins Based on the Liuxihe Model." Sustainability 15, no. 14 (July 19, 2023): 11225. http://dx.doi.org/10.3390/su151411225.
Повний текст джерелаАнотація:
The uneven distribution of meteorological stations in small and medium-sized watersheds in China and the lack of measured hydrological data have led to difficulty in flood simulation and low accuracy in flood forecasting. Traditional hydrological models no longer achieve the forecasting accuracy needed for flood prevention. To improve the simulation accuracy of floods and maximize the use of hydrological information from small and medium-sized watersheds, high-precision hydrological models are needed as a support mechanism. This paper explores the applicability of the Liuxihe model for flood simulation in the Caojiang river basin and we compare flood simulation results of the Liuxihe model with a traditional hydrological model (Xinanjiang model). The results show that the Liuxihe model provides excellent simulation of field floods in Caojiang river basin. The average Nash–Sutcliffe coefficient is 0.73, the average correlation coefficient is 0.9, the average flood peak present error is 0.33, and the average peak simulation accuracy is 93.9%. Compared with the traditional flood hydrological model, the Liuxihe model simulates floods better with less measured hydrological information. In addition, we found that the particle swarm optimization (PSO) algorithm can improve the simulation of the model, and its practical application only needs one representative flood for parameter optimization, which is suitable for areas with little hydrological information. The study can support flood forecasting in the Caojiang river basin and provide a reference for the preparation of flood forecasting schemes in other small and medium-sized watersheds.
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Arduino, G., P. Reggiani, and E. Todini. "Recent advances in flood forecasting and flood risk assessment." Hydrology and Earth System Sciences 9, no. 4 (October 7, 2005): 280–84. http://dx.doi.org/10.5194/hess-9-280-2005.
Повний текст джерелаАнотація:
Abstract. Recent large floods in Europe have led to increased interest in research and development of flood forecasting systems. Some of these events have been provoked by some of the wettest rainfall periods on record which has led to speculation that such extremes are attributable in some measure to anthropogenic global warming and represent the beginning of a period of higher flood frequency. Whilst current trends in extreme event statistics will be difficult to discern, conclusively, there has been a substantial increase in the frequency of high floods in the 20th century for basins greater than 2x105 km2. There is also increasing that anthropogenic forcing of climate change may lead to an increased probability of extreme precipitation and, hence, of flooding. There is, therefore, major emphasis on the improvement of operational flood forecasting systems in Europe, with significant European Community spending on research and development on prototype forecasting systems and flood risk management projects. This Special Issue synthesises the most relevant scientific and technological results presented at the International Conference on Flood Forecasting in Europe held in Rotterdam from 3-5 March 2003. During that meeting 150 scientists, forecasters and stakeholders from four continents assembled to present their work and current operational best practice and to discuss future directions of scientific and technological efforts in flood prediction and prevention. The papers presented at the conference fall into seven themes, as follows.
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Mistry, Shivangi, and Falguni Parekh. "Flood Forecasting Using Artificial Neural Network." IOP Conference Series: Earth and Environmental Science 1086, no. 1 (September 1, 2022): 012036. http://dx.doi.org/10.1088/1755-1315/1086/1/012036.
Повний текст джерелаАнотація:
Abstract The process of assessing the timing, amount, and period of flood events based on observed features of a river basin is known as flood forecasting. Floods cause lots of damage to properties and create a risk to human life. Flood forecasting is critical for developing appropriate flood risk management strategies, reducing flood hazards, evacuating people from flood-prone areas. The main objective of this study is to apply artificial neural networks for forecasting of river flow in the Deo River, located in Gujarat. Rainfall and discharge are the parameters considered for model development. The developed model is validated to test the accuracy of the model. Trained and validated models are evaluated using performance indices. Six alternative flood prediction models have been developed using ANN. These models are developed based on various training algorithms. A single layer feed forward back-propagation neural network with six different training algorithms (Scaled conjugate gradient, Levenberg Marquardt, Resilient back-propagation, Conjugate gradient, and Cascade forward back propagation, Bayesian regularization) was developed, with 70% of the data used for training and 30% for validation. The created models’ performance is assessed using statistical performance parameters. The best performance was obtained with an ANN model developed using the Cascade forward back-propagation training algorithm, which had a coefficient of correlation (r) of 0.83, a coefficient of determination (R2) of 0.70, and a root mean squared error (RMSE) of 5.58 for training and a coefficient of correlation (r) of 0.89, a coefficient of determination (R2) of 0.70, and a root mean squared error (RMSE) of 7.27 for validation. The forecast inflow is very close to the observed values. This study shows that ANN can be used to successfully predict floods, and the model developed can be used by flood control departments across the country for flood forecasting.
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Puttinaovarat, Supattra, and Paramate Horkaew. "Application Programming Interface for Flood Forecasting from Geospatial Big Data and Crowdsourcing Data." International Journal of Interactive Mobile Technologies (iJIM) 13, no. 11 (November 15, 2019): 137. http://dx.doi.org/10.3991/ijim.v13i11.11237.
Повний текст джерелаАнотація:
Nowadays, natural disasters tend to increase and become more severe. They do affect life and belongings of great numbers of people. One kind of such disasters that hap-pen frequently almost every year is floods in all regions across the world. A prepara-tion measure to cope with upcoming floods is flood forecasting in each particular area in order to use acquired data for monitoring and warning to people and involved per-sons, resulting in the reduction of damage. With advanced computer technology and remote sensing technology, large amounts of applicable data from various sources are provided for flood forecasting. Current flood forecasting is done through computer processing by different techniques. The famous one is machine learning, of which the limitation is to acquire a large amount big data. The one currently used still requires manpower to download and record data, causing delays and failures in real-time flood forecasting. This research, therefore, proposed the development of an automatic big data downloading system from various sources through the development of applica-tion programming interface (API) for flood forecasting by machine learning. This research relied on 4 techniques, i.e., maximum likelihood classification (MLC), fuzzy logic, self-organization map (SOM), and artificial neural network with RBF Kernel. According to accuracy assessment of flood forecasting, the most accurate technique was MLC (99.2%), followed by fuzzy logic, SOM, and RBF (97.8%, 96.6%, and 83.3%), respectively.
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Pandey, Rajendra P., Meena Desai, and Rajnesh Panwar. "Hybrid deep learning model for flood frequency assessment and flood forecasting." Multidisciplinary Science Journal 5 (August 18, 2023): 2023ss0204. http://dx.doi.org/10.31893/multiscience.2023ss0204.
Повний текст джерелаАнотація:
The most common and persistent natural hazard to people across the globe is flooding. The frequency of floods in a given place is defined as the likelihood and intensity of floods occurring there within a certain period. Examining historical flood data and using techniques are often used to determine the likelihood that a flood of a certain size would occur in a specific location. The method of flood prediction involves making forecasts on the frequency and severity of flooding. It may be influenced by a number of factors, including the topography, river flow, soil moisture content, and the period of rainfall. In this research, we provide a novel Cat Swarm Optimized Spatial Adversarial Network (CSO-SAN) technique for predicting and assessing flood frequency. This technique simulates the yearly greatest flow at the river Mahanadi measurement sites at Andhiyarkore, Bamanidhi, Baronda, and Kurubhatta over 60 years. The CSO-SAN model is adapted for the flood forecasting component to predict the frequency and size of future floods. The model incorporates real-time data from various sources, such as meteorological predictions and information on river flow, to anticipate the probability and severity of upcoming floods. Compared to other conventional statistical techniques and forecasting models, the CSO-SAN model outperformed them in tests conducted on the Mahanadi river basins. The model offers a viable method for improving the precision of flood frequency evaluation and flood forecasting, with significant advantages for managing and reducing flood risk.
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Wu, Heng Qing, Qiang Huang, Wei Xu, and Shu Feng Xi. "Application of K-Means Cluster and Rough Set in Classified Real-Time Flood Forecasting." Advanced Materials Research 1092-1093 (March 2015): 734–41. http://dx.doi.org/10.4028/www.scientific.net/amr.1092-1093.734.
Повний текст джерелаАнотація:
A new classified real-time flood forecasting framework was presented. Firstly, the historical floods were classified by K-means cluster, according to the hydrological factors. Then rough set was used to extract operation rules for flood forecasting. Following, the conceptual hydrological model was constructed and Genetic Algorithm (GA) was used to calibrate the hydrological model parameters. In simulation, River A is taken as study example. The categories of parameters are selected in operation according to flood information and rules. The result is compared with traditional flood forecasting. It demonstrates the performance of classified framework is improved in terms of accuracy and reliability.
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Zhang, Yue, Juanhui Ren, Rui Wang, Feiteng Fang, and Wen Zheng. "Multi-Step Sequence Flood Forecasting Based on MSBP Model." Water 13, no. 15 (July 30, 2021): 2095. http://dx.doi.org/10.3390/w13152095.
Повний текст джерелаАнотація:
Establishing a model predicting river flow can effectively reduce huge losses caused by floods. This paper proposes a multi-step time series forecasting model based on multiple input and multiple output strategies, and this model is applied to the flood forecasting process of a river basin in Shanxi, which effectively improves the engineering application value of the flood forecasting model based on deep learning. The experimental results show that after considering the seasonal characteristics of the river channel and screening the influencing factors, a simple neural network model can accurately predict the peak value, the peak time and flood trends. On this basis, we proposed the MSBP (Multi-step Back Propagation) model, which can accurately predict the flow trend of the river basin 20 h in advance, and the NSE (Nash Efficiency) is 0.89. The MSBP model can improve the reliability of flood forecasting and increase the internal interpretability of the model, which is of great significance for effectively improving the effect of flood forecasting.
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Chitwatkulsiri, Detchphol, and Hitoshi Miyamoto. "Real-Time Urban Flood Forecasting Systems for Southeast Asia—A Review of Present Modelling and Its Future Prospects." Water 15, no. 1 (January 1, 2023): 178. http://dx.doi.org/10.3390/w15010178.
Повний текст джерелаАнотація:
Many urban areas in tropical Southeast Asia, e.g., Bangkok in Thailand, have recently been experiencing unprecedentedly intense flash floods due to climate change. The rapid flood inundation has caused extremely severe damage to urban residents and social infrastructures. In addition, urban Southeast Asia usually has inadequate capacities in drainage systems, complicated land use patterns, and a large vulnerable population in limited urban areas. To reduce the urban flood risk and enhance the resilience of vulnerable urban communities, it has been of essential importance to develop real-time urban flood forecasting systems for flood disaster prevention authorities and the urban public. This paper reviewed the state-of-the-art models of real-time forecasting systems for urban flash floods. The real-time system basically consists of the following subsystems, i.e., rainfall forecasting, drainage system modelling, and inundation area mapping. This paper summarized the recent radar data utilization methods for rainfall forecasting, physical-process-based hydraulic models for flood inundation prediction, and data-driven artificial intelligence (AI) models for the real-time forecasting system. This paper also dealt with available technologies for modelling, e.g., digital surface models (DSMs) for the finer urban terrain of drainage systems. The review indicated that an obstacle to using process-based hydraulic models was the limited computational resources and shorter lead time for real-time forecasting in many urban areas in tropical Southeast Asia. The review further discussed the prospects of data-driven AI models for real-time forecasting systems.
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Xu, Yiyuan, Jianhui Zhao, Biao Wan, Jinhua Cai, and Jun Wan. "Flood Forecasting Method and Application Based on Informer Model." Water 16, no. 5 (March 4, 2024): 765. http://dx.doi.org/10.3390/w16050765.
Повний текст джерелаАнотація:
Flood forecasting helps anticipate floods and evacuate people, but due to the access of a large number of data acquisition devices, the explosive growth of multidimensional data and the increasingly demanding prediction accuracy, classical parameter models, and traditional machine learning algorithms are unable to meet the high efficiency and high precision requirements of prediction tasks. In recent years, deep learning algorithms represented by convolutional neural networks, recurrent neural networks and Informer models have achieved fruitful results in time series prediction tasks. The Informer model is used to predict the flood flow of the reservoir. At the same time, the prediction results are compared with the prediction results of the traditional method and the LSTM model, and how to apply the Informer model in the field of flood prediction to improve the accuracy of flood prediction is studied. The data of 28 floods in the Wan’an Reservoir control basin from May 2014 to June 2020 were used, with areal rainfall in five subzones and outflow from two reservoirs as inputs and flood processes with different sequence lengths as outputs. The results show that the Informer model has good accuracy and applicability in flood forecasting. In the flood forecasting with a sequence length of 4, 5 and 6, Informer has higher prediction accuracy, and the prediction accuracy is better than other models under the same sequence length, but the prediction accuracy will decline to a certain extent with the increase in sequence length. The Informer model stably predicts the flood peak better, and its average flood peak difference and average maximum flood peak difference are the smallest. As the length of the sequence increases, the number of fields with a maximum flood peak difference less than 15% increases, and the maximum flood peak difference decreases. Therefore, the Informer model can be used as one of the better flood forecasting methods, and it provides a new forecasting method and scientific decision-making basis for reservoir flood control.
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Cluckie, I. D., and D. Han. "Fluvial Flood Forecasting." Water and Environment Journal 14, no. 4 (August 2000): 270–76. http://dx.doi.org/10.1111/j.1747-6593.2000.tb00260.x.
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Tsegaw, Aynalem Tassachew, Thomas Skaugen, Knut Alfredsen, and Tone M. Muthanna. "A dynamic river network method for the prediction of floods using a parsimonious rainfall-runoff model." Hydrology Research 51, no. 2 (August 26, 2019): 146–68. http://dx.doi.org/10.2166/nh.2019.003.
Повний текст джерелаАнотація:
Abstract Floods are one of the major climate-related hazards and cause casualties and substantial damage. Accurate and timely flood forecasting and design flood estimation are important to protect lives and property. The Distance Distribution Dynamic (DDD) is a parsimonious rainfall-runoff model which is being used for flood forecasting at the Norwegian flood forecasting service. The model, like many other models, underestimates floods in many cases. To improve the flood peak prediction, we propose a dynamic river network method into the model. The method is applied for 15 catchments in Norway and tested on 91 flood peaks. The performance of DDD in terms of KGE and BIAS is identical with and without dynamic river network, but the relative error (RE) and mean absolute relative error (MARE) of the simulated flood peaks are improved significantly with the method. The 0.75 and 0.25 quantiles of the RE are reduced from 41% to 23% and from 22% to 1%, respectively. The MARE is reduced from 32.9% to 15.7%. The study results also show that the critical support area is smaller in steep and bare mountain catchments than flat and forested catchments.
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Chen, Y., J. Li, S. Huang, and Y. Dong. "Study of Beijiang catchment flash-flood forecasting model." Proceedings of the International Association of Hydrological Sciences 368 (May 6, 2015): 150–55. http://dx.doi.org/10.5194/piahs-368-150-2015.
Повний текст джерелаАнотація:
Abstract. Beijiang catchment is a small catchment in southern China locating in the centre of the storm areas of the Pearl River Basin. Flash flooding in Beijiang catchment is a frequently observed disaster that caused direct damages to human beings and their properties. Flood forecasting is the most effective method for mitigating flash floods, the goal of this paper is to develop the flash flood forecasting model for Beijiang catchment. The catchment property data, including DEM, land cover types and soil types, which will be used for model construction and parameter determination, are downloaded from the website freely. Based on the Liuxihe Model, a physically based distributed hydrological model, a model for flash flood forecasting of Beijiang catchment is set up. The model derives the model parameters from the terrain properties, and further optimized with the observed flooding process, which improves the model performance. The model is validated with a few observed floods occurred in recent years, and the results show that the model is reliable and is promising for flash flood forecasting.
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Riedel, Lukas, Thomas Röösli, Thomas Vogt, and David N. Bresch. "Fluvial flood inundation and socio-economic impact model based on open data." Geoscientific Model Development 17, no. 13 (July 10, 2024): 5291–308. http://dx.doi.org/10.5194/gmd-17-5291-2024.
Повний текст джерелаАнотація:
Abstract. Fluvial floods are destructive hazards that affect millions of people worldwide each year. Forecasting flood events and their potential impacts therefore is crucial for disaster preparation and mitigation. Modeling flood inundation based on extreme value analysis of river discharges is an alternative to physical models of flood dynamics, which are computationally expensive. We present the implementation of a globally applicable, open-source fluvial flood model within a state-of-the-art risk modeling framework. It uses openly available data to rapidly compute flood inundation footprints of historic and forecasted events for the estimation of associated impacts. For the example of Pakistan, we use this flood model to compute flood depths and extents and employ it to estimate population displacement due to floods. Comparing flood extents to satellite data reveals that incorporating estimated flood protection standards does not necessarily improve the flood footprint computed by the model. We further show that, after calibrating the vulnerability of the impact model to a single event, the estimated displacement caused by past floods is in good agreement with disaster reports. Finally, we demonstrate that this calibrated model is suited for probabilistic impact-based forecasting.
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Zhang, Yue, Daiwei Pan, Jesse Van Griensven, Simon X. Yang, and Bahram Gharabaghi. "Intelligent flood forecasting and warning: a survey." Intelligence & Robotics 3, no. 2 (June 28, 2023): 190–212. http://dx.doi.org/10.20517/ir.2023.12.
Повний текст джерелаАнотація:
Accurately predicting the magnitude and timing of floods is an extremely challenging problem for watershed management, as it aims to provide early warning and save lives. Artificial intelligence for forecasting has become an emerging research field over the past two decades, as computer technology and related areas have been developed in depth. In this paper, three typical machine learning algorithms for flood forecasting are reviewed: supervised learning, unsupervised learning, and semi-supervised learning. Special attention is given to deep learning approaches due to their better performance in various prediction tasks. Deep learning networks can represent flood behavior as powerful and beneficial tools. In addition, a detailed comparison and analysis of the multidimensional performance of different prediction models for flood prediction are presented. Deep learning has extensively promoted the development of real-time accurate flood forecasting techniques for early warning systems. Furthermore, the paper discusses the current challenges and future prospects for intelligent flood forecasting.
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Zhan, Xiaoyan, Hui Qin, Yongqi Liu, Liqiang Yao, Wei Xie, Guanjun Liu, and Jianzhong Zhou. "Variational Bayesian Neural Network for Ensemble Flood Forecasting." Water 12, no. 10 (September 30, 2020): 2740. http://dx.doi.org/10.3390/w12102740.
Повний текст джерелаАнотація:
Disastrous floods are destructive and likely to cause widespread economic losses. An understanding of flood forecasting and its potential forecast uncertainty is essential for water resource managers. Reliable forecasting may provide future streamflow information to assist in an assessment of the benefits of reservoirs and the risk of flood disasters. However, deterministic forecasting models are not able to provide forecast uncertainty information. To quantify the forecast uncertainty, a variational Bayesian neural network (VBNN) model for ensemble flood forecasting is proposed in this study. In VBNN, the posterior distribution is approximated by the variational distribution, which can avoid the heavy computational costs in the traditional Bayesian neural network. To transform the model parameters’ uncertainty into the model output uncertainty, a Monte Carlo sample is applied to give ensemble forecast results. The proposed method is verified by a flood forecasting case study on the upper Yangtze River. A point forecasting model neural network and two probabilistic forecasting models, including hidden Markov Model and Gaussian process regression, are also applied to compare with the proposed model. The experimental results show that the VBNN performs better than other comparable models in terms of both accuracy and reliability. Finally, the result of uncertainty estimation shows that the VBNN can effectively handle heteroscedastic flood streamflow data.
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Pham Thanh Long, Nguyen Thao Hien, Nguyen Thu Huong, and Le Thanh Trang. "BUILDING INTEGRATED TOOLS FOR FLOOD WARNING AND INUNDATION FORECAST OF RIVER BASINS IN KHANH HOA PROVINCE." Tạp chí Khoa học Biến đổi khí hậu, no. 27 (September 30, 2023): 65–74. http://dx.doi.org/10.55659/2525-2496/27.85972.
Повний текст джерелаАнотація:
In order to strengthen the flood warning and inundation forecasting system in the South Central region, the authors aim to study an integrated tool for flood disaster prevention, pilot application in two main river basins in Khanh Hoa Province. This area often suffers great damage due to the influence of storms and tropical depressions, which can be mentioned as floods in 1980, 1986, 1993, 1998, 1999, 2003, 2009, 2013, and 2016. The tool is the connection of scientific and technical products in automatic monitoring equipment and transmits real-time rainfall, water level data; as input for forecasting and warning models of flood and inundation risks; provides forecast data to users through the WebGIS online platform. The article details how to build an integrated tool for flood warning, inundation forecasting and verifying in a historical flood in 2016. The study has great significance in improving the effectiveness of natural disaster prevention, storm and flood monitoring; optimal and timely support in case of emergency. At the same time, it contributes to the forecasting of Viet Nam's Hydrometeorological Sector.
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Yao, Yi, Zhongmin Liang, Weimin Zhao, Xiaolei Jiang, and Binquan Li. "Performance assessment of hydrologic uncertainty processor through integration of the principal components analysis." Journal of Water and Climate Change 10, no. 2 (December 11, 2017): 373–90. http://dx.doi.org/10.2166/wcc.2017.137.
Повний текст джерелаАнотація:
Abstract Uncertainty analysis is important and should be always considered when using models for flood forecasting. In this paper, the ‘Principal Components Analysis-Hydrologic Uncertainty Processor’ (PCA-HUP) was developed for probabilistic flood forecasting (PFF) and further evaluated in the middle Yellow River, China. Due to the severe sediment erosion, small and medium floods drain in the main channel (normal floods) while large floods would spill over the bank and drain in river floodplains (overbank floods). Thus, the practical routing methods were used to provide the deterministic flood forecasting (DFF) input for PCA-HUP. PCA-HUP quantifies the forecast uncertainty and provides PFF results. The comparison of performance between the DFF and PFF outputs indicated that PFF could also provide a good accuracy of deterministic hydrograph. In order to explore the performance decay of DFF and PFF with lead time increasing, the lead times n = 1, 6 and 10 hours were chosen for comparison. Results suggested that, with the increasing lead time, the performances of both DFF and PFF decayed accordingly. As a consequence, this study proved the practicability of PCA-HUP in the operational forecasting for both normal and overbank floods in the middle reach of Yellow River.
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Ushiyama, Tomoki, Takahiro Sayama, and Yoichi Iwami. "Ensemble Flood Forecasting of Typhoons Talas and Roke at Hiyoshi Dam Basin." Journal of Disaster Research 11, no. 6 (December 1, 2016): 1032–39. http://dx.doi.org/10.20965/jdr.2016.p1032.
Повний текст джерелаАнотація:
In order to be able to issue flood warnings not hours but days in advance, numerical weather prediction (NWP) is essential to the forecasting of flood-producing rainfall. The regional ensemble prediction system (EPS), advanced NWP on a local scale, has a high potential to improve flood forecasting through the quantitative prediction of precipitation. In this study, the predictability of floods using the ensemble flood forecasting system, which is composed of regional EPS and a distributed hydrological model, was investigated. Two flood events which took place in a small basin in Japan in 2010 and which were caused by typhoons Talas and Roke were examined. As the forecasting system predicted the probability of flood occurrence at least 24 h beforehand in the case of both typhoons, these forecasts were better than deterministic forecasts. However, the system underestimated the peak of the flooding in the typhoon Roke event, and it was too early in its prediction of the appearance of the peak of the flooding in the Talas event. Although the system has its limitations, it has proved to have the potential to produce early flood warnings.
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Shinde Sanket and Vaibhav Shelar. "Behavior of Flood Resistant Building and Ductile Detailing of G +7 RC Building Using IS 13920-2016." World Journal of Advanced Engineering Technology and Sciences 9, no. 1 (June 30, 2023): 182–92. http://dx.doi.org/10.30574/wjaets.2023.9.1.0158.
Повний текст джерелаАнотація:
Floods are one of the most widespread and destructive natural disasters occurring in the world and with the increase in constructions along river courses and concentration of population around floodplain areas, flood-induced damages have been continuously increasing. The annual disaster record reveals that flood occurrence increased about ten folds over the past five decades. Thus, floods are posing a great threat and challenge to planers, design engineers, insurance industries, policymakers, and to the governments. Structural and non-structural measures can be used to deal with floods. Structural measures include a set of works aiming to reduce one or more hydraulic parameters like runoff volume, peak discharge, rise in water level, duration of flood, flow velocity, etc. Non-structural measures involve a wide range of measures to reduce flood risk through flood forecasting and early warning systems, emergency plans, and posing land use regulations and policies. The futuristic reinforced concrete buildings can be considered as a symbol of modern civilization. These buildings are usually constructed based on the guide lines given by the standard code books(like IS: 456:2000 and IS 13920:2016).Unfortunately, the code provisions consider the seismic loads and wind effects alone, while accounting the dead and live design loads, and exclude the flood loads. This implies the necessity to bring out corrective measures that can be adopted to reduce vulnerability before harm occurrences. In this project focuses on both the incorporation of flood loads during the analysis and design in CSI-ETABS software and the assessment of flood vulnerability of reinforced concrete residential buildings. Vulnerability is expressed as a fraction of ground floor height and maximum flood level at most immerse the building up to ground floor and first floor level. The importance of the outcome arises from the need of a strengthening solution to avoid failure of new or existing structures during floods.
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Song, Tianyu, Wei Ding, Jian Wu, Haixing Liu, Huicheng Zhou, and Jinggang Chu. "Flash Flood Forecasting Based on Long Short-Term Memory Networks." Water 12, no. 1 (December 29, 2019): 109. http://dx.doi.org/10.3390/w12010109.
Повний текст джерелаАнотація:
Flash floods occur frequently and distribute widely in mountainous areas because of complex geographic and geomorphic conditions and various climate types. Effective flash flood forecasting with useful lead times remains a challenge due to its high burstiness and short response time. Recently, machine learning has led to substantial changes across many areas of study. In hydrology, the advent of novel machine learning methods has started to encourage novel applications or substantially improve old ones. This study aims to establish a discharge forecasting model based on Long Short-Term Memory (LSTM) networks for flash flood forecasting in mountainous catchments. The proposed LSTM flood forecasting (LSTM-FF) model is composed of T multivariate single-step LSTM networks and takes spatial and temporal dynamics information of observed and forecast rainfall and early discharge as inputs. The case study in Anhe revealed that the proposed models can effectively predict flash floods, especially the qualified rates (the ratio of the number of qualified events to the total number of flood events) of large flood events are above 94.7% at 1–5 h lead time and range from 84.2% to 89.5% at 6–10 h lead-time. For the large flood simulation, the small flood events can help the LSTM-FF model to explore a better rainfall-runoff relationship. The impact analysis of weights in the LSTM network structures shows that the discharge input plays a more obvious role in the 1-h LSTM network and the effect decreases with the lead-time. Meanwhile, in the adjacent lead-time, the LSTM networks explored a similar relationship between input and output. The study provides a new approach for flash flood forecasting and the highly accurate forecast contributes to prepare for and mitigate disasters.
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Nguyen, Dinh Ty, and Shien-Tsung Chen. "Real-Time Probabilistic Flood Forecasting Using Multiple Machine Learning Methods." Water 12, no. 3 (March 12, 2020): 787. http://dx.doi.org/10.3390/w12030787.
Повний текст джерелаАнотація:
Probabilistic flood forecasting, which provides uncertain information in the forecasting of floods, is practical and informative for implementing flood-mitigation countermeasures. This study adopted various machine learning methods, including support vector regression (SVR), a fuzzy inference model (FIM), and the k-nearest neighbors (k-NN) method, to establish a probabilistic forecasting model. The probabilistic forecasting method is a combination of a deterministic forecast produced using SVR and a probability distribution of forecast errors determined by the FIM and k-NN method. This study proposed an FIM with a modified defuzzification scheme to transform the FIM’s output into a probability distribution, and k-NN was employed to refine the probability distribution. The probabilistic forecasting model was applied to forecast flash floods with lead times of 1–3 hours in Yilan River, Taiwan. Validation results revealed the deterministic forecasting to be accurate, and the probabilistic forecasting was promising in view of a forecasted hydrograph and quantitative assessment concerning the confidence level.
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Chang, Fi-John, Yen-Chang Chen, and Jin-Ming Liang. "Fuzzy Clustering Neural Network as Flood Forecasting Model." Hydrology Research 33, no. 4 (August 1, 2002): 275–90. http://dx.doi.org/10.2166/nh.2002.0008.
Повний текст джерелаАнотація:
Flood forecasting is always a challenge in Taiwan, which has a subtropical climate and high mountains. This paper develops a fuzzy clustering neural network (FCNN), and implements this novel structure and reasoning process for flood forecasting. The FCNN has a hybrid learning scheme; the unsupervised learning scheme employs fuzzy min-max clustering to extract information from the input data. The supervised learning scheme uses linear regression to determine the weights of FCNN. The network, which learns from examples, is a hydrological processes theory-free estimator. Most of the parameters, weights of the network, are adjusted automatically during the network training. Only one parameter needs to be justified during constructing the flood forecasting models. The one-hour-ahead floods of the Lanyoung River during tropical storms are forecasted by the constructed models. Our results show that the simple but reliable model is capable of real time flood forecasting.
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Stanley, S. J., and R. Gerard. "Ice jam flood forecasting: Hay River, N.W.T." Canadian Journal of Civil Engineering 19, no. 2 (April 1, 1992): 212–23. http://dx.doi.org/10.1139/l92-027.
Повний текст джерелаАнотація:
Much of the town of Hay River, N.W.T., is located on the low-lying land of the Hay River delta, and is subject to severe ice jam floods every decade or so. As a first line of defence against these floods, it was proposed that an ice jam flood forecast procedure be developed. The major components of the study included a review of historical flood data, resident interviews, field surveys, and observations of the delta ice regime. It was found that a 1–2 day forecast of discharge in Hay River can be directly determined from discharges measured at a Water Survey of Canada gauging station upstream. From this and from an understanding of the breakup ice regime developed from the study as well as the water level–discharge relations determined for ice jams at three locations in the delta, it was possible to develop a first-generation ice jam flood forecasting procedure that gave a 1–2 day warning of high water at each of the three locations. The procedure was evaluated against the breakup events of 1988 and 1989 with reasonable success. The development and application of this procedure is described in the paper. Key words: rivers, floods, ice jams, forecasting.
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Zhang, Yue, Zhaohui Gu, Jesse Van Griensven Thé, Simon X. Yang, and Bahram Gharabaghi. "The Discharge Forecasting of Multiple Monitoring Station for Humber River by Hybrid LSTM Models." Water 14, no. 11 (June 2, 2022): 1794. http://dx.doi.org/10.3390/w14111794.
Повний текст джерелаАнотація:
An early warning flood forecasting system that uses machine-learning models can be utilized for saving lives from floods, which are now exacerbated due to climate change. Flood forecasting is carried out by determining the river discharge and water level using hydrologic models at the target sites. If the water level and discharge are forecasted to reach dangerous levels, the flood forecasting system sends warning messages to residents in flood-prone areas. In the past, hybrid Long Short-Term Memory (LSTM) models have been successfully used for the time series forecasting. However, the prediction errors grow exponentially with the forecasting period, making the forecast unreliable as an early warning tool with enough lead time. Therefore, this research aimed to improve the accuracy of flood forecasting models by employing real-time monitoring network datasets and establishing temporal and spatial links between adjacent monitoring stations. We evaluated the performance of the Long Short-Term Memory (LSTM), the Convolutional Neural Networks LSTM (CNN-LSTM), the Convolutional LSTM (ConvLSTM), and the Spatio-Temporal Attention LSTM (STA-LSTM) models for flood forecasting. The dataset, employed for validation, includes hourly discharge records, from 2012 to 2017, on six stations of the Humber River in the City of Toronto, Canada. Experiments included forecasting for both 6 and 12 h ahead, using discharge data as input for the past 24 h. The STA-LSTM model’s performance was superior to the CNN-LSTM, the ConvLSTM, and the basic LSTM models when the forecast time was longer than 6 h.
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Lee, Jung Hwan, Gi Moon Yuk, Hyeon Tae Moon, and Young-Il Moon. "Integrated Flood Forecasting and Warning System against Flash Rainfall in the Small-Scaled Urban Stream." Atmosphere 11, no. 9 (September 11, 2020): 971. http://dx.doi.org/10.3390/atmos11090971.
Повний текст джерелаАнотація:
The flood forecasting and warning system enable an advanced warning of flash floods and inundation depths for disseminating alarms in urban areas. Therefore, in this study, we developed an integrated flood forecasting and warning system combined inland-river that systematized technology to quantify flood risk and flood forecasting in urban areas. LSTM was used to predict the stream depth in the short-term inundation prediction. Moreover, rainfall prediction by radar data, a rainfall-runoff model combined inland-river by coupled SWMM and HEC-RAS, automatic simplification module of drainage networks, automatic calibration module of SWMM parameter by Dynamically Dimensioned Search (DDS) algorithm, and 2-dimension inundation database were used in very short-term inundation prediction to warn and convey the flood-related data and information to communities. The proposed system presented better forecasting results compared to the Seoul integrated disaster prevention system. It can provide an accurate water level for 30 min to 90 min lead times in the short-term inundation prediction module. And the very short-term inundation prediction module can provide water level across a stream for 10 min to 60 min lead times using forecasting rainfall by radar as well as inundation risk areas. In conclusion, the proposed modules were expected to be useful to support inundation forecasting and warning systems.
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Perumal, Muthiah, Tommaso Moramarco, Silvia Barbetta, Florisa Melone, and Bhabagrahi Sahoo. "Real-time flood stage forecasting by Variable Parameter Muskingum Stage hydrograph routing method." Hydrology Research 42, no. 2-3 (April 1, 2011): 150–61. http://dx.doi.org/10.2166/nh.2011.063.
Повний текст джерелаАнотація:
The application of a Variable Parameter Muskingum Stage (VPMS) hydrograph routing method for real-time flood forecasting at a river gauging site is demonstrated in this study. The forecast error is estimated using a two-parameter linear autoregressive model with its parameters updated at every routing time interval of 30 minutes at which the stage observations are made. This hydrometric data-based forecast model is applied for forecasting floods at the downstream end of a 15 km reach of the Tiber River in Central Italy. The study reveals that the proposed approach is able to provide reliable forecast of flood estimate for different lead times subject to a maximum lead time nearly equal to the travel time of the flood wave within the selected routing reach. Moreover, a comparative study of the VPMS method for real-time forecasting and the simple stage forecasting model (STAFOM), currently in operation as the Flood Forecasting and Warning System in the Upper-Middle Tiber River basin of Italy, demonstrates the capability of the VPMS model for its field use.
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Kumar, Sanjeet, Madhusudhan M. Reddy, Meena Isukapatla, and Mara Suneel Kumar Reddy. "Flood frequency and flood forecasting analysis of Krishna basin Andhra Pradesh." Disaster Advances 16, no. 11 (October 15, 2023): 27–39. http://dx.doi.org/10.25303/1611da027039.
Повний текст джерелаАнотація:
Floods are occurrences of natural hazards, frequent during the year in many rivers across the globe. Every year, several rivers in India are vulnerable to flooding, causing loss of property and life. Krishna is one of the major rivers in India which is vulnerable to flooding in every monsoon. In this study, flood analysis was conducted for the year 2019. Rainfall data from AWS was used to estimate discharge levels in dams during the monsoon of 2019. It was observed that moderate rainfall occurred in the months of August and September, corresponding to low rainfall and that extreme flooding occurred in the same month. Compared to the flood inundation map of the satellite, it shows a close relationship with the flood map of 2019 and the affected area. The study shows that proper analysis of rainfall will be helpful in predicting downstream floods. To evaluate the flood control situation with appropriate data management in the Krishna basin, the usage of flood water is strong. Such types of studies would help to provide reliable and prompt flood forecasts and advance warning to redirect the main river flow to small canals, which will help to mitigate, excavate and remediate flooding in any area.
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Thiemig, V., B. Bisselink, F. Pappenberger, and J. Thielen. "A pan-African medium-range ensemble flood forecast system." Hydrology and Earth System Sciences 19, no. 8 (August 3, 2015): 3365–85. http://dx.doi.org/10.5194/hess-19-3365-2015.
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Abstract. The African Flood Forecasting System (AFFS) is a probabilistic flood forecast system for medium- to large-scale African river basins, with lead times of up to 15 days. The key components are the hydrological model LISFLOOD, the African GIS database, the meteorological ensemble predictions by the ECMWF (European Centre for Medium-Ranged Weather Forecasts) and critical hydrological thresholds. In this paper, the predictive capability is investigated in a hindcast mode, by reproducing hydrological predictions for the year 2003 when important floods were observed. Results were verified by ground measurements of 36 sub-catchments as well as by reports of various flood archives. Results showed that AFFS detected around 70 % of the reported flood events correctly. In particular, the system showed good performance in predicting riverine flood events of long duration (> 1 week) and large affected areas (> 10 000 km2) well in advance, whereas AFFS showed limitations for small-scale and short duration flood events. The case study for the flood event in March 2003 in the Sabi Basin (Zimbabwe) illustrated the good performance of AFFS in forecasting timing and severity of the floods, gave an example of the clear and concise output products, and showed that the system is capable of producing flood warnings even in ungauged river basins. Hence, from a technical perspective, AFFS shows a large potential as an operational pan-African flood forecasting system, although issues related to the practical implication will still need to be investigated.
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Gong, Junchao, Youbing Hu, Cheng Yao, Yanan Ma, Mingkun Sun, Junfu Gong, Zhuo Shi, and Jingbing Li. "The WRF-Driven Grid-Xin’anjiang Model and Its Application in Small and Medium Catchments of China." Water 16, no. 1 (December 27, 2023): 103. http://dx.doi.org/10.3390/w16010103.
Повний текст джерелаАнотація:
The distributed Grid-Xin’anjiang (Grid-XAJ) model is very sensitive to the spatial and temporal distribution of data when used in humid and semi-humid small and medium catchments. We used the successive correction method to merge the gauged rainfall with rainfall forecasted by the Weather Research and Forecasting (WRF) model to enhance the spatiotemporal accuracy of rainfall distribution. And we used the Penman–Monteith equation to calculate the potential evapotranspiration (PEPM). Then, we designed two forcing scenarios (WRF-driven rainfall (Wr) + PEPM, WRF-merged rainfall (Wm) + PEPM) to drive the Grid-XAJ model for flood forecasting. We found the WRF-driven Grid-XAJ model held significant potential in flood forecasting. The Grid-XAJ model provided only an approximation of flood hygrographs when driven by scenario Wr + PEPM. The results in scenario Wm + PEPM showed a high degree-of-fit with observed floods with mean Nash–Sutcliffe efficiency coefficient (NSE) values of 0.94 and 0.68 in two catchments. Additionally, scenario Wm + PEPM performed better flood hygrographs than scenario Wr + PEPM. The flood volumes and flow peaks in scenario Wm + PEPM had an obvious improvement compare to scenario Wr + PEPM. Finally, we observed that the model exhibited superior performance in forecasting flood hydrographs, flow peaks, and flood volumes in humid catchments compared with semi-humid catchments.
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El Khalki, El Mahdi, Yves Tramblay, Arnau Amengual, Victor Homar, Romualdo Romero, Mohamed El Mehdi Saidi, and Meriem Alaouri. "Validation of the AROME, ALADIN and WRF Meteorological Models for Flood Forecasting in Morocco." Water 12, no. 2 (February 6, 2020): 437. http://dx.doi.org/10.3390/w12020437.
Повний текст джерелаАнотація:
Flash floods are common in small Mediterranean watersheds and the alerts provided by real-time monitoring systems provide too short anticipation times to warn the population. In this context, there is a strong need to develop flood forecasting systems in particular for developing countries such as Morocco where floods have severe socio-economic impacts. In this study, the AROME (Application of Research to Operations at Mesoscale), ALADIN (Aire Limited Dynamic Adaptation International Development) and WRF (Weather Research and Forecasting) meteorological models are evaluated to forecast flood events in the Rheraya and Ourika basin located in the High-Atlas Mountains of Morocco. The model evaluation is performed by comparing for a set of flood events the observed and simulated probabilities of exceedances for different precipitation thresholds. In addition, two different flood forecasting approaches are compared: the first one relies on the coupling of meteorological forecasts with a hydrological model and the second one is a based on a linear relationship between event rainfall, antecedent soil moisture and runoff. Three different soil moisture products (in-situ measurements, European Space Agency’s Climate Change Initiative ESA-CCI remote sensing data and ERA5 reanalysis) are compared to estimate the initial soil moisture conditions before flood events for both methods. Results showed that the WRF and AROME models better simulate precipitation amounts compared to ALADIN, indicating the added value of convection-permitting models. The regression-based flood forecasting method outperforms the hydrological model-based approach, and the maximum discharge is better reproduced when using the WRF forecasts in combination with ERA5. These results provide insights to implement robust flood forecasting approaches in the context of data scarcity that could be valuable for developing countries such as Morocco and other North African countries.
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Chaudhari, Ronak P., Shantanu R. Thorat, Darshan J. Mehta, Sahita I. Waikhom, Vipinkumar G. Yadav, and Vijendra Kumar. "Comparison of soft-computing techniques: Data-driven models for flood forecasting." AIMS Environmental Science 11, no. 5 (2024): 741–58. http://dx.doi.org/10.3934/environsci.2024037.
Повний текст джерелаАнотація:
<p>Accurate flood forecasting is a crucial process for predicting the timing, occurrence, duration, and magnitude of floods in specific zones. This prediction often involves analyzing various hydrological, meteorological, and environmental parameters. In recent years, several soft computing techniques have been widely used for flood forecasting. In this study, flood forecasting for the Narmada River at the Hoshangabad gauging site in Madhya Pradesh, India, was conducted using an Artificial Neural Network (ANN) model, a Fuzzy Logic (FL) model, and an Adaptive Neuro-Fuzzy Inference System (ANFIS) model. To assess their capacity to handle different levels of information, three separate input data sets were used. Our objective was to compare the performance and evaluate the suitability of soft computing data-driven models for flood forecasting. For the development of these models, monthly discharge data spanning 33 years from six gauging sites were selected. Various performance measures, such as regression, root mean square error (RMSE), and percentage deviation, were used to compare and evaluate the performances of the different models. The results indicated that the ANN and ANFIS models performed similarly in some cases. However, the ANFIS model generally predicted much better than the ANN model in most cases. The ANFIS model, developed using the hybrid method, delivered the best performance with an RMSE of 211.97 and a coefficient of regression of 0.96, demonstrating the potential of using these models for flood forecasting. This research highlighted the effectiveness of soft computing techniques in flood forecasting and established useful suitability criteria that can be employed by flood control departments in various countries, regions, and states for accurate flood prognosis.</p>
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Lindenschmidt, Karl-Erich, Robert Briggs, Amir Ali Khan, and Thomas Puestow. "Elements and Processes Required for the Development of a Spring-Breakup Ice-Jam Flood Forecasting System (Churchill River, Atlantic Canada)." Water 16, no. 11 (May 29, 2024): 1557. http://dx.doi.org/10.3390/w16111557.
Повний текст джерелаАнотація:
Spring-breakup ice-jam floods are a major hazard for many rivers in cold regions. They can cause severe damage to infrastructure, property, and ecosystems along riverbanks. To reduce the risk and impact of these events, it is essential to develop reliable and timely forecasting systems that can provide early warning and guidance for mitigation actions. In this paper, we highlight the elements and processes required for the successful development of a spring-breakup ice-jam flood forecasting system, using the lower Churchill River in Labrador, Canada as a case study. We review the existing forecasting methodologies and systems for spring-breakup ice-jam floods and discuss their strengths and limitations. We then describe the case study of the lower Churchill River, where a large ice-jam flood occurred in May 2017, triggering an independent review and a series of recommendations for improving the flood preparedness and response. We present the main components and features of the forecasting system that was developed for the lower Churchill River, based on the recommendations from the independent review. We also discuss the improvements that were made to the forecasting system, such as parallelization, adaptation, and determination of ice-jam prone areas. Finally, we provide some conclusions and recommendations for future research and development of spring-breakup ice-jam flood forecasting systems, focusing on the requirements for a technical framework that incorporates community engagement and special considerations for regulated rivers.
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Liu, Li, Yue Ping Xu, Su Li Pan, and Zhi Xu Bai. "Potential application of hydrological ensemble prediction in forecasting floods and its components over the Yarlung Zangbo River basin, China." Hydrology and Earth System Sciences 23, no. 8 (August 14, 2019): 3335–52. http://dx.doi.org/10.5194/hess-23-3335-2019.
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Abstract. In recent year, floods becomes a serious issue in the Tibetan Plateau (TP) due to climate change. Many studies have shown that ensemble flood forecasting based on numerical weather predictions can provide an early warning with extended lead time. However, the role of hydrological ensemble prediction in forecasting flood volume and its components over the Yarlung Zangbo River (YZR) basin, China, has not been investigated. This study adopts the variable infiltration capacity (VIC) model to forecast the annual maximum floods and annual first floods in the YZR based on precipitation and the maximum and minimum temperature from the European Centre for Medium-Range Weather Forecasts (ECMWF). N simulations are proposed to account for parameter uncertainty in VIC. Results show that when trade-offs between multiple objectives are significant, N simulations are recommended for better simulation and forecasting. This is why better results are obtained for the Nugesha and Yangcun stations. Our ensemble flood forecasting system can skillfully predict the maximum floods with a lead time of more than 10 d and can predict about 7 d ahead for meltwater-related components. The accuracy of forecasts for the first floods is inferior, with a lead time of only 5 d. The base-flow components for the first floods are insensitive to lead time, except at the Nuxia station, whilst for the maximum floods an obvious deterioration in performance with lead time can be recognized. The meltwater-induced surface runoff is the most poorly captured component by the forecast system, and the well-predicted rainfall-related components are the major contributor to good performance. The performance in 7 d accumulated flood volumes is better than the peak flows.
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Zhu, Yanzheng, Yangbo Chen, Yanjun Zhao, Feng Zhou, and Shichao Xu. "Application and Research of Liuxihe Model in the Simulation of Inflow Flood at Zaoshi Reservoir." Sustainability 15, no. 13 (June 21, 2023): 9857. http://dx.doi.org/10.3390/su15139857.
Повний текст джерелаАнотація:
Floods occur frequently in China, and watershed floods are caused mainly by intensive rainfall, but the spatial distribution of this rainfall is often very uneven. Thus, a watershed hydrological model that enables a consideration of a heterogeneous spatial distribution of rainfall is needed. In this study, a flood forecasting scheme based on the Liuxihe model is established for the Zaoshi Reservoir. The particle swarm optimization (PSO) algorithm is used to optimize the model parameters for flood simulation, and the model’s performance is assessed by a comparison with measured flood data. The spatial distributions of rainfall selected for this study are non-uniform, with much greater rainfall in some areas than in others in some cases. Rainfall may be concentrated in the middle of the basin, in the reservoir area, or in the upstream portion of the basin. The Liuxihe-model-based flood inflow forecasting scheme for the Zaoshi Reservoir demonstrates an excellent simulation effect, with an average peak simulation accuracy of 96.3%, an average peak time of 1.042 h early, and an average Nash–Sutcliffe coefficient of 0.799. Under the condition of an uneven spatial distribution of rainfall, the Liuxihe model simulates floods well. The PSO algorithm significantly improves the model’s simulation accuracy, and its practical application requires only the selection of a typical flood for parameter optimization. Thus, the flood simulation effect of the Liuxihe model is ideal for the watershed above the Zaoshi Reservoir, and the scheme developed in this study can be applied for operational flood forecasting.
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Younis, J., S. Anquetin, and J. Thielen. "The benefit of high-resolution operational weather forecasts for flash flood warning." Hydrology and Earth System Sciences Discussions 5, no. 1 (February 12, 2008): 345–77. http://dx.doi.org/10.5194/hessd-5-345-2008.
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Abstract. In Mediterranean Europe, flash flooding is one of the most devastating hazards in terms of human life loss and infrastructures. Over the last two decades, flash floods brought losses of a billion Euros of damage in France alone. One of the problems of flash floods is that warning times are very short, leaving typically only a few hours for civil protection services to act. This study investigates if operationally available shortrange numerical weather forecasts together with a rainfall-runoff model can be used as early indication for the occurrence of flash floods. One of the challenges in flash flood forecasting is that the watersheds are typically small and good observational networks of both rainfall and discharge are rare. Therefore, hydrological models are difficult to calibrate and the simulated river discharges cannot always be compared with ground "truth". The lack of observations in most flash flood prone basins, therefore, lead to develop a method where the excess of the simulated discharge above a critical threshold can provide the forecaster with an indication of potential flood hazard in the area with leadtimes of the order of the weather forecasts. This study is focused on the Cévennes-Vivarais region in the Southeast of the Massif Central in France, a region known for devastating flash floods. The critical aspects of using numerical weather forecasting for flash flood forecasting are being described together with a threshold – exceedance. As case study the severe flash flood event which took place on 8–9 September 2002 has been chosen. The short-range weather forecasts, from the Lokalmodell of the German national weather service, are driving the LISFLOOD model, a hybrid between conceptual and physically based rainfall-runoff model. Results of the study indicate that high resolution operational weather forecasting combined with a rainfall-runoff model could be useful to determine flash floods more than 24 hours in advance.
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Alfieri, L., P. Burek, E. Dutra, B. Krzeminski, D. Muraro, J. Thielen, and F. Pappenberger. "GloFAS – global ensemble streamflow forecasting and flood early warning." Hydrology and Earth System Sciences Discussions 9, no. 11 (November 2, 2012): 12293–332. http://dx.doi.org/10.5194/hessd-9-12293-2012.
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Abstract. Anticipation and preparedness for large-scale flood events have a key role in mitigating their impact and optimizing the strategic planning of water resources. Although several developed countries have well-established systems for river monitoring and flood early warning, figures of population affected every year by floods in developing countries are unsettling. This paper presents the Global Flood Awareness System, which has been set up to provide an overview on upcoming floods in large world river basins. The Global Flood Awareness System is based on distributed hydrological simulation of numerical ensemble weather predictions with global coverage. Streamflow forecasts are compared statistically to climatological simulations to detect probabilistic exceedance of warning thresholds. In this article, the system setup is described, together with an evaluation of its performance over a two-year test period and a qualitative analysis of a case study for the Pakistan flood, in summer 2010. It is shown that hazardous events in large river basins can be skilfully detected with a forecast horizon of up to 1 month. In addition, results suggest that an accurate simulation of initial model conditions and an improved parameterization of the hydrological model are key components to reproduce accurately the streamflow variability in the many different runoff regimes of the Earth.
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Hajibabaei, Ehsan, and Alireza Ghasemi. "Flood Management, Flood Forecasting and Warning System." International Journal of Science and Engineering Applications 6, no. 2 (February 1, 2017): 33–38. http://dx.doi.org/10.7753/ijsea0602.1001.
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Blackburn, J., and F. E. Hicks. "Combined flood routing and flood level forecasting." Canadian Journal of Civil Engineering 29, no. 1 (February 1, 2002): 64–75. http://dx.doi.org/10.1139/l01-079.
Повний текст джерелаАнотація:
This paper presents a proposed modeling approach which uses unsteady flow hydraulic modeling for both flood routing and flood level determination. The onerous data requirements of hydraulic models in the flood routing application are overcome through the use of a "limited geometry" approach to describe channel geometry. In populated areas, where flood levels are needed, the model employs full cross section geometry. This hybrid model offers the advantage of operationally combining the flood routing and the determination of the flood level. In addition, the use of a hydraulic model opens up the potential for modeling more dynamic flood events such as ice jam release surges, which cannot be handled by traditional hydrological modeling approaches.Key words: flood routing, flood delineation, finite element modeling, Peace River.
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Otieno, O. M., H. S. Abdillahi, E. M. Wambui, and K. S. Kiprono. "FLOOD IMPACT-BASED FORECASTING FOR EARLY WARNING AND EARLY ACTION IN TANA RIVER BASIN, KENYA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W8 (August 22, 2019): 293–300. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w8-293-2019.
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<p><strong>Abstract.</strong> Kenya is mostly affected by floods during the March-April-May (MAM) and October-November-December (OND) rainfall. This often occurs along river basins such as the Tana river basin, leading to disruption of people’s livelihoods, loss of lives, infrastructure destruction and interruption of economic activities. This study used openly available data on flood exposure, vulnerability, lack of coping capacity, flood impacts and observed satellite rainfall to analyse and predict forecast-based impacts in Tana river. Earth observation satellites including LANDSAT, sentinel 1 and 2 were acquired based on credible flood event dates to validate flood exposure and flood events. The community risk assessment (CRA) approach was used to delineate communities at high risk of floods using combination of data on vulnerability, flood exposure and lack of coping capacity. Using an ordinary least squares (OLS) predictive model, observed satellite rainfall was used as a covariate in order to predict flood impacts on communities with high flood risk scores in Tana river. Weighted scores from the CRA dimensions were summed up with forecasted hazards from the OLS model in order to derive a flood impact-based forecast. The flood impact information is to be used in forecast-based action through early warning, early action protocols thereby reducing impacts of potential floods in communities living in high flood risk areas based on the flood risk map.</p>
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Hirpa, Feyera A., Peter Salamon, Lorenzo Alfieri, Jutta Thielen-del Pozo, Ervin Zsoter, and Florian Pappenberger. "The Effect of Reference Climatology on Global Flood Forecasting." Journal of Hydrometeorology 17, no. 4 (April 1, 2016): 1131–45. http://dx.doi.org/10.1175/jhm-d-15-0044.1.
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Abstract The Global Flood Awareness System (GloFAS) is a preoperational suite performing daily streamflow simulations to detect severe floods in large river basins. GloFAS defines the severity of a flood event with respect to thresholds estimated based on model-simulated streamflow climatology. Hence, determining accurate and consistent critical thresholds is important for its skillful flood forecasting. In this work, streamflow climatologies derived from two global meteorological inputs were compared, and their impacts on global flood forecasting were assessed. The first climatology is based on precipitation-corrected reanalysis data (ERA-Interim), which is currently used in the operational GloFAS forecast, while the second is derived from reforecasts that are routinely produced using the latest weather model. The results of the comparison indicate that 1) flood thresholds derived from the two datasets have substantial dissimilarities with varying characteristics across different regions of the globe; 2) the differences in the thresholds have a spatially variable impact on the severity classification of a flood; and 3) ERA-Interim produced lower flood threshold exceedance probabilities (and flood detection rates) than the reforecast for several large rivers at short forecast lead times, where the uncertainty in the meteorological forecast is smaller. Overall, it was found that the use of reforecasts, instead of ERA-Interim, marginally improved the flood detection skill of GloFAS forecasts.
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Belabid, Nasreddine, Feng Zhao, Luca Brocca, Yanbo Huang, and Yumin Tan. "Near-Real-Time Flood Forecasting Based on Satellite Precipitation Products." Remote Sensing 11, no. 3 (January 27, 2019): 252. http://dx.doi.org/10.3390/rs11030252.
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Floods, storms and hurricanes are devastating for human life and agricultural cropland. Near-real-time (NRT) discharge estimation is crucial to avoid the damages from flood disasters. The key input for the discharge estimation is precipitation. Directly using the ground stations to measure precipitation is not efficient, especially during a severe rainstorm, because precipitation varies even in the same region. This uncertainty might result in much less robust flood discharge estimation and forecasting models. The use of satellite precipitation products (SPPs) provides a larger area of coverage of rainstorms and a higher frequency of precipitation data compared to using the ground stations. In this paper, based on SPPs, a new NRT flood forecasting approach is proposed to reduce the time of the emergency response to flood disasters to minimize disaster damage. The proposed method allows us to forecast floods using a discharge hydrograph and to use the results to map flood extent by introducing SPPs into the rainfall–runoff model. In this study, we first evaluated the capacity of SPPs to estimate flood discharge and their accuracy in flood extent mapping. Two high temporal resolution SPPs were compared, integrated multi-satellite retrievals for global precipitation measurement (IMERG) and tropical rainfall measurement mission multi-satellite precipitation analysis (TMPA). The two products are evaluated over the Ottawa watershed in Canada during the period from 10 April 2017 to 10 May 2017. With TMPA, the results showed that the difference between the observed and modeled discharges was significant with a Nash–Sutcliffe efficiency (NSE) of −0.9241 and an adapted NSE (ANSE) of −1.0048 under high flow conditions. The TMPA-based model did not reproduce the shape of the observed hydrographs. However, with IMERG, the difference between the observed and modeled discharges was improved with an NSE equal to 0.80387 and an ANSE of 0.82874. Also, the IMERG-based model could reproduce the shape of the observed hydrographs, mainly under high flow conditions. Since IMERG products provide better accuracy, they were used for flood extent mapping in this study. Flood mapping results showed that the error was mostly within one pixel compared with the observed flood benchmark data of the Ottawa River acquired by RadarSat-2 during the flood event. The newly developed flood forecasting approach based on SPPs offers a solution for flood disaster management for poorly or totally ungauged watersheds regarding precipitation measurement. These findings could be referred to by others for NRT flood forecasting research and applications.
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Nevo, Sella, Efrat Morin, Adi Gerzi Rosenthal, Asher Metzger, Chen Barshai, Dana Weitzner, Dafi Voloshin, et al. "Flood forecasting with machine learning models in an operational framework." Hydrology and Earth System Sciences 26, no. 15 (August 5, 2022): 4013–32. http://dx.doi.org/10.5194/hess-26-4013-2022.
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Abstract. Google's operational flood forecasting system was developed to provide accurate real-time flood warnings to agencies and the public with a focus on riverine floods in large, gauged rivers. It became operational in 2018 and has since expanded geographically. This forecasting system consists of four subsystems: data validation, stage forecasting, inundation modeling, and alert distribution. Machine learning is used for two of the subsystems. Stage forecasting is modeled with the long short-term memory (LSTM) networks and the linear models. Flood inundation is computed with the thresholding and the manifold models, where the former computes inundation extent and the latter computes both inundation extent and depth. The manifold model, presented here for the first time, provides a machine-learning alternative to hydraulic modeling of flood inundation. When evaluated on historical data, all models achieve sufficiently high-performance metrics for operational use. The LSTM showed higher skills than the linear model, while the thresholding and manifold models achieved similar performance metrics for modeling inundation extent. During the 2021 monsoon season, the flood warning system was operational in India and Bangladesh, covering flood-prone regions around rivers with a total area close to 470 000 km2, home to more than 350 000 000 people. More than 100 000 000 flood alerts were sent to affected populations, to relevant authorities, and to emergency organizations. Current and future work on the system includes extending coverage to additional flood-prone locations and improving modeling capabilities and accuracy.