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1
Langdon, M. „Forecasting flood“. Engineering & Technology 4, Nr. 7 (25.04.2009): 40–42. http://dx.doi.org/10.1049/et.2009.0706.
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Xu, Wei, und Yong Peng. „Research on classified real-time flood forecasting framework based on K-means cluster and rough set“. Water Science and Technology 71, Nr. 10 (20.03.2015): 1507–15. http://dx.doi.org/10.2166/wst.2015.128.
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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.
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Brilly, M., und M. Polic. „Public perception of flood risks, flood forecasting and mitigation“. Natural Hazards and Earth System Sciences 5, Nr. 3 (18.04.2005): 345–55. http://dx.doi.org/10.5194/nhess-5-345-2005.
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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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Ren, Juanhui, Bo Ren, Qiuwen Zhang und 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, Nr. 9 (05.09.2019): 1848. http://dx.doi.org/10.3390/w11091848.
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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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Thiemig, V., B. Bisselink, F. Pappenberger und J. Thielen. „A pan-African Flood Forecasting System“. Hydrology and Earth System Sciences Discussions 11, Nr. 5 (27.05.2014): 5559–97. http://dx.doi.org/10.5194/hessd-11-5559-2014.
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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 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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Arduino, G., P. Reggiani und E. Todini. „Recent advances in flood forecasting and flood risk assessment“. Hydrology and Earth System Sciences 9, Nr. 4 (07.10.2005): 280–84. http://dx.doi.org/10.5194/hess-9-280-2005.
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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, und Falguni Parekh. „Flood Forecasting Using Artificial Neural Network“. IOP Conference Series: Earth and Environmental Science 1086, Nr. 1 (01.09.2022): 012036. http://dx.doi.org/10.1088/1755-1315/1086/1/012036.
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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, und Paramate Horkaew. „Application Programming Interface for Flood Forecasting from Geospatial Big Data and Crowdsourcing Data“. International Journal of Interactive Mobile Technologies (iJIM) 13, Nr. 11 (15.11.2019): 137. http://dx.doi.org/10.3991/ijim.v13i11.11237.
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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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Cluckie, I. D., und D. Han. „Fluvial Flood Forecasting“. Water and Environment Journal 14, Nr. 4 (August 2000): 270–76. http://dx.doi.org/10.1111/j.1747-6593.2000.tb00260.x.
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Wu, Heng Qing, Qiang Huang, Wei Xu und Shu Feng Xi. „Application of K-Means Cluster and Rough Set in Classified Real-Time Flood Forecasting“. Advanced Materials Research 1092-1093 (März 2015): 734–41. http://dx.doi.org/10.4028/www.scientific.net/amr.1092-1093.734.
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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 und Wen Zheng. „Multi-Step Sequence Flood Forecasting Based on MSBP Model“. Water 13, Nr. 15 (30.07.2021): 2095. http://dx.doi.org/10.3390/w13152095.
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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, und Hitoshi Miyamoto. „Real-Time Urban Flood Forecasting Systems for Southeast Asia—A Review of Present Modelling and Its Future Prospects“. Water 15, Nr. 1 (01.01.2023): 178. http://dx.doi.org/10.3390/w15010178.
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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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Tsegaw, Aynalem Tassachew, Thomas Skaugen, Knut Alfredsen und Tone M. Muthanna. „A dynamic river network method for the prediction of floods using a parsimonious rainfall-runoff model“. Hydrology Research 51, Nr. 2 (26.08.2019): 146–68. http://dx.doi.org/10.2166/nh.2019.003.
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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 und Y. Dong. „Study of Beijiang catchment flash-flood forecasting model“. Proceedings of the International Association of Hydrological Sciences 368 (06.05.2015): 150–55. http://dx.doi.org/10.5194/piahs-368-150-2015.
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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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Stanley, S. J., und R. Gerard. „Ice jam flood forecasting: Hay River, N.W.T.“ Canadian Journal of Civil Engineering 19, Nr. 2 (01.04.1992): 212–23. http://dx.doi.org/10.1139/l92-027.
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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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Zhan, Xiaoyan, Hui Qin, Yongqi Liu, Liqiang Yao, Wei Xie, Guanjun Liu und Jianzhong Zhou. „Variational Bayesian Neural Network for Ensemble Flood Forecasting“. Water 12, Nr. 10 (30.09.2020): 2740. http://dx.doi.org/10.3390/w12102740.
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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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Nguyen, Dinh Ty, und Shien-Tsung Chen. „Real-Time Probabilistic Flood Forecasting Using Multiple Machine Learning Methods“. Water 12, Nr. 3 (12.03.2020): 787. http://dx.doi.org/10.3390/w12030787.
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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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Yao, Yi, Zhongmin Liang, Weimin Zhao, Xiaolei Jiang und Binquan Li. „Performance assessment of hydrologic uncertainty processor through integration of the principal components analysis“. Journal of Water and Climate Change 10, Nr. 2 (11.12.2017): 373–90. http://dx.doi.org/10.2166/wcc.2017.137.
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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 und Yoichi Iwami. „Ensemble Flood Forecasting of Typhoons Talas and Roke at Hiyoshi Dam Basin“. Journal of Disaster Research 11, Nr. 6 (01.12.2016): 1032–39. http://dx.doi.org/10.20965/jdr.2016.p1032.
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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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Song, Tianyu, Wei Ding, Jian Wu, Haixing Liu, Huicheng Zhou und Jinggang Chu. „Flash Flood Forecasting Based on Long Short-Term Memory Networks“. Water 12, Nr. 1 (29.12.2019): 109. http://dx.doi.org/10.3390/w12010109.
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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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Chang, Fi-John, Yen-Chang Chen und Jin-Ming Liang. „Fuzzy Clustering Neural Network as Flood Forecasting Model“. Hydrology Research 33, Nr. 4 (01.08.2002): 275–90. http://dx.doi.org/10.2166/nh.2002.0008.
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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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Zhang, Yue, Zhaohui Gu, Jesse Van Griensven Thé, Simon X. Yang und Bahram Gharabaghi. „The Discharge Forecasting of Multiple Monitoring Station for Humber River by Hybrid LSTM Models“. Water 14, Nr. 11 (02.06.2022): 1794. http://dx.doi.org/10.3390/w14111794.
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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 und Young-Il Moon. „Integrated Flood Forecasting and Warning System against Flash Rainfall in the Small-Scaled Urban Stream“. Atmosphere 11, Nr. 9 (11.09.2020): 971. http://dx.doi.org/10.3390/atmos11090971.
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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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Hajibabaei, Ehsan, und Alireza Ghasemi. „Flood Management, Flood Forecasting and Warning System“. International Journal of Science and Engineering Applications 6, Nr. 2 (01.02.2017): 33–38. http://dx.doi.org/10.7753/ijsea0602.1001.
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Blackburn, J., und F. E. Hicks. „Combined flood routing and flood level forecasting“. Canadian Journal of Civil Engineering 29, Nr. 1 (01.02.2002): 64–75. http://dx.doi.org/10.1139/l01-079.
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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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Perumal, Muthiah, Tommaso Moramarco, Silvia Barbetta, Florisa Melone und Bhabagrahi Sahoo. „Real-time flood stage forecasting by Variable Parameter Muskingum Stage hydrograph routing method“. Hydrology Research 42, Nr. 2-3 (01.04.2011): 150–61. http://dx.doi.org/10.2166/nh.2011.063.
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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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Thiemig, V., B. Bisselink, F. Pappenberger und J. Thielen. „A pan-African medium-range ensemble flood forecast system“. Hydrology and Earth System Sciences 19, Nr. 8 (03.08.2015): 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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El Khalki, El Mahdi, Yves Tramblay, Arnau Amengual, Victor Homar, Romualdo Romero, Mohamed El Mehdi Saidi und Meriem Alaouri. „Validation of the AROME, ALADIN and WRF Meteorological Models for Flood Forecasting in Morocco“. Water 12, Nr. 2 (06.02.2020): 437. http://dx.doi.org/10.3390/w12020437.
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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.
29
Lai, Chintu, Ting-Kuei Tsay, Chen-Ho Chien und I.-Ling Wu. „Real-time Flood Forecasting“. American Scientist 97, Nr. 2 (2009): 118. http://dx.doi.org/10.1511/2009.77.118.
30
Moore, Robert J., Victoria A. Bell und David A. Jones. „Forecasting for flood warning“. Comptes Rendus Geoscience 337, Nr. 1-2 (Januar 2005): 203–17. http://dx.doi.org/10.1016/j.crte.2004.10.017.
31
Ryder, Peter. „Flood forecasting and warning“. Meteorological Applications 16, Nr. 1 (März 2009): 1–2. http://dx.doi.org/10.1002/met.133.
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Alfieri, L., P. Burek, E. Dutra, B. Krzeminski, D. Muraro, J. Thielen und F. Pappenberger. „GloFAS – global ensemble streamflow forecasting and flood early warning“. Hydrology and Earth System Sciences Discussions 9, Nr. 11 (02.11.2012): 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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Otieno, O. M., H. S. Abdillahi, E. M. Wambui und 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 (22.08.2019): 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 und Florian Pappenberger. „The Effect of Reference Climatology on Global Flood Forecasting“. Journal of Hydrometeorology 17, Nr. 4 (01.04.2016): 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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Liu, Li, Yue Ping Xu, Su Li Pan und 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, Nr. 8 (14.08.2019): 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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Younis, J., S. Anquetin und J. Thielen. „The benefit of high-resolution operational weather forecasts for flash flood warning“. Hydrology and Earth System Sciences Discussions 5, Nr. 1 (12.02.2008): 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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Belabid, Nasreddine, Feng Zhao, Luca Brocca, Yanbo Huang und Yumin Tan. „Near-Real-Time Flood Forecasting Based on Satellite Precipitation Products“. Remote Sensing 11, Nr. 3 (27.01.2019): 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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Mandryk, Oleg, Andriy Oliynyk, Roman Mykhailyuk und Lidiia Feshanych. „Flood Development Process Forecasting Based on Water Resources Statistical Data“. Grassroots Journal of Natural Resources 4, Nr. 2 (30.05.2021): 65–76. http://dx.doi.org/10.33002/nr2581.6853.040205.
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The Ukrainian Carpathians is the territory with a great threat of floods. This is due to natural and climatic conditions of this region, which is characterized by mountainous terrain, high density of hydrological network and a significant amount of precipitation. Amount of precipitation here ranges from 600 mm on plains to 1,600 mm on mountain tops. The main factors of floods occurrence are excessive precipitation, low water permeability of soil and a high proportion of low-permeability rocks (flysch layers with a predominance of clay layers). Therefore, catastrophic floods in the region were also observed in previous centuries, when the anthropogenic impact on the environment, including forest ecosystems, was not comparable with the current one. Any flood is characterized by a period of development, a period of its critical (maximum) intensity and a period of decline. In the present paper, based on the use of methods for approximating the curves and the results of experimental studies of flood waters, a method of mathematical description and forecasting of the flood development is suggested. The recommended direction of further research may be related to the development of experimental means to determine the parameters that affect the process of flood formation.
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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, Nr. 15 (05.08.2022): 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.
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Noor, Fahima, Sanaulla Haq, Mohammed Rakib, Tarik Ahmed, Zeeshan Jamal, Zakaria Shams Siam, Rubyat Tasnuva Hasan, Mohammed Sarfaraz Gani Adnan, Ashraf Dewan und Rashedur M. Rahman. „Water Level Forecasting Using Spatiotemporal Attention-Based Long Short-Term Memory Network“. Water 14, Nr. 4 (17.02.2022): 612. http://dx.doi.org/10.3390/w14040612.
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Bangladesh is in the floodplains of the Ganges, Brahmaputra, and Meghna River delta, crisscrossed by an intricate web of rivers. Although the country is highly prone to flooding, the use of state-of-the-art deep learning models in predicting river water levels to aid flood forecasting is underexplored. Deep learning and attention-based models have shown high potential for accurately forecasting floods over space and time. The present study aims to develop a long short-term memory (LSTM) network and its attention-based architectures to predict flood water levels in the rivers of Bangladesh. The models developed in this study incorporated gauge-based water level data over 7 days for flood prediction at Dhaka and Sylhet stations. This study developed five models: artificial neural network (ANN), LSTM, spatial attention LSTM (SALSTM), temporal attention LSTM (TALSTM), and spatiotemporal attention LSTM (STALSTM). The multiple imputation by chained equations (MICE) method was applied to address missing data in the time series analysis. The results showed that the use of both spatial and temporal attention together increases the predictive performance of the LSTM model, which outperforms other attention-based LSTM models. The STALSTM-based flood forecasting system, developed in this study, could inform flood management plans to accurately predict floods in Bangladesh and elsewhere.
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Salehi, Farhad, Mohsen Najarchi, Mohammad Mahdi Najafizadeh und Mohammad Mirhoseini Hezaveh. „Multistage Models for Flood Control by Gated Spillway: Application to Karkheh Dam“. Water 14, Nr. 5 (23.02.2022): 709. http://dx.doi.org/10.3390/w14050709.
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The paper demonstrates a simulation optimization framework for enhancing the real-time flood control with gated spillways at places where no flood forecasting data are available. A multiobjective modeling scheme is presented for the flood management in a gated spillway in which the operator may specify the priorities on floods based on their different return periods. Two different operation strategies were devised. Both operating strategies employ ten-stage policies, which rely on the reservoir water level as the input data. The second strategy benefits from both the observed reservoir water level and the flood peak. The optimal values of the models’ parameters were obtained using a genetic algorithm. This is a novel approach because none of its policies needs flood forecasting data, thus, making them adaptable to any flood with any return period. To evaluate the performances of the proposed models, the flood control through a gated spillway of the Karkheh reservoir was considered, where flood hydrographs with different return periods were routed through the reservoir.
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Liu, Zhiyu. „Operational flood forecasting and warning under the changing environment in China“. Proceedings of the International Association of Hydrological Sciences 383 (16.09.2020): 223–28. http://dx.doi.org/10.5194/piahs-383-223-2020.
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Abstract. Flooding has been the most severe hazard in China from time immemorial, due to its special geographical, climatic and socio-economic conditions. In recent years, against the background of global climate change and rapid urbanization, extreme hydro-meteorological events have obviously increased in China, in turn affecting the sustainable social and economic development. This paper analyses how modern flood risk management has evolved from the early “build and protect” flood control approach to a broader flood risk management approach by analyzing what has happened in China and the role of major floods in this evolution. The development of flood forecasting models and systems in terms of how they have informed decision-making as flood risk management has evolved over the years. The challenging of recognizing and dealing with forecast uncertainty and flood risk in decision-making is also analyzed in the paper.
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Dutta, Upasana, Yogesh Kumar Singh, T. S. Murugesh Prabhu, Girishchandra Yendargaye, Rohini Gopinath Kale, Binay Kumar, Manoj Khare, Rahul Yadav, Ritesh Khattar und Sushant Kumar Samal. „Flood Forecasting in Large River Basins Using FOSS Tool and HPC“. Water 13, Nr. 24 (07.12.2021): 3484. http://dx.doi.org/10.3390/w13243484.
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The Indian subcontinent is annually affected by floods that cause profound irreversible damage to crops and livelihoods. With increased incidences of floods and their related catastrophes, the design, development, and deployment of an Early Warning System for Flood Prediction (EWS-FP) for the river basins of India is needed, along with timely dissemination of flood-related information for mitigation of disaster impacts. Accurately drafted and disseminated early warnings/advisories may significantly reduce economic losses incurred due to floods. This study describes the design and development of an EWS-FP using advanced computational tools/methods, viz. HPC, remote sensing, GIS technologies, and open-source tools for the Mahanadi River Basin of India. The flood prediction is based on a robust 2D hydrodynamic model, which solves shallow water equations using the finite volume method. The model is open-source, supports geographic file formats, and is capable of simulating rainfall run-off, river routing, and tidal forcing, simultaneously. The model was tested for a part of the Mahanadi River Basin (Mahanadi Delta, 9225 sq km) with actual and predicted discharge, rainfall, and tide data. The simulated flood inundation spread and stage were compared with SAR data and CWC Observed Gauge data, respectively. The system shows good accuracy and better lead time suitable for flood forecasting in near-real-time.
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Nadeem, Muhammad Umer, Zeeshan Waheed, Abdul Mannan Ghaffar, Muhammad Mashood Javaid, Ameer Hamza, Zain Ayub, Muhammad Asim Nawaz et al. „Application of HEC-HMS for flood forecasting in hazara catchment Pakistan, south Asia“. International Journal of Hydrology 6, Nr. 1 (17.01.2022): 7–12. http://dx.doi.org/10.15406/ijh.2022.06.00296.
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Floods have become more severe and frequent as a result of climate change around the world, posing a hazard to public safety and economic development. This study investigates the use of distributed hydrological models in flash flood risk management in a small watershed in Hazara, Pakistan, with the goal of improving Pakistan's early warning lead time. First, the HEC-HMS model was built using geographic data and the river network's structure, then calibrated and verified using eight high rainfall events from 2013. demonstrating that the HEC-HMS model could simulate floods in the research area Second, given that rainfall and flood events have happened, this paper proposes an analysis approach for a flood forecasting and warning system, as well as criteria for sending urban-stream flash flood alerts based on rainfall, in order to provide sufficient lead time. The DEMs (digital elevation models) of the research regions were processed using HEC-Geo HMS, an ArcView GIS tool for catchment delineation, terrain pre-processing, and basin processing. The model was calibrated and verified using previously observed data. The proposed flood prediction and risk reduction methodology is nonstructural. The Hydrologic Modeling System (HEC-HMS), which provides a sufficient lead time forecast and computes the runoff/stage threshold conditions, is at the heart of the flood warning application. For flood risk assessment, data from the Pakistan Meteorological Department (PMD) is entered into a hydro-meteorological database and then into the HEC-HMS. A server-client application was utilised to visualise the real-time flood scenario and send out an early warning message. The outcomes of this study will be used to develop flood validation measures in the Hazara stream watershed to deal with potential flash floods.
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Chang, Tzu-Yin, Hongey Chen, Huei-Shuin Fu, Wei-Bo Chen, Yi-Chiang Yu, Wen-Ray Su und Lee-Yaw Lin. „An Operational High-Performance Forecasting System for City-Scale Pluvial Flash Floods in the Southwestern Plain Areas of Taiwan“. Water 13, Nr. 4 (04.02.2021): 405. http://dx.doi.org/10.3390/w13040405.
Annotation:
A pluvial flash flood is rapid flooding induced by intense rainfall associated with a severe weather system, such as thunderstorms or typhoons. Additionally, topography, ground cover, and soil conditions also account for the occurrence of pluvial flash floods. Pluvial flash floods are among the most devastating natural disasters that occur in Taiwan, and these floods always /occur within a few minutes or hours of excessive rainfall. Pluvial flash floods usually threaten large plain areas with high population densities; therefore, there is a great need to implement an operational high-performance forecasting system for pluvial flash flood mitigation and evacuation decisions. This study developed a high-performance two-dimensional hydrodynamic model based on the finite-element method and unstructured grids. The operational high-performance forecasting system is composed of the Weather Research and Forecasting (WRF) model, the Storm Water Management Model (SWMM), a two-dimensional hydrodynamic model, and a map-oriented visualization tool. The forecasting system employs digital elevation data with a 1-m resolution to simulate city-scale pluvial flash floods. The extent of flooding during historical inundation events derived from the forecasting system agrees well with the surveyed data for plain areas in southwestern Taiwan. The entire process of the operational high-performance forecasting system prediction of pluvial flash floods in the subsequent 24 h is accomplished within 8–10 min, and forecasts are updated every six hours.
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Cai, Yaxi, und Xiaodong Yang. „Flood Risk Forecasting and Early Warning Technology for Medium and Small Rivers in the Yellow River Basin Induced by Heavy Rain“. Journal of Electronic Research and Application 6, Nr. 5 (26.10.2022): 8–14. http://dx.doi.org/10.26689/jera.v6i5.4438.
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The Yellow River Basin is one of the important sand-producing and sediment-transporting areas in China, and one of the three most important sand-producing areas in the world. The amount of sand and dust days in the “Three Norths” (Dongbei, Xibei, and Huabei) area has increased, and regional sand and dust storms have occurred frequently. There are generally more serious hidden danger points of debris flow geological disasters in small and medium-sized river basins. The technical achievements of flood risk forecasting and early warning for medium and small rivers in the Yellow River Basin based on rainstorm-induced floods are important technical supports for flood forecasting and early warning for medium and small rivers. Based on this, a case study was carried out on the problems such as the weak forecasting and early warning ability of flood disasters induced by heavy rain and the low accuracy of flood disaster loss assessment in the flood disasters of medium and small rivers, for the reference of relevant personnel.
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Minghong, Chen, Fang Hongwei, Zheng Yi und He Guojian. „Integrated Flood Management for Beiyun River, China“. Journal of Hydrology and Hydromechanics 61, Nr. 3 (01.09.2013): 177–87. http://dx.doi.org/10.2478/johh-2013-0023.
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Abstract Beiyun River Basin is holistically suffering a water shortage and relatively concentrated flood risk. The current operation (level-control) of dams and floodgates, which is in passive defense mode, cannot meet the demands of both flood control and storm water resources. An integrated flood forecasting and management system is developed by the connecting of the hydrological model and hydrodynamic model and coupling of the hydrodynamic model and hydraulic model for dams and floodgates. Based upon the forecasted runoff processes, a discharge-control operation mode of dams and floodgates is proposed to be utilized in order to well regulate the flood routing in channels. The simulated water level, discharge, and water storage volume under different design conditions of rainfall return periods and floodgates operation modes are compared. The results show that: (1) for small floods, current operation modes can satisfy the objectives, but discharge-control operation can do better; (2) for medium size floods, since pre-storing of the floods affects the discharge of follow-up floods by floodgates, the requirement of flood control cannot be satisfied under current operations, but the discharge-control operation can; (3) for large floods, neither operation can meet the requirement because of the limited storage of these dams. Then, the gravel pits, wetlands, ecological lakes and flood detention basins around the river must be used for excess flood waters. Using the flood forecasting and management system can change passive defense to active defense mode, solving the water resources problem of Beijing city and Beiyun River Basin to a certain extent.
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Verkade, J. S., und M. G. F. Werner. „Estimating the benefits of single value and probability forecasting for flood warning“. Hydrology and Earth System Sciences Discussions 8, Nr. 4 (11.07.2011): 6639–81. http://dx.doi.org/10.5194/hessd-8-6639-2011.
Annotation:
Abstract. Flood risk can be reduced by means of flood forecasting, warning and response systems (FFWRS). These systems include a forecasting sub-system which is imperfect, meaning that inherent uncertainties in hydrological forecasts may result in false alarms and missed floods, or surprises. This forecasting uncertainty decreases the potential reduction of flood risk, but is seldom accounted for in estimates of the benefits of FFWRSs. In the present paper, a method to estimate the benefits of (imperfect) FFWRSs in reducing flood risk is presented. These benefits include not only the reduction of flood losses due to a warning response, but also consider the costs of the warning response itself, as well as the costs associated with forecasting uncertainty. The method allows for estimation of the benefits of FFWRSs that use either deterministic or probabilistic forecasts. Through application to a case study, it is shown that FFWRSs using a probabilistic forecast have the potential to realise higher benefits at all lead-times. However, it is also shown that provision of warning at increasing lead-time does not necessarily lead to an increasing reduction of flood risk, but rather that an optimal lead-time at which warnings are provided can be established as a function of forecast uncertainty and the cost-loss ratio of the user receiving and responding to the warning.
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Macalalad, Rhonalyn V., Roy A. Badilla, Olivia C. Cabrera und Gerry Bagtasa. „Hydrological Response of the Pampanga River Basin in the Philippines to Intense Tropical Cyclone Rainfall“. Journal of Hydrometeorology 22, Nr. 4 (April 2021): 781–94. http://dx.doi.org/10.1175/jhm-d-20-0184.1.
Annotation:
AbstractThe Philippines is frequently affected by tropical cyclones (TCs), and understanding the flood response of the Pampanga River basin (PRB) from TC-induced rain is needed in effective disaster risk management. As large uncertainties remain in TC rain forecasting, we propose a simple checklist method for flood forecasting of the PRB that depends on the general TC track, season, and accumulated rainfall. To this end, flood events were selected based on the alert, alarm, and critical river height levels established by the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA). Results show that all flood events in the PRB were induced by TCs. All intense TCs that directly traversed the PRB resulted in critical-level floods. These TCs also had the shortest flood onset of 7–27 h from alert to critical level. Flooding from distant landfalling TCs, on the other hand, are dependent on season. TCs traversing north (south) of the PRB induced flooding only during the southwest (northeast) monsoon season. These TCs can raise water levels from alert to critical in 11–48 h. Remote precipitation from non-landfalling TCs can also induce critical-level flooding but with a longer onset time of 59 h. These results indicate that a simple checklist method can serve as a useful tool for flood forecasting in regions with limited data and forecasting resources.
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Láng-Ritter, Josias, Marc Berenguer, Francesco Dottori, Milan Kalas und Daniel Sempere-Torres. „Compound flood impact forecasting: integrating fluvial and flash flood impact assessments into a unified system“. Hydrology and Earth System Sciences 26, Nr. 3 (10.02.2022): 689–709. http://dx.doi.org/10.5194/hess-26-689-2022.
Annotation:
Abstract. Floods can arise from a variety of physical processes. Although numerous risk assessment approaches stress the importance of taking into account the possible combinations of flood types (i.e. compound floods), this awareness has so far not been reflected in the development of early warning systems: existing methods for forecasting flood hazards or the corresponding socio-economic impacts are generally designed for only one type of flooding. During compound flood events, these flood type-specific approaches are unable to identify overall hazards or impacts. Moreover, from the perspective of end-users (e.g. civil protection authorities), the monitoring of separate flood forecasts – with potentially contradictory outputs – can be confusing and time-consuming, and ultimately impede an effective emergency response. To enhance decision support, this paper proposes the integration of different flood type-specific approaches into one compound flood impact forecast. This possibility has been explored through the development of a unified system combining the simulations of two impact forecasting methods: the Rapid Risk Assessment of the European Flood Awareness System (EFAS RRA; representing fluvial floods) and the radar-based ReAFFIRM method (representing flash floods). The unified system has been tested for a recent catastrophic episode of compound flooding: the DANA event of September 2019 in south-east Spain (Depresión Aislada en Niveles Altos, meaning cut-off low). The combination of the two methods identified well the overall compound flood extents and impacts reported by various information sources. For instance, the simulated economic losses amounted to about EUR 670 million against EUR 425 million of reported insured losses. Although the compound impact estimates were less accurate at municipal level, they corresponded much better to the observed impacts than those generated by the two methods applied separately. This demonstrates the potential of such integrated approaches for improving decision support services.