Journal articles on the topic 'Flood forecasting Australia Mathematical models'
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1
Zhijia, Li, and Kong Xiangguang. "Comparison on Three Mathematical Models For Special Values in Flood Forecasting." Journal of Lake Sciences 9, no. 2 (1997): 117–22. http://dx.doi.org/10.18307/1997.0204.
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White, C. J., S. W. Franks, and D. McEvoy. "Using subseasonal-to-seasonal (S2S) extreme rainfall forecasts for extended-range flood prediction in Australia." Proceedings of the International Association of Hydrological Sciences 370 (June 11, 2015): 229–34. http://dx.doi.org/10.5194/piahs-370-229-2015.
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Abstract. Meteorological and hydrological centres around the world are looking at ways to improve their capacity to be able to produce and deliver skilful and reliable forecasts of high-impact extreme rainfall and flooding events on a range of prediction timescales (e.g. sub-daily, daily, multi-week, seasonal). Making improvements to extended-range rainfall and flood forecast models, assessing forecast skill and uncertainty, and exploring how to apply flood forecasts and communicate their benefits to decision-makers are significant challenges facing the forecasting and water resources management communities. This paper presents some of the latest science and initiatives from Australia on the development, application and communication of extreme rainfall and flood forecasts on the extended-range "subseasonal-to-seasonal" (S2S) forecasting timescale, with a focus on risk-based decision-making, increasing flood risk awareness and preparedness, capturing uncertainty, understanding human responses to flood forecasts and warnings, and the growing adoption of "climate services". The paper also demonstrates how forecasts of flood events across a range of prediction timescales could be beneficial to a range of sectors and society, most notably for disaster risk reduction (DRR) activities, emergency management and response, and strengthening community resilience. Extended-range S2S extreme flood forecasts, if presented as easily accessible, timely and relevant information are a valuable resource to help society better prepare for, and subsequently cope with, extreme flood events.
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De OLIVEIRA, Guilherme Garcia, Dejanira Luderitz SALDANHA, and Laurindo Antonio GUASSELLI. "MODELS FOR SPATIALIZATION AND FORECASTING OF FLOODED AREAS IN THE SÃO SEBASTIÃO DO CAÍ URBAN ZONE, RIO GRANDE DO SUL STATE, BRAZIL." Pesquisas em Geociências 38, no. 2 (August 31, 2011): 132. http://dx.doi.org/10.22456/1807-9806.26379.
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The study aims at developing models for the spatialization and forecasting of floods in the urban area of São Sebastião do Caí, RS, Brazil. For the calculation of return period (RP), and in order to analyze the seasonality of floods, streamflow data from the station located in the city were used. However, for the development of a mathematical model for flood forecasting, the time series of a station upstream was also used in order to perform a regression with the quotas recorded in both seasons. For the identification of flood plains, a digital terrain model was produced based on elevation data in scales between 1:2,000 and 1:10,000. The QuickBird satellite image (spatial resolution of 0.61 m) was used only for the spatialization of the land use and land cover reached by each flood scenario. Mapping and 3D simulation of the areas affected by flooding were obtained for RP of 2, 5, 10 and 30 years. The following results are most significant: i) the river water level rises between 9.28 m and 11.98 m for RP of 2 to 30 years; ii) along the historical series, 75% of floods have occurred between June and October; iii) the mathematical model for flood forecasting showed an average error of 0.72 m, and the accuracy varies between 0.62 m and 1.84 m, according to the expected magnitude; iv) it was observed that 93 hectares of urban area in São Sebastião do Caí are hit by floods with a RP of 30 years (23% of the urban area); v) modelling of a recent flood event dated of 24/09/2007 has resulted in similar values for the simulated and observed flooded area.
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Marquez, Adriana, Bettys Farias, and Edilberto Guevara. "Method for forecasting the flood risk in a tropical country." Water Supply 20, no. 6 (June 18, 2020): 2261–74. http://dx.doi.org/10.2166/ws.2020.129.
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Abstract In this study, a novel method for forecasting the flood risk in a tropical country is proposed, called CIHAM-UC-FFR. The method is based on the rainfall–runoff process. The CIHAM-UC-FFR method consists of three stages: (1) calibration and validation for the effective precipitation model, called CIHAM-UC-EP model, (2) calibration of forecasting models for components of the CIHAM-UC-EP model, (3) proposed model for forecasting of gridded flood risk called CIHAM-UC-FR. The CIHAM-UC-EP model has a mathematical structure derived from a conceptual model obtained by applying the principle of mass conservation combined with the adapted principle of Fick's law. The CIHAM-UC-FR model is a stochastic equation based on the exceedance probability of the forecast effective precipitation. Various scenarios are shown for a future time where the flood risk is progressively decreased as the expected life parameter of the hydraulic structure is increased.
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Kizilova, N. M., and N. L. Rychak. "Probabilistic models of water resources management on urbanized areas." Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics and Mathematics, no. 4 (2020): 22–27. http://dx.doi.org/10.17721/1812-5409.2020/4.3.
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Gradual global climate change poses new challenges to the mathematical sciences, which are related to forecasting of meteorological conditions, preparing the infrastructure for possible rains, storms, droughts, and other climatic disasters. One of the most common approaches is synthetic regression-probability models, which use the spatio-temporal probability density functions of precipitation level. This approach is applied to the statistics of precipitation in the Kharkiv region, which shows the tendency to a gradual increase in air temperature, high indices of basic water stress, indices of drought and riverside flood threats. Open data on temperature distributions and precipitation were processed using various probability statistics. It is shown that the lognormal distribution most accurately describes the measurement data and allows making more accurate prognoses. Estimates of drought and flood probabilities in Kharkiv region under different scenarios of climate change dynamics have been carried out. The results of the study can be used for management of water resources on urban territories at global climate warming.
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Simmonds, Jose, Juan A. Gómez, and Agapito Ledezma. "The role of agent-based modeling and multi-agent systems in flood-based hydrological problems: a brief review." Journal of Water and Climate Change 11, no. 4 (October 25, 2019): 1580–602. http://dx.doi.org/10.2166/wcc.2019.108.
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Abstract Flood problems are complex phenomena with a direct relationship with the hydrological cycle; these are natural processes occurring in water systems, that interact at different spatial and temporal scales. In modeling the hydrological phenomena, traditional approaches, like physics-based mathematical equations and data-driven modeling (DDM) are used. Advances in hydroinformatics are helping to understand these physical processes, with improvements in the collection and analysis of hydrological data, information and communication technologies (ICT), and geographic information systems (GIS), offering opportunities for innovations in model implementation, to improve decision support for the response to societally important floods impacting our societies. This paper offers a brief review of agent-based models (ABMs) and multi-agent systems (MASs) methodologies' applications for solutions to flood problems, their management, assessment, and efforts for forecasting stream flow and flood events. Significant observations from this review include: (i) contributions of agent technologies, as a growing methodology in hydrology; (ii) limitations; (iii) capabilities of dealing with distributed and complex domains; and (iv), the capabilities of MAS as an increasingly accepted point of view applied to flood modeling, with examples presented to show the variety of system combinations that are practical on a specialized architectural level for developing and deploying sophisticated flood forecasting systems.
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Franks, S. W. "Multi-decadal climate variability, New South Wales, Australia." Water Science and Technology 49, no. 7 (April 1, 2004): 133–40. http://dx.doi.org/10.2166/wst.2004.0437.
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Traditional hydrological risk estimation has treated the observations of hydro-climatological extremes as being independent and identically distributed, implying a static climate risk. However, recent research has highlighted the persistence of multi-decadal epochs of distinct climate states across New South Wales (NSW), Australia. Climatological studies have also revealed multi-decadal variability in the magnitude and frequency of El Niño/Southern Oscillation (ENSO) impacts. In this paper, examples of multi-decadal variability are presented with regard to flood and drought risk. The causal mechanisms for the observed variability are then explored. Finally, it is argued that the insights into climate variability provide (a) useful lead time for forecasting seasonal hydrological risk, (b) a strong rationale for a new framework for hydrological design and (c) a strong example of natural climate variability for use in the testing of General Circulation Models of climate change.
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Mazzoleni, M., M. Verlaan, L. Alfonso, M. Monego, D. Norbiato, M. Ferri, and D. P. Solomatine. "Can assimilation of crowdsourced streamflow observations in hydrological modelling improve flood prediction?" Hydrology and Earth System Sciences Discussions 12, no. 11 (November 3, 2015): 11371–419. http://dx.doi.org/10.5194/hessd-12-11371-2015.
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Abstract. Monitoring stations have been used for decades to properly measure hydrological variables and better predict floods. To this end, methods to incorporate such observations into mathematical water models have also being developed, including data assimilation. Besides, in recent years, the continued technological improvement has stimulated the spread of low-cost sensors that allow for employing crowdsourced and obtain observations of hydrological variables in a more distributed way than the classic static physical sensors allow. However, such measurements have the main disadvantage to have asynchronous arrival frequency and variable accuracy. For this reason, this study aims to demonstrate how the crowdsourced streamflow observations can improve flood prediction if integrated in hydrological models. Two different types of hydrological models, applied to two case studies, are considered. Realistic (albeit synthetic) streamflow observations are used to represent crowdsourced streamflow observations in both case studies. Overall, assimilation of such observations within the hydrological model results in a significant improvement, up to 21 % (flood event 1) and 67 % (flood event 2) of the Nash–Sutcliffe efficiency index, for different lead times. It is found that the accuracy of the observations influences the model results more than the actual (irregular) moments in which the streamflow observations are assimilated into the hydrological models. This study demonstrates how networks of low-cost sensors can complement traditional networks of physical sensors and improve the accuracy of flood forecasting.
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Robertson, D. E., D. L. Shrestha, and Q. J. Wang. "Post-processing rainfall forecasts from numerical weather prediction models for short-term streamflow forecasting." Hydrology and Earth System Sciences 17, no. 9 (September 27, 2013): 3587–603. http://dx.doi.org/10.5194/hess-17-3587-2013.
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Abstract. Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post-processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post-processing raw numerical weather prediction (NWP) rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for individual locations and forecast lead times. Ensemble forecasts with appropriate spatial and temporal correlations are then generated by linking samples from the forecast probability distributions using the Schaake shuffle. The new approach is evaluated by applying it to post-process predictions from the ACCESS-R numerical weather prediction model at rain gauge locations in the Ovens catchment in southern Australia. The joint distribution of NWP predicted and observed rainfall is shown to be well described by the assumed log-sinh transformed bivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for individual forecast lead times and for cumulative totals throughout all forecast lead times. Skill increases result from the correction of not only the mean bias, but also biases conditional on the magnitude of the NWP rainfall prediction. The post-processed forecast ensembles are demonstrated to successfully discriminate between events and non-events for both small and large rainfall occurrences, and reliably quantify the forecast uncertainty. Future work will assess the efficacy of the post-processing method for a wider range of climatic conditions and also investigate the benefits of using post-processed rainfall forecasts for flood and short-term streamflow forecasting.
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Robertson, D. E., D. L. Shrestha, and Q. J. Wang. "Post processing rainfall forecasts from numerical weather prediction models for short term streamflow forecasting." Hydrology and Earth System Sciences Discussions 10, no. 5 (May 29, 2013): 6765–806. http://dx.doi.org/10.5194/hessd-10-6765-2013.
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Abstract. Sub-daily ensemble rainfall forecasts that are bias free and reliably quantify forecast uncertainty are critical for flood and short-term ensemble streamflow forecasting. Post processing of rainfall predictions from numerical weather prediction models is typically required to provide rainfall forecasts with these properties. In this paper, a new approach to generate ensemble rainfall forecasts by post processing raw NWP rainfall predictions is introduced. The approach uses a simplified version of the Bayesian joint probability modelling approach to produce forecast probability distributions for individual locations and forecast periods. Ensemble forecasts with appropriate spatial and temporal correlations are then generated by linking samples from the forecast probability distributions using the Schaake shuffle. The new approach is evaluated by applying it to post process predictions from the ACCESS-R numerical weather prediction model at rain gauge locations in the Ovens catchment in southern Australia. The joint distribution of NWP predicted and observed rainfall is shown to be well described by the assumed log-sinh transformed multivariate normal distribution. Ensemble forecasts produced using the approach are shown to be more skilful than the raw NWP predictions both for individual forecast periods and for cumulative totals throughout the forecast periods. Skill increases result from the correction of not only the mean bias, but also biases conditional on the magnitude of the NWP rainfall prediction. The post processed forecast ensembles are demonstrated to successfully discriminate between events and non-events for both small and large rainfall occurrences, and reliably quantify the forecast uncertainty. Future work will assess the efficacy of the post processing method for a wider range of climatic conditions and also investigate the benefits of using post processed rainfall forecast for flood and short term streamflow forecasting.
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Jha, Sanjeev K., Durga L. Shrestha, Tricia A. Stadnyk, and Paulin Coulibaly. "Evaluation of ensemble precipitation forecasts generated through post-processing in a Canadian catchment." Hydrology and Earth System Sciences 22, no. 3 (March 23, 2018): 1957–69. http://dx.doi.org/10.5194/hess-22-1957-2018.
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Abstract. Flooding in Canada is often caused by heavy rainfall during the snowmelt period. Hydrologic forecast centers rely on precipitation forecasts obtained from numerical weather prediction (NWP) models to enforce hydrological models for streamflow forecasting. The uncertainties in raw quantitative precipitation forecasts (QPFs) are enhanced by physiography and orography effects over a diverse landscape, particularly in the western catchments of Canada. A Bayesian post-processing approach called rainfall post-processing (RPP), developed in Australia (Robertson et al., 2013; Shrestha et al., 2015), has been applied to assess its forecast performance in a Canadian catchment. Raw QPFs obtained from two sources, Global Ensemble Forecasting System (GEFS) Reforecast 2 project, from the National Centers for Environmental Prediction, and Global Deterministic Forecast System (GDPS), from Environment and Climate Change Canada, are used in this study. The study period from January 2013 to December 2015 covered a major flood event in Calgary, Alberta, Canada. Post-processed results show that the RPP is able to remove the bias and reduce the errors of both GEFS and GDPS forecasts. Ensembles generated from the RPP reliably quantify the forecast uncertainty.
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Malekpour Heydari, Salimeh, Teh Noranis Mohd Aris, Razali Yaakob, and Hazlina Hamdan. "Data-Driven Forecasting and Modeling of Runoff Flow to Reduce Flood Risk Using a Novel Hybrid Wavelet-Neural Network Based on Feature Extraction." Sustainability 13, no. 20 (October 19, 2021): 11537. http://dx.doi.org/10.3390/su132011537.
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The reliable forecasting of river flow plays a key role in reducing the risk of floods. Regarding nonlinear and variable characteristics of hydraulic processes, the use of data-driven and hybrid methods has become more noticeable. Thus, this paper proposes a novel hybrid wavelet-neural network (WNN) method with feature extraction to forecast river flow. To do this, initially, the collected data are analyzed by the wavelet method. Then, the number of inputs to the ANN is determined using feature extraction, which is based on energy, standard deviation, and maximum values of the analyzed data. The proposed method has been analyzed by different input and various structures for daily, weekly, and monthly flow forecasting at Ellen Brook river station, western Australia. Furthermore, the mean squares error (MSE), root mean square error (RMSE), and the Nash-Sutcliffe efficiency (NSE) is used to evaluate the performance of the suggested method. Furthermore, the obtained findings were compared to those of other models and methods in order to examine the performance and efficiency of the feature extraction process. It was discovered that the proposed feature extraction model outperformed their counterparts, especially when it came to long-term forecasting.
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Issermann, Maikel, Fi-John Chang, and Haifeng Jia. "Efficient Urban Inundation Model for Live Flood Forecasting with Cellular Automata and Motion Cost Fields." Water 12, no. 7 (July 14, 2020): 1997. http://dx.doi.org/10.3390/w12071997.
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The mitigation of societal damage from urban floods requires fast hydraulic models for emergency and planning purposes. The simplified mathematical model Cellular Automata is combined with Motion Cost fields, which score the difficulty to traverse an area, to the urban inundation model CAMC. It is implemented with simple matrix and logic operations to achieve high computational efficiency. The development concentrated on an application in dense urban built-up areas with numerous buildings. CAMC is efficient and flexible enough to be used in a “live” urban flood warning system with current weather conditions. A case study is conducted in the German city of Wuppertal with about 12,000 buildings. The water depth estimation of every time step are visualized in a web-interface on the basis of the virtual globe NASA WorldWind. CAMC is compared with the shallow water equations-based model ANUGA. CAMC is approximatively 5 times faster than ANUGA at high spatial resolution and able to maintain numerical stability. The Nash-Sutcliffe coefficient (0.61), Root Mean Square Error (0.39 m) and Index of Agreement (0.65) indicate acceptable agreement for water depth estimation but identify different areas where important deviations occur. The estimation of velocity performs considerably less well (0.34 for Nash-Sutcliffe coefficient, 0.13 ms − 1 for Root Mean Square Error, and 0.39 for Index of Agreement) because CA ignores momentum conservation.
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LAKSMI, PUTU IKA OKTIYARI, KOMANG DHARMAWAN, and LUH PUTU IDA HARINI. "PERAMALAN KUNJUNGAN WISATAWAN MENGGUNAKAN MODEL ARMAX DENGAN NILAI KURS DAN EKSPOR-IMPOR SEBAGAI FAKTOR EKSOGEN." E-Jurnal Matematika 3, no. 4 (November 28, 2014): 138. http://dx.doi.org/10.24843/mtk.2014.v03.i04.p076.
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Forecasting is science to estimate occurrence of the future. This matter can be conducted by entangling intake of past data and place to the next period with a mathematical form. This research aims to estimate the number of foreign tourists visiting Bali models using autoregressive moving average exogenous (ARMAX). The data used in this study is the number of tourists in Australia and the number of tourists in the RRC as a variable Y, and foreign currency exchange rate AUD, Chinese Yuan, and Export Import as the X factor from the period July 2009 to July 2014. In the analysis can be obtained in the best ARMAX models of the number of tourists in Australia is ARMAX(1,2,2) and the best model of the number of tourists in the RRC does not exist because the data for the ARMAX model parameters tourists no significant RRC.
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Zhu, D., and I. D. Cluckie. "A preliminary appraisal of Thurnham dual polarisation radar in the context of hydrological modelling structure." Hydrology Research 43, no. 5 (May 3, 2012): 736–52. http://dx.doi.org/10.2166/nh.2012.023.
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The Thurnham radar is a prototype of a potential operational C-Band dual-polarisation weather radar designed specifically for the measurement of rainfall. It is also designed to increase the radar coverage over London when operating as a conventional C-Band radar as a direct consequence of the Lewes floods of October 2000. Dual-polarisation processing is expected to provide improved estimation of rainfall rates, especially at higher intensities, in terms of clutter removal, attenuation correction and rainfall estimation. In this study, three hydrological models with different mathematical structures were selected to evaluate the impact that dual-polarisation technology could have on operational hydrology and recommendations provided on the further development of the dual-polarisation algorithms in the short term. The preliminary appraisal was focused on the Upper Medway Catchment (south of London, UK) using different precipitation inputs, including raingauge measurements, radar rainfall estimates from single-polarised algorithms (cartesian format) and five different dual-polarisation algorithms (polar format). The influence of the different rainfall inputs on the various hydrological models were compared using a extreme flood event to provide an initial evaluation of the performance of the Thurnham radar. Recommendations for applying dual-polarisation radar to real-time flood forecasting are discussed in detail.
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Berdnikov, Sergey V., Liudmila V. Dashkevich, Valerii V. Kulygin, Igor V. Sheverdyaev, I. A. Tretyakova, and Natalia A. Yaitskaya. "EX-MARE - FORECASTING SYSTEM OF NATURAL HAZARDS IN THE AZOV SEA REGION." GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY 11, no. 2 (June 28, 2018): 29–45. http://dx.doi.org/10.24057/2071-9388-2018-11-2-29-45.
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The paper presents approach used for the development of the forecasting system of extreme hydro-meteorological events in the region of the Sea of Azov. Due to numerous dangerous extreme events that occurred in the beginning of XXI century the issue of creation such system has become very relevant and important. The forecasting system, named EX-MARE, was started developing in 2014 as a complex of mathematical models. For each type of hydro-meteorological events, the modeling component was designed. The EX-MARE system is based on a scenario approach implied the consideration a variety of possible futures taking into account the existing uncertainty. Accurate extreme events estimation requires automated monitoring systems and longterm database application. In the paper, the detail description of the system components and the data sources is examined. Three case studies about the sea surges, flash flood and ice conditions researches demonstrate the application of the EX-MARE system and the benefits of its using. Further development of the EX-MARE system assumes adding data on exposure and vulnerability to perform the risk assessment, as well as focusing on multi-hazards exploring methodology.
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Sarker, Chandrama, Luis Mejias, Frederic Maire, and Alan Woodley. "Flood Mapping with Convolutional Neural Networks Using Spatio-Contextual Pixel Information." Remote Sensing 11, no. 19 (October 8, 2019): 2331. http://dx.doi.org/10.3390/rs11192331.
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Remote sensing technology in recent years has been regarded the most important source to provide substantial information for delineating the flooding extent to the disaster management authority. There have been numerous studies proposing mathematical or statistical classification models for flood mapping. However, conventional pixel-wise classifications methods rely on the exact match of the spectral signature to label the target pixel. In this study, we propose a fully convolutional neural networks (F-CNNs) classification model to map the flooding extent from Landsat satellite images. We utilised the spatial information from the neighbouring area of target pixel in classification. A total of 64 different models were generated and trained with a variable neighbourhood size of training samples and number of learnable filters. The training results revealed that the model trained with 3 × 3 neighbourhood sized training samples and with 32 convolutional filters achieved the best performance out of the experiments. A new set of different Landsat images covering flooded areas across Australia were used to evaluate the classification performance of the model. A comparison of our proposed classification model to the conventional support vector machines (SVM) classification model shows that the F-CNNs model was able to detect flooded areas more efficiently than the SVM classification model. For example, the F-CNNs model achieved a maximum precision rate (true positives) of 76.7% compared to 45.27% for SVM classification.
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Royal Yusibov, Tofiq Samedov, Royal Yusibov, Tofiq Samedov. "FORECAST OF HYDROCARBON RATIOS BASED ON EXTRACTED RESERVES." PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions 17, no. 06 (May 18, 2022): 170–74. http://dx.doi.org/10.36962/pahtei17062022-170.
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The article provides an analysis of the forecast of hydrocarbon ratios based on the data extracted. Various methods are used in the technological processes of gas extraction in order to build forecast models of the final gas-condensate yield coefficients. It is important to select the most accurate and reliable models in order to determine the recoverable reserves of gas and condensate fields operated in flood and depletion regimes or to assess the effectiveness of geological and technical measures taken to increase development efficiency. Mathematical modeling methods are widely used for analysis, control of gas and condensate field development and qualitative and quantitative forecasting of its technological parameters. At the same time, long-term operation of gas-condensate fields shows that the application of erolusion modeling methodology, which is one of the most accurate methods for determining the forecast values of recoverable gas reserves for different stages of development, is more appropriate. Keywords: Extracted reserves, hydrocarbon ratios, forecast, regimes, water pressure, gas-condensate fields.
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Om, Dorji, Chompunooch Thamanukornsri, kado, and Montip Tiensuwan. "Application of Box-Jenkins Models to the Tourist Inflow in Bhutan." Journal of Mathematical Sciences & Computational Mathematics 3, no. 1 (October 4, 2021): 13–25. http://dx.doi.org/10.15864/jmscm.3102.
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Bhutan has now increasingly become a popular destination for many international tourists. Tourism in Bhutan is considered as one of the largest foreign earning industries. The number of tourist inflow in the country is increasing year by year. Forecasting is very necessary for administration and tourist agent for creating awareness and planning for the future development. It can also predict the future trends as accurately as possible and helps in staying one step ahead of the competition. This study aims to apply mathematical model for forecasting monthly tourist inflow from Malaysia, Singapore, China, USA, England, France, Germany, Thailand, Australia and Japan to Bhutan. The Box-Jenkins model is used to identify the parameters of Autoregressive integrated moving average (ARIMA) model of monthly tourist visited data of above mentioned countries in the period 2011-2015 obtained from Tourism Council of Bhutan. An Akaike's Information Criterion, Schwartz's Bayesian Criterion and estimate variance of white noise are used throughout to test for the identification of best fit model. Further, the periodogram analysis was used to confirm the seasonal period of the model. The results showed ARIMA model for Thai, Chinese, Malaysian and Japanese, while seasonal ARIMA for American, Australian, British, French, Singaporean and German. Further, seasonal ARIMA model was obtained as the best fit model for the overall data. These models are illustrated and could possibly forecast the monthly tourist inflow of one year ahead with acceptable accuracy.
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Kozytskyi, O. M., S. A. Shevchuk, and I. A. Shevchenko. "MONITORING AND MANAGEMENT OF HYDROLOGICAL RISKS IN THE RIVER BASINS OF UKRAINE." Міжвідомчий тематичний науковий збірник "Меліорація і водне господарство", no. 2 (December 12, 2019): 157–66. http://dx.doi.org/10.31073/mivg201902-206.
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Background of the study. Due to the increasing intensity and frequency of catastrophic floods occurrence, one of the most important tasks of the water management of Ukraine is to increase the efficiency of the existing system of flood protection due to the implementation of integrated flood risk management methods based on the assessment of flood hazard levels requirements according to Directive 2007/60/EC. The development of scientific and methodological bases for the assessment and mapping of flood hazard and risk levels, as well as the development of integrated flood risk management plans based on them, is an important and urgent task in Ukraine as an associated EU member.
The purpose of the work is to highlight the main works results, performed at the Institute on the study of patterns of riverbed transformations, the development of strategies for flood risk management and scientific and methodological support of the assessment and mapping of flood hazard and risk, taking into account the nature and the intensity of river bed transformation and exogenous processes in river basins of Ukraine.
Outline of the main material. Systematic research on flood protection issues and river bed evolution in IWPaLR has been conducted since the middle of the last century.
The problems of the dynamics of river bed’s evolutions, ensuring the stability of dams, erosion of the tail water of dam, development of active hydraulic structures and their arrangement in river beds, forecasting river bed evolution, runoffs, development study of permissible (nondestructive) flow velocities for alluvial soils, taking into account the phenomenon of self-patching of the river bed, the dynamic equilibrium of the beds, the typing of the beds of mountain rivers, etc., were studied and solved under the natural conditions and in the hydraulic laboratory of the Institute. Based on the results of theoretical and experimental studies of river bed evolution, a number of methodological provisions on the complex regulation of channel deformations and safe passage of high floods were formulated and published a number of regulatory and methodological documents on the calculation and forecasting of river bed transformations, designing of dams and protection structures.
An important role was given to the issues of regulation and redistribution of floodwater by the system of river reservoirs and replenishment of groundwater reserves.
The methodological recommendations for sampling of river bed deposits and sediments, on the base of the international ISO standards’ requirements and recommendations of have been developed at the Institute, as well as the method of estimation of the river bed transformation’s dynamics, for the discrete and quantitative assessments of river bed deformations and their intensity.
The paper also highlights the main results of work on the implementation of the Flood Directive 2007/60/EC in Ukraine, in particular, the development of a Flood Risk Management Strategy in the Ukrainian Carpathian River basins. In the Strategy declared the latest approaches to flood response, which foresee the abandonment of the current paradigm of "flood protection" to favor integrated flood risk management. It defines national mechanisms of strategic management in the field of flood risk reduction, directions of transboundary cooperation, coordination of works within river basins. For the future development of this Strategy, the paper presents the scientific and methodological bases for a comprehensive assessment of the total levels of flood hazard and flood risk and their mapping on a GIS basis.
Conclusion. In the future, scientific research on integrated flood risk management should focus on the study of patterns of evolution of river bed and development of mathematical models of regulation of channel deformations, improvement of the flood forecasting and prevention methodology based on simulation modeling, as well as the development new management schemes for runoff ‘s regulation.
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Idier, Déborah, Axel Aurouet, François Bachoc, Audrey Baills, José Betancourt, Fabrice Gamboa, Thierry Klein, et al. "A User-Oriented Local Coastal Flooding Early Warning System Using Metamodelling Techniques." Journal of Marine Science and Engineering 9, no. 11 (October 27, 2021): 1191. http://dx.doi.org/10.3390/jmse9111191.
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Given recent scientific advances, coastal flooding events can be properly modelled. Nevertheless, such models are computationally expensive (requiring many hours), which prevents their use for forecasting and warning. In addition, there is a gap between the model outputs and information actually needed by decision makers. The present work aims to develop and test a method capable of forecasting coastal flood information adapted to users’ needs. The method must be robust and fast and must integrate the complexity of coastal flood processes. The explored solution relies on metamodels, i.e., mathematical functions that precisely and efficiently (within minutes) estimate the results that would provide the numerical model. While the principle of relying on metamodel solutions is not new, the originality of the present work is to tackle and validate the entire process from the identification of user needs to the establishment and validation of the rapid forecast and early warning system (FEWS) while relying on numerical modelling, metamodelling, the development of indicators, and information technologies. The development and validation are performed at the study site of Gâvres (France). This site is subject to wave overtopping, so the numerical phase-resolving SWASH model is used to build the learning dataset required for the metamodel setup. Gaussian process- and random forest classifier-based metamodels are used and post-processed to estimate 14 indicators of interest for FEWS users. These metamodelling and post-processing schemes are implemented in an FEWS prototype, which is employed by local users and exhibits good warning skills during the validation period. Based on this experience, we provide recommendations for the improvement and/or application of this methodology and individual steps to other sites.
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Kholoptsev, Alexander, Sergei Podporin, and Evgeniy OlKhovik. "Impact of Floods in the Kolyma River Delta on Navigation Conditions in the East Siberian Sea." E3S Web of Conferences 363 (2022): 01004. http://dx.doi.org/10.1051/e3sconf/202236301004.
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Problem of improving the quality of medium- and long-term forecasting of changes in ice conditions in the Northern Sea Route, and in particular in the east Siberian sea, where one of the methods of selecting waterways is the passage of ships in areas of ice-covered polynya. The hypothesis is verified that during the summer months, such changes may be significantly influenced by the timing of the onset of high water in the Kolyma River delta. Data from the global reanalysis GLORYS12v1 supported by the European Copernicus Marine Service were used as factual material on the ice cover and levels of the East Siberian Sea in the months of May to October 1993-2019. The analysis is based on mathematical models of the NEMO family verified by satellite altimetry data. Using the developed methodology, the dates of abrupt changes in level and sea ice extent on the Kolyma River estuarine seashore have been estimated for selected periods of the year. The study uses statistical methods to confirm the validity of the stated hypothesis for a number of areas of the East Siberian Sea, through which the shipping routes of the Northern Sea Route pass. It has been established that the greatest influence of flood timing on ice conditions and navigation conditions in such areas takes place in July. It is shown that early floods in the Kolyma delta generally lead to improvement of ice conditions, while late floods lead to complication of ice conditions. The identified relationships are recommended for use in forecasting changes in ice conditions. It has been suggested that with further climate warming and shifting of flood dates to earlier dates, the complication of ice conditions due to freezing of the formed polynya is not excluded.
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PERMINOV, A. V., O. S. ERMOLAEVA, E. V. KUZNETSOVA, and V. V. ILJINICH. "Experience of computer simulation of flood runoff of the Kuban river to the Krasnodar reservoir based on the DWAT model." Prirodoobustrojstvo, no. 4 (2022): 107–13. http://dx.doi.org/10.26897/1997-6011-2022-4-107-113.
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The article is dedicated to PC modeling of the maximum water runoff of the Kuban rivers flowing to the Krasnodar reservoir after the storm rains. A mathematical model of the «precipitation-runoff» type – DWAT was used. This model is recommended by the World Meteorological Organization for use on rivers with flash floods, based on river survey digital elevation models and storm rain forecasts. A possibility of using proposed approach, the estimated possibility of using DWAT for short-term forecasting of flood inflow to the Krasnodar reservoir based on storm precipitation at predictor meteorological stations, which is tentatively assessed based on the analysis of the dependence of the main elements of floods on the storm rain characteristics. In addition to the digital relief model, the model uses georeferenced layers of data on vegetation, land use types and soils of the catchment area. In general, the article shows the process of entering and processing data into the model. The obtained final simulation results, expressed by the forecast hydrograph, are compared with the observed true values. The forecasted flood graphs correspond suffi ciently to the observed ones and in general the ma ximum water discharges of the forecasted floods obtained using the model under study for previous precipitation are for the most part slightly higher than the observed ones, which is partly explained by the spatial data due to map resolution used. The use of more detailed source data and map resolution may improve the final result.
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Kholoptsev, Aleksandr V., and Sergey A. Podporin. "IMPACT OF FLOODS IN THE KOLYMA RIVER DELTA ON NAVIGATION CONDITIONS IN THE EAST SIBERIAN SEA." Vestnik Gosudarstvennogo universiteta morskogo i rechnogo flota imeni admirala S. O. Makarova 14, no. 4 (September 13, 2022): 563–70. http://dx.doi.org/10.21821/2309-5180-2022-14-4-563-570.
Full textAbstract:
The problem of improving the quality of medium- and long-term forecasting of changes in the ice situation on the Northern Sea Route, and in particular in the East Siberian Sea, where one of the methods for choosing waterways is the passage of vessels in the areas of flaw polynya, is considered. The hypothesis that in the summer months such changes can be significantly affected by the terms of floods onset in the Kolyma River Delta is tested. The data of the GLORYS12v1 global reanalysis supported by the Copernicus Marine Service are used as factual material on the ice cover and levels of the East Siberian Sea in the months from May to October of 1993-2019. The reanalysis is based on mathematical models of the NEMO family, verified using altimetry data from satellite measurements. Using the developed methodology for the selected periods of the year, the dates of sharp changes in the level and ice cover on the pre-estuary seaside of the Kolyma River are estimated. Using statistical methods, the validity of the stated hypothesis for a number of the East Siberian Sea areas, along which the shipping lanes of the Northern Sea Route pass, is confirmed. It has been established that the greatest influence of the floods terms on the ice situation and navigation conditions in such areas takes place in July. It has been shown that early floods in the Kolyma delta generally lead to an improvement in the ice situation, and late floods lead to its complication. The identified relationships are recommended for use in forecasting changes in ice conditions. The assumption that with further climate warming and a shift in the flood terms to earlier dates, it is possible that the ice conditions will become more complicated due to the freezing of the formed polynya, is made.
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Ratnayake, Dinesh C., Guna A. Hewa, and David J. Kemp. "Challenges in Quantifying Losses in a Partly Urbanised Catchment: A South Australian Case Study." Water 14, no. 8 (April 18, 2022): 1313. http://dx.doi.org/10.3390/w14081313.
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Quantifying hydrological losses in a catchment is crucial for developing an effective flood forecasting system and estimating design floods. This can be a complicated and challenging task when the catchment is urbanised as the interaction of pervious and impervious (both directly connected and indirectly connected) areas makes responses to rainfall hard to predict. This paper presents the challenges faced in estimating initial losses (IL) and proportional losses (PL) of the partly urbanised Brownhill Creek catchment in South Australia. The loss components were calculated for 57 runoff generating rainfall events using the non-parametric IL-PL method and parametric method based on two runoff routing models, Runoff Routing Burroughs (RORB) and Rainfall-Runoff Routing (RRR). The analysis showed that the RORB model provided the most representative median IL and PL for the rural portion of the study area as 9 mm and 0.81, respectively. However, none of the methods can provide a reliable loss value for the urban portion because there is no runoff contribution from unconnected areas for each event. However, the estimated non-parametric IL of 1.37 mm can be considered as IL of EIA of the urban portion. Several challenges were identified in the loss estimation process, mainly when selecting appropriate storm events, collecting data with the available temporal resolution, extracting baseflow, and determining the main-stream transmission losses, which reduced the urban flow by 5.7%. The effect of hydrograph shape in non-parametric loss estimation and how combined runoff from the effective impervious area and unconnected (combined indirectly connected impervious and pervious) areas affects the loss estimation process using the RORB and RRR models are further discussed. We also demonstrate the importance of identifying the catchment specific conditions appropriately when quantifying baseflow and runoff of selected events for loss estimation.
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Panant, Charoen, and Samakkee Boonyawat. "A Simplified Rainfall-Streamflow Network Model on Multivariate Regression Analysis for Water Level Forecasting in Klong Luang (KGT.19 Station) Sub-watershed, Chon Buri Province, Thailand." Applied Environmental Research, October 17, 2014, 53–65. http://dx.doi.org/10.35762/aer.2014.36.4.6.
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A simplifiedrainfall-streamflownetwork model based on multivariate linear regression (MLR) analysishas been proposed. To determine significant coefficients of streamflow network, eleven MLR models were examined. The study’s three objectives were 1) to develop a novel a mathematical model based on MLR analysis for forecasting optimal water levels;2) to determine the most significant coefficient of rainfall-streamflow network among in the area of interest in the vicinity of Klong Luang sub-watershedKGT.19 station; and3) to apply the optimal MLR model forwater level andflood forecasting mapsin Klong Luang Sub-watershed. We used Geographic Information System (GIS) and Remotely Sensed Data (RS) data recorded from Klong Luang (KGT.19 Station) sub-watershed, and Phanat Nikhom, Chonburi, Ban Bueng and Phan Thong districts, in Chonburi Province, Thailand.The findings indicated that the MLR based Model No. 8 is the most applicable and effective. The proposed model also could be applied in water level forecasting, water resource management, flood hazard planning, and flood early warning.
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Khastagir, Anirban, Iqbal Hossain, and A. H. M. Faisal Anwar. "Efficacy of linear multiple regression and artificial neural network for long-term rainfall forecasting in Western Australia." Meteorology and Atmospheric Physics 134, no. 4 (July 11, 2022). http://dx.doi.org/10.1007/s00703-022-00907-4.
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AbstractPrecipitation is one of the most intrinsic resources for manifold industrial activities all over Western Australia; consequently, immaculate rainfall prediction is indispensable for flood mitigation as well as water resources management. This study investigated the performance of artificial neural networks (ANN) and Linear multiple regression (LMR) analysis to forecast long-term seasonal spring rainfall in Western Australia, using lagged El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as potential climatic phenomena. The ANN was developed in the form of multilayer perceptron using Levenberg–Marquardt algorithm and subsequently LMR was used with statistical significance for future spring rainfall forecast. The total climatic dataset has been divided into calibration and testing phases to determine the efficacy of the developed models. Different statistical skill tests such as root mean square error (RMSE), mean absolute error (MAE), and Willmott index of agreement ‘d’ were used to assess the efficacy of LMR and ANN modelling. In general, LMR has lower MAE and RMSE values as compared to ANN for most of the stations during calibration and testing periods, whereas ANN models performed better than LMR models based on ‘d’ values. The overall statistical analysis paradigm suggests the efficacy of LMR over ANN models for rainfall forecasting using more climatic variables. As a result, the developed LMR model, incorporated with lagged global climate indices, will facilitate the adequate preparedness for the risks associated with potential droughts in the study region.