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• W3037111239 endingPage "125206" @default.
• W3037111239 startingPage "125206" @default.
• W3037111239 abstract "Abstract Physically distributed hydrological models are effective in hydrological simulations of large river basins, but the complex characteristics of hydrological features limit their application. An easy-to-use and high-efficiency hydrological model is needed for efficient water resource management in practice. Machine learning (ML) based models have the potential to provide fast mapping pathways between meteorological predictors and hydrological responses without detailed descriptions of the corresponding physical processes. However, the extensive data requirements, ignoring of spatial variability and poor performance for extreme flows limit the application of ML models. This study attempts to develop an ML-based hydrological model by combining physically based distributed hydrological model with an artificial neural networks (ANN), computer vision (CV) and a categorization approach (CA). To solve the insufficient training problem, we use a physically distributed hydrological model (GBHM) together with a stochastic rainfall generator to generate a large amount of synthetic data (GBHM-ANN). To improve the extreme flow simulation, we add the categorization approach into GBHM-ANN (GBHM-ANN-CA). To capture the spatial variability of the predictors, we also use a local binary pattern-based computer vision method to form GBHM-ANN-CA-CV model. The effectiveness of the three modeling approaches are demonstrated by synthetic case studies. We finally evaluate GBHM-ANN-CA-CV using the real data from the upper Chao Phraya Basin in Thailand. The results show that the prediction accuracy of our new data-driven model is greatly improved in data-limited watersheds. Specifically, the CV extracted spatial information can improve the robustness of the data-driven hydrological model, and the CA can greatly improve high flow simulations. The combined model yields a satisfactory accuracy for long-term daily streamflow simulations. This study demonstrates the potential of ML-based hydrological models in water resource management, especially in changing environments." @default.
• W3037111239 created "2020-07-02" @default.
• W3037111239 creator A5016961360 @default.
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• W3037111239 date "2020-11-01" @default.
• W3037111239 modified "2023-10-14" @default.
• W3037111239 title "A physical process and machine learning combined hydrological model for daily streamflow simulations of large watersheds with limited observation data" @default.
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• W3037111239 cites W1589475696 @default.
• W3037111239 cites W1619499024 @default.
• W3037111239 cites W1936477987 @default.
• W3037111239 cites W1939438503 @default.
• W3037111239 cites W1968125527 @default.
• W3037111239 cites W1972596327 @default.
• W3037111239 cites W1979282872 @default.
• W3037111239 cites W1989259839 @default.
• W3037111239 cites W1996647721 @default.
• W3037111239 cites W1996782572 @default.
• W3037111239 cites W2001737606 @default.
• W3037111239 cites W2009003962 @default.
• W3037111239 cites W2013871756 @default.
• W3037111239 cites W2014718495 @default.
• W3037111239 cites W2031292142 @default.
• W3037111239 cites W2033904036 @default.
• W3037111239 cites W2039051707 @default.
• W3037111239 cites W2058998445 @default.
• W3037111239 cites W2073491504 @default.
• W3037111239 cites W2082484980 @default.
• W3037111239 cites W2084696946 @default.
• W3037111239 cites W2090137585 @default.
• W3037111239 cites W2093328831 @default.
• W3037111239 cites W2114358147 @default.
• W3037111239 cites W2116992828 @default.
• W3037111239 cites W2119132330 @default.
• W3037111239 cites W2126356715 @default.
• W3037111239 cites W2143593953 @default.
• W3037111239 cites W2144968266 @default.
• W3037111239 cites W2153855662 @default.
• W3037111239 cites W2156599833 @default.
• W3037111239 cites W2160773748 @default.
• W3037111239 cites W2162719469 @default.
• W3037111239 cites W2162915993 @default.
• W3037111239 cites W2166258979 @default.
• W3037111239 cites W2169886917 @default.
• W3037111239 cites W2169915027 @default.
• W3037111239 cites W2172196609 @default.
• W3037111239 cites W2257795092 @default.
• W3037111239 cites W2286565014 @default.
• W3037111239 cites W2300217830 @default.
• W3037111239 cites W2302515489 @default.
• W3037111239 cites W2307724647 @default.
• W3037111239 cites W2343754209 @default.
• W3037111239 cites W2441507532 @default.
• W3037111239 cites W2605739831 @default.
• W3037111239 cites W2728702994 @default.
• W3037111239 cites W2734256217 @default.
• W3037111239 cites W2745003183 @default.
• W3037111239 cites W2793545812 @default.
• W3037111239 cites W2794068821 @default.
• W3037111239 cites W2799422346 @default.
• W3037111239 cites W2800819102 @default.
• W3037111239 cites W2815792770 @default.
• W3037111239 cites W2893656991 @default.
• W3037111239 cites W2897429783 @default.
• W3037111239 cites W2900466252 @default.
• W3037111239 cites W2905485021 @default.
• W3037111239 cites W2928248204 @default.
• W3037111239 cites W2947721686 @default.
• W3037111239 cites W2950905533 @default.
• W3037111239 cites W2952317511 @default.
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• W3037111239 cites W2969297848 @default.
• W3037111239 cites W2979753402 @default.
• W3037111239 cites W2985931611 @default.
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• W3037111239 doi "https://doi.org/10.1016/j.jhydrol.2020.125206" @default.
• W3037111239 hasPublicationYear "2020" @default.
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