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Saeed Samadianfard, Salar Jarhan, Ely Salwana, Amir Mosavi, Shahaboddin Shamshirband and Shatirah Akib
Advancement in river flow prediction systems can greatly empower the operational river management to make better decisions, practices, and policies. Machine learning methods recently have shown promising results in building accurate models for river flow...
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Yen-Chang Chen, Hui-Chung Yeh, Su-Pai Kao, Chiang Wei and Pei-Yi Su
In this study, a novel model that performs ensemble empirical mode decomposition (EEMD) and stepwise regression was developed to forecast the water level of a tidal river. Unlike more complex hydrological models, the main advantage of the proposed model ...
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Mahshid Khazaeiathar, Reza Hadizadeh, Nasrin Fathollahzadeh Attar and Britta Schmalz
The behavior of hydrological processes is periodic and stochastic due to the influence of climatic factors. Therefore, it is crucial to develop the models based on their periodicity and stochastic nature for prediction. Furthermore, forecasting the strea...
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Sergei Borsch, Yuri Simonov, Andrei Khristoforov, Natalia Semenova, Valeria Koliy, Ekaterina Ryseva, Vladimir Krovotyntsev and Victoria Derugina
This paper presents a method of hydrograph extrapolation, intended for simple and efficient streamflow forecasting with up to 10 days lead time. The forecast of discharges or water levels is expressed by a linear formula depending on their values on the ...
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Ganesh R. Ghimire, Sanjib Sharma, Jeeban Panthi, Rocky Talchabhadel, Binod Parajuli, Piyush Dahal and Rupesh Baniya
Improving decision-making in various areas of water policy and management (e.g., flood and drought preparedness, reservoir operation and hydropower generation) requires skillful streamflow forecasts. Despite the recent advances in hydrometeorological pre...
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Cristian Guevara Ochoa, Ignacio Masson, Georgina Cazenave, Luis Vives and Gabriel Vázquez Amábile
Due to the socioeconomical impact of water extremes in plain areas, there is a considerable demand for suitable strategies aiding in the management of water resources and rainfed crops. Numerical models allow for the modelling of water extremes and their...
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Ye Tian, Yue-Ping Xu, Zongliang Yang, Guoqing Wang and Qian Zhu
This study applied a GR4J model in the Xiangjiang and Qujiang River basins for rainfall-runoff simulation. Four recurrent neural networks (RNNs)?the Elman recurrent neural network (ERNN), echo state network (ESN), nonlinear autoregressive exogenous input...
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Konrad Bogner, Katharina Liechti and Massimiliano Zappa
Post-processing has received much attention during the last couple of years within the hydrological community, and many different methods have been developed and tested, especially in the field of flood forecasting. Apart from the different meanings of t...
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