Hydrological forecasting plays an important role in water resource management, supporting socio-economic development and managing water-related risks in river basins. There are many flow forecasting techniques that ha...Hydrological forecasting plays an important role in water resource management, supporting socio-economic development and managing water-related risks in river basins. There are many flow forecasting techniques that have been developed several centuries ago, ranging from physical models, physics-based models, conceptual models, and data-driven models. Recently, Artificial Intelligence (AI) has become an advanced technique applied as an effective data-driven model in hydrological forecasting. The main advantage of these models is that they give results with compatible accuracy, and require short computation time, thus increasing forecasting time and reducing human and financial effort. This study evaluates the applicability of machine learning and deep learning in Hanoi water level forecasting where it is controlled for flood management and water supply in the Red River Delta, Vietnam. Accordingly, SANN (machine learning algorithm) and LSTM (deep learning algorithm) were tested and compared with a Physics-Based Model (PBM) for the Red River Delta. The results show that SANN and LSTM give high accuracy. The R-squared coefficient is greater than 0.8, the mean squared error (MSE) is less than 20 cm, the correlation coefficient of the forecast hydrology is greater than 0.9 and the level of assurance of the forecast plan ranges from 80% to 90% in both cases. In addition, the calculation time is much reduced compared to the requirement of PBM, which is its limitation in hydrological forecasting for large river basins such as the Red River in Vietnam. Therefore, SANN and LSTM are expected to help increase lead time, thereby supporting water resource management for sustainable development and management of water-related risks in the Red River Delta.展开更多
The currently used hydrological forecast system in China is mainly focused on flood,and the flood forecasting frameworks are typically based on point discharge measurements and predictions at discrete locations,hence ...The currently used hydrological forecast system in China is mainly focused on flood,and the flood forecasting frameworks are typically based on point discharge measurements and predictions at discrete locations,hence they can’t provide spatio-temporal information of various hydrological elements,such as surface runoff,soil moisture,ground water table,and flood inundation extents over large scales and at high spatial resolutions.The use of distributed hydrological model has recently appeared to be the most suitable option to bridge this gap.An open source GIS-based distributed hydrological forecast system was established recently,and the watershed delineation and hydrological modelling were integrated together seamlessly.The time and human consuming work of processing the spatial data in building distributed hydrological model could be reduced significantly,and the spatial distribution of hydrological information could be quickly simulated and predicted using this system.The system was applied successfully to forecast the flood caused by super strong typhoon"Mangkhut"which attacked the south China in2018.展开更多
文摘Hydrological forecasting plays an important role in water resource management, supporting socio-economic development and managing water-related risks in river basins. There are many flow forecasting techniques that have been developed several centuries ago, ranging from physical models, physics-based models, conceptual models, and data-driven models. Recently, Artificial Intelligence (AI) has become an advanced technique applied as an effective data-driven model in hydrological forecasting. The main advantage of these models is that they give results with compatible accuracy, and require short computation time, thus increasing forecasting time and reducing human and financial effort. This study evaluates the applicability of machine learning and deep learning in Hanoi water level forecasting where it is controlled for flood management and water supply in the Red River Delta, Vietnam. Accordingly, SANN (machine learning algorithm) and LSTM (deep learning algorithm) were tested and compared with a Physics-Based Model (PBM) for the Red River Delta. The results show that SANN and LSTM give high accuracy. The R-squared coefficient is greater than 0.8, the mean squared error (MSE) is less than 20 cm, the correlation coefficient of the forecast hydrology is greater than 0.9 and the level of assurance of the forecast plan ranges from 80% to 90% in both cases. In addition, the calculation time is much reduced compared to the requirement of PBM, which is its limitation in hydrological forecasting for large river basins such as the Red River in Vietnam. Therefore, SANN and LSTM are expected to help increase lead time, thereby supporting water resource management for sustainable development and management of water-related risks in the Red River Delta.
基金supported by the National Key Research and Development Program of China(Grant No.2018YFC1508100)
文摘The currently used hydrological forecast system in China is mainly focused on flood,and the flood forecasting frameworks are typically based on point discharge measurements and predictions at discrete locations,hence they can’t provide spatio-temporal information of various hydrological elements,such as surface runoff,soil moisture,ground water table,and flood inundation extents over large scales and at high spatial resolutions.The use of distributed hydrological model has recently appeared to be the most suitable option to bridge this gap.An open source GIS-based distributed hydrological forecast system was established recently,and the watershed delineation and hydrological modelling were integrated together seamlessly.The time and human consuming work of processing the spatial data in building distributed hydrological model could be reduced significantly,and the spatial distribution of hydrological information could be quickly simulated and predicted using this system.The system was applied successfully to forecast the flood caused by super strong typhoon"Mangkhut"which attacked the south China in2018.
