Machine learning(ML)is becoming an ever more important tool in hydrologic modeling.Previous studies have shown the higher prediction accuracy of those ML models over traditional process-based ones.However,there is ano...Machine learning(ML)is becoming an ever more important tool in hydrologic modeling.Previous studies have shown the higher prediction accuracy of those ML models over traditional process-based ones.However,there is another advantage of ML which is its lower computational demand.This is important for the applications such as hydraulic soil erosion estimation over a large area and at a finer spatial scale.Using traditional models like Rangeland Hydrology and Erosion Model(RHEM)requires too much computation time and resources.In this study,we designed an Artificial Neural Network that is able to recreate the RHEM outputs(annual average runoff,soil loss,and sediment yield and not the daily storm event-based values)with high accuracy(Nash-Sutcliffe Efficiency≈1.0)and a very low computational time(13 billion times faster on average using a GPU).We ran the RHEM for more than a million synthetic scenarios and train the Emulator with them.We also,fine-tuned the trained Emulator with the RHEM runs of the real-world scenarios(more than 32,000)so the Emulator remains comprehensive while it works specifically accurately for the real-world cases.We also showed that the sensitivity of the Emulator to the input variables is similar to the RHEM and it can effectively capture the changes in the RHEM outputs when an input variable varies.Finally,the dynamic prediction behavior of the Emulator is statistically similar to the RHEM.展开更多
基金supported by the U.S.Department of Agriculture,Natural Resources Conservation Service,Conservation Effects Assessment Project(CEAP)Grazing Lands Component,under agreement number NR193A750007C002。
文摘Machine learning(ML)is becoming an ever more important tool in hydrologic modeling.Previous studies have shown the higher prediction accuracy of those ML models over traditional process-based ones.However,there is another advantage of ML which is its lower computational demand.This is important for the applications such as hydraulic soil erosion estimation over a large area and at a finer spatial scale.Using traditional models like Rangeland Hydrology and Erosion Model(RHEM)requires too much computation time and resources.In this study,we designed an Artificial Neural Network that is able to recreate the RHEM outputs(annual average runoff,soil loss,and sediment yield and not the daily storm event-based values)with high accuracy(Nash-Sutcliffe Efficiency≈1.0)and a very low computational time(13 billion times faster on average using a GPU).We ran the RHEM for more than a million synthetic scenarios and train the Emulator with them.We also,fine-tuned the trained Emulator with the RHEM runs of the real-world scenarios(more than 32,000)so the Emulator remains comprehensive while it works specifically accurately for the real-world cases.We also showed that the sensitivity of the Emulator to the input variables is similar to the RHEM and it can effectively capture the changes in the RHEM outputs when an input variable varies.Finally,the dynamic prediction behavior of the Emulator is statistically similar to the RHEM.
