Comparison of Machine Learning Models Performance on Simulating Reservoir Outflow: A Case Study of Two Reservoirs in Illinois, U.S.A.

摘要

Reservoir outflow is an important variable for understanding hydrological processes and waterresource management. Natural streamflow variation, in addition to the streamflow regulation provided by damsand reservoirs, can make streamflow difficult to understand and predict. This makes them a challenge to accuratelysimulate hydrologic processes at a daily scale. In this study, three Machine Learning (ML) algorithms,Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were examined andcompared to model reservoir outflow. Past, current, and future hydrologic and meteorological data were used asmodel inputs, and the outflow of next day was used as prediction. Simulation results demonstrated that all threemodels can reasonably simulate reservoir outflow. For Carlyle Lake, the coefficient of determination and Nash–Sutcliffe efficiency were each close to one for the three models. The coefficient of determination, relative meanbias, and root mean square error indicated that the SVM performed better than the RF and ANN, but the SVMoutput displayed a larger relative mean bias than that from RF and ANN. For Lake Shelbyville, the ANN modelperformed better than RF and SVM when considering the coefficient of determination, Nash–Sutcliffe efficiency,relative mean bias, and root mean square error. The study results demonstrate that the three ML algorithms(RF, SVM, and ANN) are all promising tools for simulating reservoir outflow. Both the accuracy and efficacy ofthe three ML algorithms are considered to support practitioners in planning reservoir management.