Optimized model inputs selections for enhancing river streamflow forecasting accuracy using different artificial intelligence techniques
Document Type
Article
Publication Date
12-1-2022
Abstract
The development of a river inflow prediction is a prerequisite for dam reservoir management. Precise forecasting leads to better irrigation water management, reservoir operation refinement, enhanced hydropower output and mitigation of risk of natural hazards such as flooding. Dam created reservoirs prove to be an essential source of water in arid and semi-arid regions. Over the years, Artificial Intelligence (AI) has been used for development of models for prediction of various natural variables in different engineering fields. Also, several AI models have been proved to be beneficial over the conventional models in efficient prediction of various natural variables. In this study, four AI models, namely, Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF) and Boosted Tree Regression (BTR) were developed and trained over 130-years of monthly historical rainfall data to forecast streamflow at Aswan High Dam, Egypt. The input parameters were selected according to the Autocorrelation Function (ACF) plot. The findings revealed that RF model outperformed other techniques and could provide precise monthly streamflow prediction with the lowest RMSE (2.2395) and maximum WI (0.998462), R-2 (0.9012). The input combination for the optimum RF model was Q(t-1), Q(t-11), and Q(t-12) (i.e., one-, eleven- and twelve-months delay inputs). The optimum RF model provides a reliable source of data for inflow predictions, which allows improved utilization of water resources and long-term water resource planning and management.
Keywords
Streamflow prediction, Aswan High Dam, Artificial Neural Network, Support Vector Machine, Random Forest, Boosted Tree Regression
Divisions
sch_civ
Publication Title
Water Resources Management
Volume
36
Issue
15
Publisher
Springer Verlag
Publisher Location
VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS