Evaluation of Soil and Water Assessment Tool and Artificial Neural Network models for hydrologic simulation in different climatic regions of Asia

In this study, a physically-based hydrological model, Soil and Water Assessment Tool (SWAT) and three types of Artificial Neural Network (ANN) models were used to simulate daily streamflow, and results were compared with observed data for performance analysis. The study was carried out in three different river basins with three different climatic characteristics, namely the West-Seti River Basin in a sub-tropical (partially wet) climatic region, Sre Pok River Basin in a tropical (wet) climatic region and Hari Rod River Basin in a semi-arid (dry) climatic region. The SWAT and ANN models were evaluated using statistical indicators such as the correlation coefficient (R²), Nash-Sutcliffe efficiency (NSE), and percentage bias (PBIAS). The performance of ANN models was found to be very good with both R² and NSE values greater than 0.95 for the training and validation periods in the West-Seti River Basin and Sre Pok River Basin. Whereas, in the Hari Rod River Basin, the performance of the SWAT model was good with both R² and NSE values greater than 0.60 for the calibration and validation periods. Moreover, the performance of SWAT and ANN models was evaluated based on hydrological indicators (i.e. annual discharge, base flow, Q_dry, and Q_wet), during different flow periods (very high to very low flow) using flow duration curves (FDCs). The SWAT model was found to be better for low flow simulation and the ANN model performed better for high flow simulation in the three river basins.