Intercomparison of SWAT and ANN techniques in simulating streamflows in the Astore Basin of the Upper Indus

Abstract

The current research work was carried out to simulate monthly streamflow historical record using Soil and Water Assessment Tool (SWAT) and Artificial Neural Network (ANN) at the Astore Basin, Gilgit-Baltistan, Pakistan. The performance of SWAT and ANN models was assessed during calibration (1985–2005) and validation (2006–2010) periods via statistical indicators such as coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), and root-mean-square error (RMSE). R2, NSE, PBIAS, and RMSE values for SWAT (ANN with Architecture (2,27,1)) models during calibration are 0.80 (0.88), 0.73 (0.82), 15.7 (0.008), and 79.81 (70.34), respectively, while during validation, the corresponding values are 0.71 (0.86), 0.66 (0.95), 17.3 (0.10), and 106.26 (75.92). The results implied that the ANN model is superior to the SWAT model based on the statistical performance indicators. The SWAT results demonstrated an underestimation of the high flow and overestimation of the low flow. Comparatively, the ANN model performed very well in estimating the general and extreme flow conditions. The findings of this research highlighted its potential as a valuable tool for accurate streamflow forecasting and decision-making. The current study recommends that additional machine learning models may be compared with the SWAT model output to improve monthly streamflow predictions in the Astore Basin. HIGHLIGHTS SWAT and ANN model performance was assessed via statistical performance indicators.; Compromise programming was used to rank ANN architectures.; ANN architecture (2,27,1) outperformed SWAT model.; SWAT model exhibits limitations in accurately estimating high and low flows.; ANN model excels in predicting both general and extreme flow scenarios.;

Keywords

• ann
• astore basin
• machine learning
• streamflow
• swat