Enhancing hydrological model efficiency through satellite image classification

This paper aims to evaluate the performance of a hydrological model by using satellite image classification (SIM) to extract land use (LU) information. Four methods, namely naive Bayes (NB), classification and regression trees (CART), support vector machine (SVM), and random forest (RF), are assessed for SIM using satellite images. CART demonstrated the highest overall accuracy (OA) of 0.996, followed by RF (OA = 0.994), SVM (OA = 0.797), and NB (OA = 0.543). The Soil and Water Assessment Tool (SWAT) model was employed in this study to construct and refine the hydrological model for simulating the streamflow in the basin. For this purpose, the ground-based layer was replaced by LU generated from the classification algorithms. Integrating the four classification approaches yielded a significant improvement in Nash-Sutcliffe efficiency of the SWAT model, increasing it from 0.37 to approximately 0.76. These findings highlight the effectiveness of using satellite image classification in enhancing the efficiency of hydrological models.