An inverse method for watershed change detection using hybrid conceptual and artificial intelligence approaches

Land Use and Land Cover (LULC) changes intrinsically lead to various hydrological impacts especially on the outflow rate of the watersheds. This research investigated LULC changes and its effect on outlet runoff by detecting LULC changes location and severity via an inverse method for the Little River Watershed, USA. For this purpose, the Clark's conceptual Rainfall-Runoff model was applied to the delineated sub-watersheds of the basin to generate different outflows by altering the Storage Coefficient (SC) of the model for each sub-watershed as the representative of LULC. Then, the relation between SC values and outflow time series was simulated by Artificial Intelligence (AI) based models of artificial neural network and least square support vector machine. In this way, in order to ignore redundant information and reduce the dimension of input vector, Wavelet-Entropy (WE) values of the outflow sub-series were computed and used as the inputs of the AI model to compute SCs of the sub-watersheds as the outputs. The trained multi-output AI model as an inverse method could be then used to predict the SC values (as representative of LULC) of the sub-watersheds using the observed time series of runoff at the outlet. The obtained results showed that the proposed inverse method could reliably detect not only the location but also the severity of LULC changes by prediction of SC values in the coming future years. For validation of the method, a comparison was also performed between the obtained results and recorded changes via normalized difference vegetation index (NDVI) and land use classification, extracted from Landsat images. The comparison approved the ability of the proposed method for LULC change detection in a way that deforestation and cropland increasing of the sub-watersheds from 1990 to 2013 were aligned with the SC reduction e.g., 26% decrease of SC for downstream sub-watershed versus 53% decrease and 21% increase of forest and crop lands, respectively.