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Construction of check dams is one of the important measures in controlling soil erosion and sediment entering the reservoirs of dams. These structures, which provide water reduction and sedimentation by modifying the slope of the canal, play a significant role in preventing the transfer of sediment and pollutants to dam reservoirs, and water bodies. The most challenging task, however, is to identify suitable sites for check dams. In the present study, four machine learning algorithms (namely K-nearest neighbour (KNN), extreme gradient boosting (XGBoost), extremely randomized tree (ERT), and random forest (RF)) and high-resolution remote sensing data were used to find the optimal locations of check dams in the Kandolus watershed in Mazandaran Province (Iran). Sixteen topographical, hydrological, and geomorphic factors were used as inputs in the abovementioned machine learning approaches to identify appropriate locations of check dams. The models were evaluated using receiver operating characteristics (ROC) statistical analyses. The results showed that the RF, ERT, XGB, and KNN models could accurately identify the suitable locations of check dams with the area under curve (AUC) values of 0.93, 0.92, 0.83, and 0.82, respectively. According to the AUC values, RF had the highest accuracy to identify the suitable locations for check dams. This study demonstrate that the utilized artificial intelligence methods and remote sensing data can help land-use planners and water resource managers identify optimal locations of check dams to more efficiency control floods, reduce erosion and land degradation, and enhance groundwater recharge.

geocarto international

Using computational-intelligence algorithms and remote sensing data to optimize the locations of check dams to control sediment and runoff in Kandolus watershed, Mazandaran, Iran