This research aims to forecast, using various criteria, the flow of soil erosion that will occur at a particular geographical location. On 80% of the dataset from the sample sites, an algorithm combining heap-based optimizer (HBO), political optimizer (PO), teaching-learning based optimization (TLBO), and backtracking search algorithm (BSA) with artificial neural network (ANN) was used to create a flow erosion susceptibility model and establishes a unique and original approach. After it was confirmed to be successful, the algorithms were applied to create a susceptibility map for this area, demonstrating the integrity of the results. The AUC values were computed for every optimization algorithm used in this study. The optimal estimated accuracy indices for populations of 450 were determined to be 0.9846 using the BSA-MLP training databases. The maximum AUC value for the HBO-MLP training databases with different swarm sizes was 0.9736. A swarm size of 350–300 is considered optimal for forecasting erosion susceptibility mapping in hybrid models. With the same swarm size constraints, the AUC value for training in the TLBO-MLP scenario was 0.996. After 150 swarm size conditions were used to train the PO-MLP model, the AUC values were 0.9845. According to these findings, the TLBO-MLP and PO-MLP algorithms worked best with populations of 50 and 150, respectively. Additional study findings demonstrated that the optimization strategies employed here could enhance the neural network's performance in creating the flow soil erosion map.
