Multilayer Perceptron and Their Comparison with Two Nature-Inspired Hybrid Techniques of Biogeography-Based Optimization (BBO) and Backtracking Search Algorithm (BSA) for Assessment of Landslide Susceptibility

Regarding evaluating disaster risks in Iran’s West Kurdistan area, the multi-layer perceptron (MLP) neural network was upgraded with two novel techniques: backtracking search algorithm (BSA) and biogeography-based optimization (BBO). Utilizing 16 landslide conditioning elements such as elevation (aspect), plan (curve), profile (curvature), geology, NDVI (land use), slope (degree), stream power index (SPI), topographic wetness (TWI), rainfall, sediment transport (STI), 504 landslides target variables, a large geographic database is constructed. Applying techniques mentioned above to synthesis of MLP results suggested BBO-MLP BSA-MLP ensembles. As accuracy standards, we benefit from mean absolute error, square area under receiving operating characteristic curve assess utilized models, have also designed scoring system. The MLP’s increases thanks application BBO BSA algorithms. Comparing BSA, find that former achieves higher average ranks (20, 15, 14). A further finding showed superior at maximizing MLP.