Assessing landslide susceptibility using machine learning models: a comparison between ANN, ANFIS, and ANFIS-ICA

The present study is aimed at conducting a comparative landslide susceptibility assessment in a landslide-prone subset area of the Tajan Watershed in northern Iran. For this aim, three probabilistic models were used namely: multilayer perceptron Artificial Neural Networks with a Back-Propagation algorithm (BPANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and the coupled ANFIS-Imperialist Competitive Algorithm (ANFIS-ICA). An all-inclusive landslide inventory map was prepared together with ten pivotal geo-environmental and anthropogenic landslide-controlling factors. Three indices including Pierce Skill Score (PSS), Cohen’s kappa, and the Area Under the Receiver Operating Characteristic curve (AUROC) were calculated from the confusion matrix and used to assess the performance of the models. Results in the validation stage revealed that the ensemble of ANFIS-ICA outperformed its counterparts with the respective PSS, kappa, and AUROC values of 0.766, 0.792, 0.966, followed by ANFIS (0.629, 0.666, 0.902), and ANN (0.603, 0.652, 0.866). Regarding the superior model (ANFIS-ICA), about 27% of the study area falls within high landslide susceptibility zones which needs to be considered for further risk mitigation measures and pragmatic actions. Our results pinpointed the outstanding performance of ANFIS-ICA ensemble in landslide susceptibility modeling at the watershed scale. Furthermore, it was found that the ANFIS-ICA model borrows most of its susceptibility pattern and performance from the distance to roads factor, although the total performance of the model is derived from the integration of all the factors. Moreover, this study attested to the advantages of hybrid algorithms and showed that the integration of machine learning models with evolutionary algorithms can be a new horizon to ensemble modeling. Utilization of well-adjusted ensemble models is pivotal for natural resource managers due particularly to their enhanced prediction powers which, in turn, can significantly reduce the social-economic losses emanated from failed predictions.