Landslide hazard risk modeling in north-west of Iran using optimized machine learning models

Abstract

Landslide is one of the most destructive natural hazards threatening the life of people and their properties. Mountainous areas are at a high risk of landslide occurrence. One-third of Iran is mountainous areas where landslide susceptibility modeling is required for sustainable land development. In this research for landslide susceptibility modeling, several supervised machine learning models, including Random Forest, eXtreme Gradient Boosting, the Salp Swarm Algorithm Multi-layer Perceptron (SSAMLP), and the Multi-verse Optimization Multi-layer Perceptron (MVOMLP), are developed in R and MATLAB programming languages. The Random Forest and eXtreme Gradient Boosting models are optimized based on their hyper-parameters in the R programming language using the caret library. In this research, landslide susceptibility models divide the study area into very low (class 1), low (class 2), moderate (class 3), high (class 4), and very high (class 5) landslide susceptibility zones. Results show that the eXtreme Gradient Boosting model has the best performance with values of 0.00225 and 0.00805 for the Mean Absolute Error and Root Mean Squared Error indices, respectively, for the training dataset. For the test dataset, the eXtreme Gradient Boosting model with the values of 0.0599 for the Mean Absolute Error, respectively, outperformed the Random Forest and the MVOMLP and the SSAMLP models. In terms of Mean Absolute Error, the SSAMLP algorithm has the best performance over the other three supervised models with a value of 0.1472.