Landslide susceptibility prediction using artificial neural networks, SVMs and random forest: hyperparameters tuning by genetic optimization algorithm

This paper evaluates a comparison between three machine learning algorithms (MLAs), namely support vector machine (SVM), multilayer perceptron artificial neural network (MLP-ANN) and random forest (RF), in landslide susceptibility mapping and addresses a optimization algorithm to optimize the performance of a MLA to yield more accurate and reliable results. A genetic algorithm (GA) approach as a part of evolutionary algorithms was utilized in order to optimize the performance of best model among three utilized MLAs. The study area (Tarom-Khalkhal sub-basin) is located in North-West of Iran with mountainous nature (western part of Alborz Mountains), wherein numerous landslide occurrences were recorded. In this case, fifteen predisposing factors, gathered from aerial images and field surveys, were considered to generate the final landslide susceptibility models. The validation procedure was conducted with taking advantage of confusion matrices for different algorithms. Finally, landslide susceptibility maps were generated and evaluated through receiver operator characteristic (ROC) curves. RF algorithm showed the best performance; therefore, hybridized genetic random forest (GRF) was employed in order to optimize the hyperparameters (number of trees, number splits and depth) of the model, which can affect the performance of model. As a result, GRF has best performance among all mentioned algorithms with AUC = 0.93. As a conclusion, genetic algorithm was found to be suitable in optimizing the performance of machine learning algorithms, which is crucial when it comes to landslide susceptibility mapping.