An optimal search for neural network parameters using the Salp swarm optimization algorithm: a landslide application

Abstract

ABSTRACTThis study aims at investigating the balance between exploration and exploitation search capability of a newly developed Salp swarm optimization algorithm (SSA) for fine-tuning parameters of a three-hidden-layer neural network. The landslide study was selected as a thematic application, and a mountainous area of Vietnam was chosen as a case study. A training dataset with thirteen predictor variables and historical landslide occurrences from the study area were used to train and validate the model. The experiments showed an improvement in several statistic measurements such as Root mean square error = 0.3732, Overall accuracy = 79.35%, Mean absolute error = 0.3075, and Area under Receiver operating characteristic = 0.886 in comparison to conventional benchmark methods. Based on the results, the use of SSA would enhance the search efficiency and could be used as an alternative optimizer for a multiple hidden layer neural network for landslide application as well as for other natural hazard analysis.