Landslide identification using water cycle particle swarm optimization‐based deep generative adversarial network

Abstract

A natural vulnerability called a landslide threatens people, property, and infrastructure in many different places all over the world. Various landslide identification techniques are developed to assess the landslide hazard, but accurate identification of landslide occurrence particularly in certain regions remains a challenging issue, due to the complex scale-dependent processes among geomorphometric features and landslides. To solve the shortcomings of landslide identification, the water cycle particle swarm optimization-based deep generative adversarial network (WCPSO-based Deep GAN) hybridization technique is presented. To develop the developed WCPSO, the water cycle algorithm (WCA) and particle swarm optimization (PSO) were hybridized. The landslide rainfall dataset is used as the source of the input data in this instance, and the Manhattan distance approach is then used to do feature selection. After performing data augmentation based on the results of the feature selection, the proposed WCPSO-based deep GAN approach accurately detects the landslides. The developed WCPSO-driven deep GAN's performance is also measured using the three metrics accuracy, specificity, and sensitivity, with a maximum accuracy of 0.937, higher specificity of 0.919, and higher sensitivity of 0.952.