Landslides Identification through Conglomerate Grey Wolf Optimization and Whale Optimization Algorithm
Authors
Rajesh B, Assistant Professor, BASE University, Bengaluru
Details
BASE University Working Paper Series 09 /2021
Abstract
The research aims to develop a prediction model to identify landslide through Deep Neural Network (DNN) for predicting the hazard assessment and mitigation of landslide-related losses. It is essential to identify landslide for preventing from affecting the population with significant socioeconomic damage and high economic losses in developing countries. While evaluating the performance of traditional DNN, it is apparent that changes in weights influence the output performance. In this basis, the research aims to involve optimization techniques to identify optimal weights parameters. The involved optimization techniques are Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), and the proposed conglomeration of GWO and WOA (CGW). The performance of the proposed technique shows the better performance over other comparative techniques. The developed proposed model predicts the types and size of landslide effectively with 97.75% accuracy.
Keyword: landslide, Deep Neural Network, Grey Wolf Optimization (GWO), Whale Optimization Algorithm, conglomeration of GWO and WOA (CGW).