Intelligent Landslide System Based on Discriminant Analysis and Cascade-Forward Back-Propagation Network

Inspired from the great potential of the discriminant analysis (DA) as a feature selection tool for pattern recognition, this paper proposes an intelligent prediction of landslides based on selected optimum factors. The proposed prediction framework consists of three parts: the collection and extraction of landslide factors, determination of the important factors using DA, and the artificial intelligence for the classification and prediction of landslide hazard mapping for Penang Island. Twenty-one factors are extracted and collected for the first part. The DA is introduced as a feature selection tool for the second part while the cascade-forward back-propagation network (CFBPN) is proposed to predict the locations where landslides are prone to occur. The CFBPN is compared with multilayered perceptron network and Elman back-propagation network. The performances were verified using the classification and prediction accuracy. Results obtained proved that the proposed DA is an effective feature selection technique. Based on the results, the CFBPN produces good performance after the factor selection process with an accuracy of 89.28 % for the classification and 92.58 % for the prediction, as compared with the accuracy before the factors selection process, 88.13 % for the classification and 91.55 % for the prediction.