Title: Uncertain genetic neural network for landslide hazard prediction

Authors: Yimin Mao; Jiawei Wang; Xinrong Lu; Dinghui Mao; Xiaodong Gao

Addresses: Information Institute of Jiangxi University of Science and Technology, GanZhou, JiangXi, China ' Information Institute of Jiangxi University of Science and Technology, GanZhou, JiangXi, China ' Applied Science Institute of Jiangxi, University of Science and Technology, GanZhou, JiangXi, China ' 211 Battalion, Co., Ltd., in China Shanxi, Nuclear Industry Group Company, Xi'an 710024, China ' Information Institute of Jiangxi, University of Science and Technology, GanZhou, JiangXi, China

Abstract: Due to difficulties in obtaining and effectively processing rainfall and other uncertain factors in landslide hazard prediction, as well as the existence of local minima and the slow training speed of the standard back-propagation algorithm, a prediction method based on an uncertain genetic neural network in order to improve the hazard prediction accuracy has been proposed. The method is founded on an optimised genetic algorithm and the back-propagation neural network classification algorithm. Briefly, combining the prediction theory of landslide disaster with rainfall and other uncertainties associated with landslides, we propose the concept of separation of uncertain data, elaborate the processing methods of uncertain property data, and build the uncertain genetic neural network and a landslide hazard prediction model. The experiment conducted in the Baota district of Yan'an showed that the effective and overall accuracies of the method are 92.1% and 86.7%, respectively, and prove the feasibility of an uncertainty genetic neural network algorithm in landslide hazard prediction.

Keywords: uncertain data; landslide; genetic algorithm; back-propagation neural network; hazard prediction.

DOI: 10.1504/IJHPSA.2017.091492

International Journal of High Performance Systems Architecture, 2017 Vol.7 No.3, pp.140 - 150

Received: 04 May 2017
Accepted: 16 Oct 2017
Published online: 02 May 2018 *

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