Loss prediction of mountain flood disaster in villages and towns based on rough set RBF neural network

Abstract

Flood disasters cause serious economic losses to our country every year, which affects the rapid development and construction of our country’s economy to a certain extent. Our country’s mountain floods mostly occur in mountainous hills and other rural areas with relatively backward economic development. Disaster relief and reconstruction ability in these places are relatively weak. To provide an important basis for making decisions on flood prevention and mitigation, we must first be able to scientifically, accurately and efficiently assess the losses caused by flood disasters. Aiming at the problems of low assessment accuracy, heavy workload and cumbersome operation in the disaster assessment work of Dangtian in the disaster-affected area, this paper starts from the analysis of the disaster mechanism and attribute characteristics of mountain floods and proposes an RBF neural network integration based on rough set flood damage loss assessment model of, and the flood loss assessment model based on neural network integration was applied in the flood loss prediction work of Village A, making the flood loss rate suitable for the use of the flood loss assessment model. The forecast results are compared and analyzed with the actual statistically published flood loss value, which verifies the feasibility of the forecast model and provides a new method for flood loss assessment. The prediction results of mountain flood disaster loss in villages and towns show that the research based on the improved RBF neural network earthquake damage loss prediction can quickly and accurately obtain more reliable evaluation results in terms of direct economic loss and casualty assessment, which improves rescue efficiency and reduces the affected population. It has significant practicality in disaster reduction and rescue operations, industrial layout planning, and economic benefit evaluation.