Research on weighting method of geological hazard susceptibility evaluation index based on apriori Algorithm

Abstract

Given the inconsistency between the information value and the weight value in the weighted information value model, a weight model based on the Apriori algorithm is established in this paper to analyze the correlation between the second-level intervals of disaster factors and the susceptibility of geological disasters. The objective weight of the second-level intervals of each index factor is calculated through the mining of association rules by the Apriori algorithm. The subjective uncertainty of the existing second-level factor weighting method is eliminated. Taking the geological disaster data of Xiangtan urban area as an example, 10 evaluation indexes were selected to establish the entropy weight method-information value (EWM-IV) model and the entropy weight method-Apriori algorithm-information value (EWM-Apriori-IV) model to evaluate the geological disaster susceptibility, and the disaster area ratio and the receiver operating characteristic curve (ROC) verification method were used to test and analyze the evaluation results. The results showed that compared with the EWM-IV model, the EWM-Apriori-IV model is used to evaluate the disaster area ratio of high-prone area increased by 58.3%, and the disaster area ratio of low-prone area decreased by 43.1%, the area under the curve (AUC) increased by 7.4%, and the evaluation accuracy was relatively improved compared with the former. This paper proves the rationality and practicability of the weighting method of the geological hazard susceptibility evaluation index based on the Apriori algorithm.

Collapse Susceptibility Assessment in Taihe Town Based on Convolutional Neural Network and Information Value Method

“…With the development of the Geographical Information System (GIS) and Remote Sensing (RS) technology, geological hazard susceptibility assessments have also become more efficient and accurate, which greatly accelerates the research process in this field. The assessment methods of geological hazards mainly include the Analytic Hierarchy Process (AHP) [3][4][5][6][7], the Support Vector Machine (SVM) [8,9], the Information Value method (IV) [10,11], the Frequency Ratio method (FR) [4,12], the Weight of Evidence method (WOE) [4,13,14], Artificial Neural Network (ANN) [15][16][17], Random Forest (RF) [8,18] and a variety of model coupling methods [19][20][21][22].…”