A novel technology for unraveling the spatial risk of Natech disasters based on machine learning and GIS: a case study from the city of Changzhou, China

Abstract

In recent years, technical accidents caused by natural disasters have caused huge losses. The purpose of this study is to develop a mathematical model to predict and prevent the risk of such accidents. The model applied machine learning to predict the risk of such accidents in the hope of providing risk visualization results for local governments. The expected impact of this research will benefit residents and public welfare organizations. In this study, Random Forest (RF), the K-Nearest Neighbor (KNN), the Back Propagation (BP) neural network, Adaptive Boosting (AdaBoost), Gradient Boosting Decision Tree (GBDT), and the Extreme Gradient Boosting (XGBoost) was applied to predict the risk value. At the same time, this study applied ArcGIS to spatially interpolate the risk prediction values to generate the risk map. The results demonstrated that the RF algorithm achieved the highest classification performance among the five algorithms tested. Specifically, the RF algorithm attained an accuracy of 0.874, an F1-Score of 0.887, and an Area Under the Curve (AUC) of 0.984. The three townships with the highest risk were Xueyan, Daibu, and Shanghuang, with the proportion of risk area accounting for 48.39%, 44.34% and 79.64% respectively. This study provides a reference for the local government, which can take targeted measures to prevent and control. For disaster managers, the risks for those high-risk areas should receive sufficient attention. The government should establish a real-time updated disaster database to monitor the development of the situation. Moreover, the development and acquisition of historical disaster data is worthy of encouragement.