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

IF 2.7 4区 地球科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Weiyi Ju, Zhixiang Xing
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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.

Abstract Image

基于机器学习和地理信息系统的新型纳特奇灾害空间风险揭示技术:中国常州市的案例研究
近年来,自然灾害引发的技术事故造成了巨大损失。本研究的目的是开发一个数学模型来预测和预防此类事故的风险。该模型应用机器学习来预测此类事故的风险,希望为地方政府提供风险可视化结果。这项研究的预期影响将惠及居民和公益组织。在本研究中,随机森林(RF)、K-近邻(KNN)、反向传播(BP)神经网络、自适应提升(AdaBoost)、梯度提升决策树(GBDT)和极端梯度提升(XGBoost)被用于预测风险值。同时,本研究应用 ArcGIS 对风险预测值进行空间插值,生成风险地图。结果表明,在所测试的五种算法中,RF 算法的分类性能最高。具体而言,射频算法的准确率为 0.874,F1 分数为 0.887,曲线下面积(AUC)为 0.984。风险最高的三个乡镇分别是雪堰、戴埠和上黄,风险面积占比分别为 48.39%、44.34% 和 79.64%。这项研究为地方政府提供了参考,可以有针对性地采取防治措施。对于灾害管理者来说,高风险地区的风险应引起足够重视。政府应建立实时更新的灾害数据库,监控灾情发展。此外,历史灾害数据的开发与获取也值得鼓励。
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来源期刊
Earth Science Informatics
Earth Science Informatics COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
4.60
自引率
3.60%
发文量
157
审稿时长
4.3 months
期刊介绍: The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.
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