An ensemble machine learning model to uncover potential sites of hazardous waste illegal dumping based on limited supervision experience

作     者：Jinghua Geng Yimeng Ding Wenjun Xie Wen Fang Miaomiao Liu Zongwei Ma Jianxun Yang Jun Bi 

作者机构：State Key Laboratory of Pollution Control and Resource ReuseSchool of the EnvironmentNanjing UniversityNanjing 210023 China 

出 版 物：《Fundamental Research》 (自然科学基础研究（英文版）)

年 卷 期：2024年第4卷第4期

页      面：972-978页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：the National Natural Science Foundation of China(71761147002,71921003,and 52270199) Jiangsu R&D Special Fund for Carbon Peaking and Carbon Neutrality(BK20220014) State Key Laboratory of Pollution Control and Resource Reuse(PCRRZZ-202109) 

主　　题：Hazardous waste Illegal dumping site Positive-unlabeled machine learning Probability prediction Model interpretation 

摘      要：With the soaring generation of hazardous waste(HW)during industrialization and urbanization,HW illegal dumping continues to be an intractable global *** in developing regions with lax regulations,it has become a major source of soil and groundwater *** dominant challenge for HW illegal dumping supervision is the invisibility of dumping sites,which makes HW illegal dumping difficult to be found,thereby causing a long-term adverse impact on the *** to utilize the limited historic supervision records to screen the potential dumping sites in the whole region is a key challenge to be *** this study,a novel machine learning model based on the positive-unlabeled(PU)learning algorithm was proposed to resolve this problem through the ensemble method which could iteratively mine the features of limited historic *** of the random forest-based PU model showed that the predicted top 30%of high-risk areas could cover 68.1%of newly reported cases in the studied region,indicating the reliability of the model *** novel framework will also be promising in other environmental management scenarios to deal with numerous unknown samples based on limited prior experience.