Assessing and predicting the illegal dumping risks in relation to road characteristics

Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.

Bibliographische Detailangaben
Veröffentlicht in:Waste management (New York, N.Y.). - 1999. - 169(2023) vom: 01. Sept., Seite 332-341
1. Verfasser: Du, Linwei (VerfasserIn)
Weitere Verfasser: Zuo, Jian, Vanzo, John, Chang, Ruidong, Zillante, George
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2023
Zugriff auf das übergeordnete Werk:Waste management (New York, N.Y.)
Schlagworte:Journal Article Illegal dumping Low-population density Prediction Risk assessment Road characteristics Solid waste
Beschreibung
Zusammenfassung:Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.
Using historical data to assess illegal dumping risks has significant potential to enhance the effectiveness of waste management in low-population density counties where the ability to patrol and regulate illegal dumping is limited. Using big data and geographical analysis to identify high-risk areas plays an important role in improving the effectiveness of supervision related to illegal dumping. However, current methods for classifying risk areas have limited accuracy. Taking an area in South Australia as an example, this study aims to improve the accuracy of classifying risk areas by using geo-information technology and machine learning methods. The results show that combining illegal dumping locations with road characteristics allows the high-risk areas to be refined to road sections. Compared with identifying the whole road or area as a high-risk spot, this result could be beneficial for monitoring illegal dumping in real life. Moreover, this model allows the analysis of factors that affect illegal dumping locations. Results show that the influencing factors for different risk levels of illegal dumping vary significantly. The model developed in this research can effectively distinguish risk levels according to these factors, and the model classification accuracy can reach 85%. In addition, there are priorities amongst these factors. This finding could help environmental authorities to allocate equipment and personnel with consideration of varying level of importance of those factors. This study has both technical contributions to identify high risk areas of illegal dumping, and theoretical implications for its management
Beschreibung:Date Completed 08.09.2023
Date Revised 08.09.2023
published: Print-Electronic
Citation Status MEDLINE
ISSN:1879-2456
DOI:10.1016/j.wasman.2023.07.031