文献类型：期刊文献

中文题名：Ecological Suitability Assessment Methods of Waste Pile-Up along Railway Routes Based on Machine Learning Algorithms

作者：Cuicui Ji[1,2,3,4] Zaoyang Huang[1] Xiangjun Pei[2,3] Bin Sun[4] Lichuan Chen[5] Dan Liang[5] Yanfei Kang[5]

第一作者：Cuicui Ji

机构：[1]School of Smart City,Chongqing Jiaotong University,Chongqing 400074,China;[2]State Key Laboratory of Geohazard Prevention and Geoenvironment Protection,Chengdu University of Technology,Chengdu 610059,China;[3]State Environmental Protection Key Laboratory of Synergetic Control and Joint Remediation for Soil and Water Pollution,Chengdu University of Technology,Chengdu 610059,China;[4]Institute of Forest Resource Information Techniques,Chinese Academy of Forestry,Beijing 100091,China;[5]Chongqing Institute of Geology and Mineral Resources,Chongqing 400042,China

年份：2024

期号：5

起止页码：160-172

中文期刊名：Ecosystem Health and Sustainability

外文期刊名：生态系统健康与可持续性(英文)

收录：CSCD:【CSCD2023_2024】；

基金：funded by the National Science Foundation of China(No.42301459);the China Postdoctoral Science Foundation(No.2023M740418);the China Meteorological Services Association Meteorological Science and Technology Innovation Platform Project(No.CMSA2023MC002);the Opening Fund of State Key Laboratory of Geohazard Prevention and Geo-environment Protection(Chengdu University of Tech nology)(No.SKLGP2022K028);the Opening Fund of State Environmental Protection Key Laboratory of Synergetic Control and Joint Remediation for Soil and Water Pollution(No.GHBK-2023-04);the Chongqing Key Project of Technological Innovation and Application Development(No.CSTB2022TIAD-KPX0198)

语种：英文

中文关键词：machine learning;random forest;waste pile up;Landsat;extreme gradient boosting;deep neural network;buffer zone;railway routes;

分类号：X825;TP181

摘要：Waste pile-up along railway routes poses an important threat to the regional ecological environment.However,there is a lack of methods that assess the ecological suitability of waste pile-up(ESWP)at a macro scale,which is crucial for informed decision-making.We define the ESWP and propose a methodology to measure the level of ESWP along railway routes.Specifically,we focus on the Ya'an to Nyingchi section of the railway,selecting a 30-km buffer zone on either side as the study area.To develop ESWP maps,we employed Landsat 8,digital elevation model(DEM),soil database,land use,and meteorological data.We tested 3 machine learning methods—random forest(RF),deep neural network(DNN),and extreme gradient boosting(XGBoost)—using 7 key indicators as input parameters.The performance of these models was evaluated using overall accuracy and the Kappa index.Additionally,we analyzed the relative importance of each indicator on the results.The study reached the following results:Firstly,the combination of selected indicators with machine learning methods effectively assesses the ESWP along railways.Secondly,among the tested methods,DNN demonstrated superior performance,achieving an accuracy of 86.49%,outperforming RF(80.31%)and XGBoost(79.54%).Thirdly,the indicators with the greatest impact on the assessment were biological richness(weight is 0.23),vegetation coverage(weight is 0.20),and soil nutrients(weight is 0.16).These findings provide a novel approach to assessing the ecological suitability and identifying low-risk sites for waste pile-up along railway routes.