农业环境科学学报2023，Vol.42Issue(12)：2860-2868,9.DOI:10.11654/jaes.2023-0898
机器学习在生态环境损害鉴定评估领域的应用前景
Prospects of machine learning in the field of ecological environmental damage identification and assessment
摘要
Abstract
Ecological environment damage appraisal and assessment is essential for environmental administrative punishment.With the continuous occurrence of ecological environment damage cases in recent years,the complexity of damage appraisal workflow,the workload of analyzing case information,and the seriousness of the data missing problems,such as time-consuming and exhausting on-site investigation,difficulty in the traceability of pollutants,unclear baseline,and difficulty in determining the damage compensation,continue to emerge.This study explored the prospects for applying machine learning in the appraisal and evaluation of ecological environmental damage to address these problems.Machine learning has been crucial in data mining,image recognition,and natural language processing in recent years through its powerful computational ability.By reviewing the existing progress of machine learning in the above fields,combined with the overall workflow of ecological environment damage appraisal with an in-depth exploration of the prospects for applying machine learning in damage appraisal,this study analyzed the challenges and limitations of applying machine learning in appraisals and indicates that it is challenging to solve these problems by its application.It also indicates that machine learning can improve the efficiency of damage appraisal and promote its orderly and systematicly development.关键词
生态环境损害鉴定评估/机器学习/图像识别/自然语言处理Key words
ecological damage assessment/machine learning/image recognition/natural language processing分类
资源环境引用本文复制引用
武子豪,吴礼滨,洪伟,丁泽聪,易皓,张晓园,曾子龙,崔恺..机器学习在生态环境损害鉴定评估领域的应用前景[J].农业环境科学学报,2023,42(12):2860-2868,9.基金项目
中央级公益性科研院所基本科研业务费专项资金项目(PM-zx703-202204-070,PM-zx703-202305-270,PM-zx703-202305-189) Fundamental Research Funds for the Central Public Welfare Research Institutes(PM-zx703-202204-070,PM-zx703-202305-270,PM-zx703-202305-189) （PM-zx703-202204-070,PM-zx703-202305-270,PM-zx703-202305-189）
