| 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. |
| 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 |
| 投稿时间：2023-10-30 |
| DOI：10.11654/jaes.2023-0898 |
| 中文关键词: 生态环境损害鉴定评估 机器学习 图像识别 自然语言处理 |
| 英文关键词: ecological damage assessment machine learning image recognition natural language processing |
| 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） |
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| 中文摘要: |
| 生态环境损害鉴定评估工作是环境行政处罚的重要依据，随着近年来生态环境损害案件的不断发生，损害鉴定工作流程复杂、案件资料分析工作量大、数据缺失严重等问题不断显现，如现场勘察耗时耗力、污染物溯源困难、基线不清、损害赔偿金额难以确定等。为解决这些问题，本文探讨了机器学习在生态环境损害鉴定评估中的应用前景。近几年，机器学习凭借其强大的计算能力，已在数据挖掘、图像识别和自然语言处理等领域发挥了重要作用，通过综述机器学习在上述领域的已有进展，结合生态环境损害鉴定评估总体工作流程，本文深入探究了机器学习在损害鉴定评估中的应用前景，分析了机器学习在鉴定评估工作中的挑战和局限性，指明机器学习的应用可以提高损害鉴定评估的工作效率，促进损害鉴定评估有序化、系统化发展。 |
| 英文摘要: |
| 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. |
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