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| Prospects of machine learning in the field of ecological environmental damage identification and assessment |
| Received:October 30, 2023 |
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| KeyWord:ecological damage assessment;machine learning;image recognition;natural language processing |
| Author Name | Affiliation | E-mail | | WU Zihao | South China Environmental Forensic Center, South China Institute of Environmental Science, Ministry of Ecology and Environment, Guangzhou 510655, China | | | WU Libin | South China Environmental Forensic Center, South China Institute of Environmental Science, Ministry of Ecology and Environment, Guangzhou 510655, China | | | HONG Wei | South China Environmental Forensic Center, South China Institute of Environmental Science, Ministry of Ecology and Environment, Guangzhou 510655, China | | | DING Zecong | South China Environmental Forensic Center, South China Institute of Environmental Science, Ministry of Ecology and Environment, Guangzhou 510655, China | | | YI Hao | South China Environmental Forensic Center, South China Institute of Environmental Science, Ministry of Ecology and Environment, Guangzhou 510655, China | | | ZHANG Xiaoyuan | South China Environmental Forensic Center, South China Institute of Environmental Science, Ministry of Ecology and Environment, Guangzhou 510655, China | | | ZENG Zilong | South China Environmental Forensic Center, South China Institute of Environmental Science, Ministry of Ecology and Environment, Guangzhou 510655, China | | | CUI Kai | South China Environmental Forensic Center, South China Institute of Environmental Science, Ministry of Ecology and Environment, Guangzhou 510655, China | cuikai@scies.org |
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| 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. |
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