Trusted artificial intelligence for environmental assessments: An explainable high-precision model with multi-source big data  

作　　者：Haoli Xu Xing Yang Yihua Hu Daqing Wang Zhenyu Liang Hua Mu Yangyang Wang Liang Shi Haoqi Gao Daoqing Song Zijian Cheng Zhao Lu Xiaoning Zhao Jun Lu Bingwen Wang Zhiyang Hu 

机构地区：[1]State Key Laboratory of Pulsed Power Laser,College of Electronic Engineering,National University of Defense Technology,Hefei,230037,China [2]Jianghuai Advance Technology Center,Hefei,230000,China [3]Key Laboratory of Electronic Restriction of Anhui Province,Hefei,230037,China [4]Defense Engineering College,Army Engineering University of PLA,Nanjing,210007,China [5]International Studies College,National University of Defense Technology,Nanjing,210000,China [6]School of Electrical Engineering and Automation,Hefei University of Technology,Hefei,230009,China

出　　处：《Environmental Science and Ecotechnology》2024年第6期327-338,共12页环境科学与生态技术（英文）

基　　金：Dreams Foundation of Jianghuai Advance Technology Center(No.2023-ZM01D006);National Natural Science Foundation of China(No.62305389);Scientific Research Project of National University of Defense Technology under Grant(22-ZZCX-07)。

摘　　要：Environmental assessments are critical for ensuring the sustainable development of human civilization.The integration of artificial intelligence(AI)in these assessments has shown great promise,yet the"black box"nature of AI models often undermines trust due to the lack of transparency in their decision-making processes,even when these models demonstrate high accuracy.To address this challenge,we evaluated the performance of a transformer model against other AI approaches,utilizing extensive multivariate and spatiotemporal environmental datasets encompassing both natural and anthropogenic indicators.We further explored the application of saliency maps as a novel explainability tool in multi-source AI-driven environmental assessments,enabling the identification of individual indicators'contributions to the model's predictions.We find that the transformer model outperforms others,achieving an accuracy of about 98%and an area under the receiver operating characteristic curve(AUC)of 0.891.Regionally,the environmental assessment values are predominantly classified as level Ⅱ or Ⅲ in the central and southwestern study areas,level Ⅳ in the northern region,and level Ⅴ in the western region.Through explainability analysis,we identify that water hardness,total dissolved solids,and arsenic concentrations are the most influential indicators in the model.Our AI-driven environmental assessment model is accurate and explainable,offering actionable insights for targeted environmental management.Furthermore,this study advances the application of AI in environmental science by presenting a robust,explainable model that bridges the gap between machine learning and environmental governance,enhancing both understanding and trust in AI-assisted environmental assessments.

关 键 词：Intelligent environmental assessment TRANSFORMER Multi-source data Explainable AI 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]