基于机器学习和情景分析的我国焦化行业企业用地污染预测

针对焦化行业企业用地缺乏时序连续监测数据而无法预测其污染趋势的问题，从企业特征、企业管理水平、污染物特征和自然地理要素等4个方面选取13个影响企业用地污染的指标，识别焦化行业企业用地污染主控因子，在此基础上构建基于机器学习的焦化行业企业用地污染预测模型，并在不同情境下，对2025年和2030年焦化行业企业用地污染状况进行预测.结果表明，生产经营活动时间、建厂时间、企业环境监管记录、土壤黏粒和年均风速是焦化行业企业用地污染的主控因子；相对于支持向量机模型、BP神经网络模型和决策树模型，逻辑斯蒂模型预测价值高、性能指标稳健，其预测精度受试者工作曲线面积为0.91，模型准确率和召回率分别为84%和88%.在乐观情境下，2025年和2030年焦化行业高概率污染地块数量分别为1599块和1695块；在悲观情境下，2025年和2030年焦化行业高概率污染地块数量分别为1671块和1715块.研究结果可为焦化行业企业用地的修复治理和生态环境的宏观决策提供科学依据.

Soil Pollution Prediction from Enterprise for Coking Industry in China Based on Machine Learning and Scenario Analysis

Owing to the lack of sequential monitoring data of soil pollutants in coking industry enterprises, it is hard to accurately predict their soil pollution. To predict the trend of soil pollution of coking industry enterprises in the future, a prediction model should be developed using machine learning based on the influencing factors. A total of 13 potential factors were selected from the enterprise characteristics, enterprise management level, pollutant characteristics, and natural factors, and the main controlling factors were identified. On this basis, the prediction models were developed using a support vector machine, BP neural network model, decision tree model, and logistic model, and then the pollution situation of enterprises in the coking industry in 2025 and 2030 was predicted under different scenarios. The results indicated that time of service for the enterprise, time of establishment for the enterprise, the environmental illegal record, soil clay, and annual wind speed were the major controlling factors of soil pollution of enterprises in the coking industry. Compared with the support vector machine, BP neural network model, and decision tree model, the logistic model had a robust performance index, with an area under the receiver operating characteristic curve of 0.91. The accurate rate and recall rate were 84% and 88%, respectively. Under the optimistic scenario, there will be 1599 and 1695 plots with a high probability of pollution in the coking industry in 2025 and 2030, respectively; under the pessimistic scenario, there will be 1671 and 1715 plots with a high probability of pollution in the coking industry in 2025 and 2030, respectively. The results of this study provided a scientific basis for soil environmental remediation and eco-environmental strategy development for the coking industry.