Establishment of Risk Prediction Model for Soil and Groundwater Pollution of Gas Station with Machine Learning Techniques

Abstract

With the rapid development of network technology and the digital economy, the wave of the era of artificial intelligence has swept the world. Facing the era of big data and artificial intelligence, data-oriented technologies are undoubtedly served as the practical research trend. Therefore, the precise analysis provided by big data and artificial intelligence can provide effective and accurate knowledge and decision-making references for all sectors. In order to effectively and appropriately evaluate the potential risk to soil and groundwater for gas station industry, this study focuses on the potential risk factors affecting soil and groundwater pollution. In the past, our team has evaluated the risk factors affecting the remediation cost of soil and groundwater pollution for possible potential pollution sources such as gas stations, this study proceeds with the existing industrial database for in-depth discussion, uses machine learning technology to evaluate the key factors of pollution risk for soil and groundwater, and compares the differences, applicability and relative importance of the three machine learning techniques (such as neural networks, random forests and support vector machine). The performance indicators reveal that the random forest algorithm is better than support vector machine and artificial neural network. The relative importance of parameters of different machine learning models is not consistent, and the first five dominant parameters are location, number of gas monitoring wells, age of gas station, numbers of gasoline oil nozzle, and number of fuel dispenser for random forest model.