Manuscript received June 4, 2022; revised July 1, 2022; accepted August 3, 2022.
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.
Index Terms—Neural network, support vector machine, random forest, gas station, soil and groundwater pollution
I-Cheng Chang is with the Department of Environmental Engineering, National Ilan University, Yilan, Taiwan. Email: email@example.com (I.C.C.)
Shen-De Chen is with the Apollo Technology Co., Ltd, Taipei, Taiwan. Email: firstname.lastname@example.org (S.D.C.)
Tai-Yi Yu is with the Department of Risk Management, Ming Chuan University, Taipei, Taiwan.
*Correspondence: email@example.com (T.Y.Y.)
Cite: I-Cheng Chang, Shen-De Chen, and Tai-Yi Yu*, "Establishment of Risk Prediction Model for Soil and Groundwater Pollution of Gas Station with Machine Learning Techniques," International Journal of Machine Learning vol. 13, no. 4, pp. 153-157, 2023.Copyright @ 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).