A Systematic Approach to Using A Fuzzy Inference System and Comparing it With Multi-criteria Decision- Making Techniques for Environmental Management of Crude Oil Contaminated Soils in an Oil Pump Station

Abstract

Background & Aims of the Study: Oil pollution has a significant impact on soil properties. There are various physical, chemical, biological, or a combination of these methods to deal with it. An important challenge in managing oil-contaminated soils is choosing the ideal method (s) to deal with them. Different methods have been used for decision-making, but can the fuzzy inference engine be used in environmental decisions? The present study investigated the efficiency of a fuzzy inference system for environmental management of crude oil contaminated soils in an oil pump station in southwestern Iran in 2019. Materials and Methods: Four soil samples were taken to identify the current situation of oil pollution in the study area. Factors, such as the extent of pollution, duration of pollution, climatic, geographical, and biological properties, and the type of soil used in the study area were also determined. Seventeen methods to deal with oil pollution in the soil were identified. Based on the data type, Mamdani fuzzy method was used. For input data, the triangular membership function used the maximum-minimum combination of operators in the inference step, and the Center of Gravity (COG) method was used for defuzzification. Results: Based on the results of the fuzzy method, land farming with a membership of 0.071 to a completely desirable set, the best method to deal with oil polution in the soil was determined. Conclusion: The results of this study and its overlap with other methods of prioritizing options in managerial decisions showed that the fuzzy inference engine could be used as an intelligent system for prioritizing options and decision-making.