# Prediction of Total Petroleum Hydrocarbons and Heavy Metals in Acid Tars Using Machine Learning
## 1 Introduction
* Characterization of stabilized tar leachate and classifying it according to the Council Decision of December 2002 and Romanian Ministry Order 95 12 February 2005 [27]. * The identification of the conditions for the capitalization of the research and the obtained results for the treatment of the acid tars in the studied lagoon. The characterization of untreated and treated acid tars in each site is conventionally accomplished through expensive sampling and testing of numerous borehole samples using highly sensitive and specific analytical procedures involving extraction and subsequent gravimetric or chromatographic techniques [4; 13; 28]. These procedures are laborious, expensive, time-consuming, and inadequate when high spatial and temporal resolutions of petroleum hydrocarbon content are required [15]. To overcome these limitations, a machine learning model was used in this work to predict pH, petroleum hydrocarbon concentrations, heavy metals, and As in the source area. The presented method estimates some selected properties of acid tar (pH, TPH, five heavy metals and a metalloid--As) based on the geographical location and depth of where the sample was taken. To achieve this purpose, a training database was created that contains data from other acid tar samples whose geographic coordinates and depth are already known. The Automatic Machine Learning technique determines the algorithm that offers the best estimation (smallest error) for every acid tar property that was estimated. Eight response variables from the field survey and database were considered as output variables for machine learning to train and test the models. Artificial intelligence (AI) represents the simulation of human intelligence by computing machines, which are programmed to think and act as a human would [29]. AI is presented in different forms in our everyday lives in fields like games, stock market predictions, aeronautics, or electronic commerce. The application of AI is an important issue in chemical, process, and petroleum engineering. Thus, they have been widely used in various applications in the chemical engineering field, including modeling, process control, classification, fault detection, and diagnosis [30]. The application of AI in important issues in oilfield development, including oilfield production dynamic prediction, developing plan optimization, residual oil identification, fracture identification, and enhanced oil recovery [31]. Also, with AI, refineries can leverage large datasets to help provide analytics that produce actionable insights [32; 33]. In a similar manner, artificial intelligence and machine learning are essential to controlling or managing land contamination through prediction, clustering, data-centric analysis, and soil quality evaluation [34; 35]. The prediction of soil petroleum hydrocarbon concentration is achieved by machine learning and the resistivity tomography method [36]. Since the field measurement of soil heavy metal content involves significant costs, methods have been developed to estimate soil heavy metals based on remote sensing images and machine learning [37; 38; 39]. Also, some measurements were studied and published using machine learning predictions of soil pH [40]. Unfortunately, there is limited research on predicting soil pollution, let alone the hydrocarbons and heavy metal content of acid tar logoons; most research estimates air and water pollution. The concentration of hydrocarbons, heavy metals, and soil acidity does not change significantly without appropriate remedial measures, and this is based on the knowledge of the level of contamination of the soil with these contaminants. There have been numerous studies worldwide on the qualitative analysis, prevention and control, and remediation of soil pollution; however, there have been few studies on the quantitative analysis of soil pollution. It has become extremely important to evaluate and accurately predict the content of hydrocarbons and heavy metals in soil via acid tars logoons.