Modeling of Soil Water Repellency Using Regression, Artificial Neural Network and Gene Expression Programming

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Heydari Kimia; Asghari Shokrollah; Shahab Arkhazloo Hossain; Hasanpour Kashani Mahsa

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Journal Paper

Paper Information

Journal: APPLIED SOIL RESEARCH Year:2024 | Volume:12 | Issue:4 Page(s): 1-14

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Information Journal Paper

Title

Modeling of Soil Water Repellency Using Regression, Artificial Neural Network and Gene Expression Programming

Author(s)

Heydari Kimia | Asghari Shokrollah | Shahab Arkhazloo Hossain | Hasanpour Kashani Mahsa | Issue Writer Certificate

Keywords

Intelligent Models

Land use

Pedotransfer functions

Readily available soil variables

Water repellency

Abstract

Increasing the concentration of heavy metals in soil has adverse effects on ecosystem and causes serious damage to humans. Quantifying pollution can be helpful for soil management. In this study, the pollution of heavy metals has been studied and quantified in agricultural lands around some industrial units at Ardabil plain. For this, we selected 9 industrial units and prepared 46 soil samples (0 to 30 cm). Clay, sand and silt percentages, soil organic carbon content, pH and EC were measured. The heavy metals were extracted by digestion using HNO3 and HCl and the concentration of Pb, Zn, Cu and Cd were measured by AAS. Pollution index (Pi), comprehensive pollution index (Pj), ecological risk (Er) and potential ecological risk (RI) were calculated. The average concentration of heavy metals varied from 0.724 mgkg-1 of Cd to 120.58 mg/kg-1 of Cu. All regions had Pi greater than 2 and showed mild pollution except region 4 which had slight pollution. The pollution index of Cd had the highest value among all heavy metals. Pj had the lowest (1.268) and highest (3.636) mean values in regions 2 and 5, respectively. Region 2 had slight pollution class, regions 1, 6 and 7 had a mild class and regions 3, 4, 5, 8 and 9 had a moderate class. The ecological risk of Pb, Zn and Cu was lower than 3.0 in all regions and ErCd was between 120 and 240 for regions 3, 5, 8 and 9 increasing a serious pollution class and greater than 240 indicating a severe pollution class for other areas. All regions were in the serious pollution class according to the RI index. There is a significant difference between the concentration of Pb (sig. 5%) and Zn and Cu (sig.1%) in all regions. It shows differences between regions cannot be related to parent material and human activities have resulted in an increase in metal concentration.

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