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Combining Spatial Autocorrelation with Artificial Intelligence Models to Estimate Spatial Distribution and Risks of Heavy Metal Pollution in Agricultural Soils by Elif Günal; Mesut Budak; Miraç Kılıç; Bilal Cemek; Mesut Sırrı is a Environmental Science article available to read on EtoBox.

What is Combining Spatial Autocorrelation with Artificial Intelligence Models to Estimate Spatial Distribution and Risks of Heavy Metal Pollution in Agricultural Soils about?

Information on spatial distribution and potential sources of heavy metals in agricultural lands is very important for human health and food safety. In this study, pollution degree of lead (Pb), cadmium (Cd), and nickel (Ni) in Yüksekova Plain, located on the border in the southeastern part of Turkey, was evaluated by geoaccumulation index (Igeo), modified contamination factor (mCdeg), and Nemerow pollution index (PI Nemerow ) combined with spatial autocorrelation using deep learning algorithms. A total of 304 soil samples were collected from two different depths (0-20 and 20-40 cm) in the study area, which covered 17.5 thousand ha land. Covariates were determined for spatial distribution models of Pb, Cd, and Ni by factor analysis (FA). Spatial distribution models for surface soils were developed using pedovariables (silt, sand, clay lime, organic matter, electrical conductivity, pH, Ca, and Na) determined by the FA and Igeo and mCdeg values by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models. The estimation success of models for different depths was assessed by root mean square error (RMSE), mean absolute percent error (MAPE), and Taylor dia

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Author

Elif Günal; Mesut Budak; Miraç Kılıç; Bilal Cemek; Mesut Sırrı

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Environmental Science (Physical Sciences)