Artificial neural network model to predict transport parameters of reactive solutes from basic soil properties
2019
Mojid, M.A. | Hossain, A.B.M.Z. | Ashraf, M.A.
Measurement of solute-transport parameters through soils for a wide range of solute- and soil-types is time-consuming, laborious, expensive and practically impossible. So, indirect methods for estimating the transport parameters by pedo-transfer functions are now advancing. This study developed and evaluated an Artificial Neural Network (ANN) model for estimating the transport velocity (V), dispersion coefficient (D) and retardation factor (R) of NaAsO₂, Pb(NO₃)₂, Cd(NO₃)₂, C₉H₉N₃O₂ and CaCl₂ from the basic soil properties. Breakthrough data of the solutes were measured in 14 agricultural soils of Bangladesh by time-domain reflectometry (TDR) in repacked soil columns under unsaturated steady-state water-flow conditions. The transport parameters of the chemicals were determined by analyzing the solute breakthrough data. Bulk density (γ), organic carbon (OC), clay (C) content, pH, median grain diameter (D₅₀) and uniformity coefficient (Cᵤ) of the soils were determined. An ANN model for V, D and R was developed by using data of eight soils, validated/tested with the data of five soils and verified with the data of one soil. Clay content and bulk density of the soils were the most sensitive input variables to the ANN model followed by other soil properties (OC, C, pH, D₅₀ and Cᵤ). The model reliably predicted V, D and R with relative root-mean-square error (RRMSE) of 0.028–0.363, mean error (ME) of – 0.00004 to 0.0005, bias error (BOE%) of 0–0.003 and modeling efficiency (EF) of >0.99. Thus, the ANN model can significantly enhance prediction of pollution transport through soils in terms of cost and effort.
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