A Nonparametric Model by Using k-nearest neighbor Technique for Predicting Soil Saturated Hydraulic Conductivity

Document Type : Research Article

Authors

• V.R. Jalali
• M. Homaee

Faculty of Agriculture, Tarbiat Modares University of Tehran

10.22067/jsw.v0i0.9388

Abstract

Abstract
Saturated hydraulic conductivity (Ks) is needed for many studies related to water and solute transport, but often cannot be measured because of practical and/or cost-related reasons. Nonparametric approaches are being used in various fields to estimate continuous variables. One type of the nonparametric lazy learning algorithms, a k-nearest neighbor (k-NN) algorithm, was introduced and tested to estimate saturated hydraulic conductivity (Ks) from other soil properties including soil textural fractions, EC, pH, SP, OC, TNV, ρs and ρb. A number of 10 nearest neighbors, based on Cross Validation technique were selected to perform saturated hydraulic conductivity prediction from 151 soil sample attributes. The nonparametric k-NN technique performed mostly equally well, in terms of Pearson correlation coefficient (r=0.801), modeling efficiency (EF=0.65), root-mean-squared errors (RMSE=71.15) maximum error (ME=120.47), coefficient of determination (CD=1.32) and coefficient of residual mass (CRM=-0.046) statistics. It can be concluded that the k-NN technique is a competitive alternative to other techniques such as pedotransfer functions (PTFs) to estimate saturated hydraulic conductivity.

Keywords: k-nearest neighbor (k-NN), Modeling, Saturated hydraulic conductivity

20.1001.1.20084757.1390.25.2.15.2

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