Interpolation of soil infiltration in furrow irrigation: Comparison of kriging, inverse distance weighting, multilayer perceptron and principal component analysis methods

Nazli Alipour,Abolfazl Nasseri

doi:10.17951/pjss.2019.52.1.59

Abstract

<p>Study on soil infiltration rate as part of water cycle is essential for managing water resources and designing irrigation systems. The present study was conducted with the aim to compare Kriging, inverse distance weighting (IDW), multilayer perceptron (MLP) and principal component analysis (PCA) methods in the interpolation of soil infiltration in furrow irrigation, and determine the best interpolation method. To conduct infiltration tests, furrows were made on the farm in four triad groups. Infiltration through the blocked furrows method was measured 10, 20, 30, 40, 50, 60, 90, 120, 150, 160, 180 and 210 min after irrigation at a 10-meter distance in each furrow. Data were analyzed by GS+ and Neuro Solutions (NS) software packages. In this study, the maximum error (ME), mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), relative error (RE) and correlation coefficient (r) were used to compare the interpolation methods. The results of analysis of variance (ANOVA) indicated that differences in methods based on RMSE, MBE, MAE and ME indices were not significant; however, this difference was significant based on r and RE indices. According to the ANOVA results, it can be said that the PCA method with a r of 0.69 and RE of 31%, was predicted with a higher accuracy as compared to other methods.</p>

Highlights

With increasing population and reducing water resources, water consumption efficiency improvement in agricultural production has attracted great attention in recent years
The results indicated that the spatial correlation of the variable on the farm surface increases with increase in the irrigation time, this correlation reaches 100% at the time of 210 min
Soil infiltration patterns at t = 210 min were presented in Figures 4 and 5 for Kriging and Inverse distance weighting (IDW) methods, for instance

Summary

Introduction

With increasing population and reducing water resources, water consumption efficiency improvement in agricultural production has attracted great attention in recent years. Uniform distribution of water on the farm is essential for optimal exploitation of existing water resources. Many studies have reported the negative effects of non-uniform water distribution on product amount and wastes of cumulative infiltration (Sanchez et al 2010, Salmeron et al 2012, Perez Ortola et al 2015). Infiltration is one of the most important soil parameters in designing, evaluating and planning surface irrigation methods (Elliott and Walker 1982), which directly affects its efficiency (Tabatabaei et al 2005). Water infiltration in soil is a function of time and location, and its measurement is difficult, time-consuming and costly. Finding an estimation method with the ability to provide the best results as compared to farm data seems necessary

Methods

Results

Conclusion