An evaluation of genetic algorithm method compared to geostatistical and neural network methods to estimate saturated soil hydraulic conductivity using soil texture


  • R. Sedghi
  • S. Bairami University of Mohaghegh Ardabili
  • Y. Hoseini University of Mohaghegh Ardabili

ABSTRACT-Determining hydraulic conductivity of soil is difficult, expensive, and time-consuming. In this study, Algorithm Genetic and geostatistical analysis and Neural Networks method are used to estimate soil saturated hydraulic conductivity using the properties of particle size distribution. The data were gathered from 134soil profiles from soil and lander form studies of the Ardabil Agricultural Organization. Results showed that Or denary cokriging has the best fit for the geostatistical methods. The best-fitted vario gram was the exponential model with anugget effect of 0 cm day-1 and sill of 156 cm day-1 which is the strength of the spatial structure and full effect of the structural components on the vario gram model for the region; also, in the or denary cokriging method, an accurate estimate was obtained using R2 = 0.93 and RMSE = 3.21.Multilayer perceptron (MLP) network used the Levenberg- Marquardt (trainlm) algorithm with are gression coefficient (R2) of 0.997 and Root Mean Square Error (RMSE) of 1.22 to estimate the hydraulic conductivity of saturated soil. For GA model, parameters of root mean square error (RMSE) cm day-1 and the coefficient of determination (R2) were determined as 1.35 and 0.926, respectively. Performance evaluation of the models showed that the Neural Networks model compared with geostatistical analysis and genetic algorithm was able to predict soil hydraulic conductivity with high and more accuracy and results of this method was closer to the measurement results. 

Upgrade to premium to download articles

Sign up to access the full text

sign up

Already have an account?login

similar resources

مقایسه روش‌های شبکه عصبی مصنوعی و رگرسیونی برای پیش‌بینی هدایت هیدرولیکی اشباع خاک‌های استان خوزستان

Direct measurement of soil hydraulic characteristics is costly and time-consuming. Also, the method is partly unreliable due to soil heterogeneity and laboratory errors. Instead, soil hydraulic characteristics can be predicted using readily available data such as soil texture and bulk density using pedotransfer functions (PTFs). Artificial neural networks (ANNs) and statistical regression are t...

full text

Empirical Correlations and an Artificial Neural Network Approach to Estimate Saturated Vapor Pressure of Refrigerants

The examination of available vapor pressure data in the case of the methane, ethane, propane and butane halogenated refrigerants, allowed recommendations of standard equations for this property. In this study, three new models include a general correlation; a substance-dependent correlation and an artificial neural network (ANN) approach have been developed to estimate the saturated vapor press...

full text

Estimating the Saturated Hydraulic Conductivity of Soil Using Gene Expression Programming Method and Comparing It with the Pedotransfer Functions

Saturated hydraulic conductivity of soil is an important physical property of soil that affects water movement in soil, Since the measurement of saturated hydraulic conductivity by direct methods in the field or in the laboratory is hard, time-consuming and costly, the indirect methods are being used.The aim of this study is to estimate the saturated hydraulic conductivity from other soil prope...

full text

Effect of pH on Saturated Hydraulic Conductivity and Soil Dispersion

The adverse effects of exchangeable sodium on soil hydraulic conductivity (K) are well known, but at present only sodicity and total electrolyte concentration are used in evaluating irrigation water suitability. In arid areas, high sodicity is often associated with high dissolved carbonate and thus high pH, but in humid areas high sodicity may be associated with low pH. To evaluate the effect o...

full text

Study of Saturated Hydraulic Conductivity Variations in Different Aggregate Size Distributions in an Agricultural Soil

Saturated hydraulic conductivity (Ks) is one of the most important soil physical characteristics that plays a major role in the soil hydrological behaviour. It is mainly affected by the soil structure characteristics. Aggregate size distribution is a measure of soil structure formation that can affect Ks. In this study, variations of Ks were investigated in various aggregate size distributions ...

full text

Save to my library

Save to my library Already added to my library

{@ msg_add @}

  Save resource for easier access later

Download full text

Sign up to access the full text


Already have an account?login

Journal title:

volume 36  issue 1

pages  91- 104

publication date 2022-06-22

By following a journal you will be notified via email when a new issue of this journal is published.

Hosted on Doprax cloud platform

copyright © 2015-2022