Spatial Estimation of Saturated Hydraulic Conductivity from Terrain Attributes Using Regression, Kriging, and Artificial Neural Networks

Motaghian, H. R.; Mohammadi, J.

doi:10.1016/s1002-0160(11)60115-x

Cited by 60 publications

(27 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has been widely used in soil science for developing PTF by predicting soil parameters (Parasuraman et al, 2006;Agyare et al, 2007;Motaghian and Mohammadi, 2011). Recently, the ANN approach has been successfully used as a suitable data processing technique for modelling relationships between soil features and other measurable properties in PTF development (Pachepsky and Rawls, 1999;Baker and Ellison, 2008;Minasny and Mcbratney, 2000).…”

Section: Data Analysis 231 Pedotransfer Function Developmentmentioning

confidence: 99%

Using pedotransfer functions to estimate soil hydraulic conductivity in the Loess Plateau of China

Zhao

Shao

Jia

et al. 2016

CATENA

View full text Add to dashboard Cite

Soil hydraulic conductivity (K s ) is a crucial soil physical property that not only influences soil hydrological processes, but also the planning for vegetation recovery, irrigation practice and drainage design. However, K s data are often lacking at large-scale soil database due to difficulties in direct measurement that is often labour intensive, time consuming and cost inefficient. The objective of this study was to compare the performance of different emerging methods [Multiple linear regression (MLR) and artificial neural network (ANN)] of K s prediction. The pedotransfer function (PTF) is one such method that is based on selected factors closely correlated with K s at regional scale. We collected disturbed and undisturbed soil samples in the 0-40 cm soil layer at 243 sites across the entire typical Loess Plateau of China (430,000 km 2 ) and then measured K s and the potentially related factors. The results showed that K s was normally distributed with moderate a spatial variation (CV = 67%). Correlation analysis indicated that bulk density (BD), saturated soil water content (SSWC), clay content (Clay), silt content (Silt) and latitude were closely correlated (p b 0.05) with K s . Although the accuracies of MLR and ANN were equal in terms of estimating K s , the stability of PTF developed via ANN was not as good as that of MLR. Thus PTF developed via MLR, which included BD, Silt and Clay, was considered as the best model for estimating K s . There is a need to closely monitor the stability and repeatability of PTF during comparison and determination of PTF.

show abstract

Section: Data Analysis 231 Pedotransfer Function Developmentmentioning

confidence: 99%

Using pedotransfer functions to estimate soil hydraulic conductivity in the Loess Plateau of China

Zhao

Shao

Jia

et al. 2016

CATENA

View full text Add to dashboard Cite

show abstract

“…Therefore, it is necessary to have reliable information on characteristic of water quality for effective pollution control and water resource management, especially in arid regions [5,4].…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Network For Defining The Water Quality in The River

Haribowo¹,

Dermawan²,

Yudha³

2018

Civense

View full text Add to dashboard Cite

Abstract. Predicting point and nonpoint source runoff of dissolved and suspended materials into their receiving streams is important to protecting water quality. Therefore, it is important to monitoring the condition of river water quality. The purpose of this study is to predict water quality in small streams using an Artificial Neural Network (ANN). The study focuses on small stream in tributary of Brantas River. The variables of interest are dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), pH and temperature (T). To validate the performance of the trained ANN, it was applied to an unseen data set from a station in the region. The result show that the prediction of DO is 6.03 mg/litre, pH is 6,47 mg/litre and temperature is 25.18°. With the relatively error was 15.63%, 12.64% and 14.12% respectively. It was finally concluded that ANN models are capable of simulating the water quality parameters.

show abstract

“…Nevertheless, PTFs began to be used also with topographic and spatial data (Motaghian & Mohammadi, 2011).…”

Section: Introductionmentioning

confidence: 99%

Pedotransfer functions of soil water properties to estimate the S-index

Caviglione

2018

Rev. bras. eng. agríc. ambient.

View full text Add to dashboard Cite

One big challenge for soil science is to translate existing data into data that is needed. Pedotransfer functions have been proposed for this purpose and they can be point or parametric when estimating the water retention characteristics. Many indicators of soil physical quality have been proposed, including the S-Index proposed by Dexter. The objective of this study was to assess the use of pedotransfer functions for soil water retention to estimate the S-index under field conditions in the diversity of soils of the Paraná state. Soil samples were collected from 36 sites with textures ranging from sandy to heavy clay in the layers of 0-0.10 and 0.10-0.20 m and under two conditions (native forest and cultivated soil). Water content at six matric potentials, bulk density and contents of clay, sand and silt were determined. Soil-water retention curve was fitted by the van Genuchten-Mualem model and the S-index was calculated. S-index was estimated from water retention curves obtained by the pedotransfer function of Tomasella (point and parametric). Although the coefficient of determination varied from 0.759 to 0.895, modeling efficiency was negative and the regression coefficient between observed and predicted data was different from 1 in all comparisons. Under field conditions in the soil diversity of the Paraná state, restrictions were found in S-index estimation using the evaluated pedotransfer functions.

show abstract

Spatial Estimation of Saturated Hydraulic Conductivity from Terrain Attributes Using Regression, Kriging, and Artificial Neural Networks

Cited by 60 publications

References 29 publications

Using pedotransfer functions to estimate soil hydraulic conductivity in the Loess Plateau of China

Using pedotransfer functions to estimate soil hydraulic conductivity in the Loess Plateau of China

Application of Artificial Neural Network For Defining The Water Quality in The River

Pedotransfer functions of soil water properties to estimate the S-index

Contact Info

Product

Resources

About