Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity

Merdun, Hasan; Çınar, Özer; Meral, Ramazan; Apan, Mehmet

doi:10.1016/j.still.2005.08.011

Cited by 139 publications

(73 citation statements)

References 26 publications

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“…Root water uptake is assumed to be zero when soil pressure head is below h 3 , i.e. the soil pressure head at the permanent wilting point (h 3 = -15000 cm), and is unlimited for soil pressure head between h 1 (-1 cm) and (-500 cm) for a rapid transpiration (0.5 cm The model performance was evaluated using the statistical indices of the coefficient of determination (R 2 ), the root of the mean squared errors (RMSE) and the mean error (ME) (Bohne and Salzmann, 2002;Ritter et al, 2003;Merdum et al, 2006). (Groot and Verberne, 1991).…”

Section: Actual Crop Transpirationmentioning

confidence: 99%

An easily implemented agro-hydrological procedure with dynamic root simulation for water transfer in the crop–soil system: Validation and application

Yang

Zhang

et al. 2009

Journal of Hydrology

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Section: Actual Crop Transpirationmentioning

confidence: 99%

An easily implemented agro-hydrological procedure with dynamic root simulation for water transfer in the crop–soil system: Validation and application

Yang

Zhang

et al. 2009

Journal of Hydrology

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“…Then, the neural network method was used in many research subjects in engineering geology and civil engineering. The neural network has been applied in structural damage inspection [8], soft rocks strength prediction [9], ground vibration prediction [10], ground surface settlement [11,12], engineering cost prediction [13,14], concrete expansion prediction [15], soil swell potential prediction [16], triaxial compression behavior prediction [17], saturated hydraulic conductivity prediction [18], and other engineering subjects [19][20][21]. Only a fraction of the studies were about predicting the frostheaving behavior of saline soil in Western Jilin [22].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Frost‐Heaving Behavior of Saline Soil in Western Jilin Province, China, by Neural Network Methods

Zhang

Wang

Huo

et al. 2017

Mathematical Problems in Engineering

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In this study, backpropagation neural network (BPNN) and generalized regression neural network (GRNN) approaches are used to predict the frost-heaving ratio (FR) of the saline soil specimen collected from Nong'an, Western Jilin, China. Four variables, namely, water content (WC), compactness, temperature, and content of soluble salts (CSS), are considered in predicting FR. A total of 360 pieces of data, collected from the experimental results, in which 30 pieces of data were selected randomly as the testing set data and the rest of the data were treated as the training set data, are applied to develop the prediction models. The predicted data from the models are compared with the experimental data. Then, the results of the two approaches are compared to obtain a relatively reliable model. Results indicate that the prediction model for the FR of saline soil in Nong'an can be successfully established using the GRNN method. Four new GRNN models are constructed for sensitivity analysis to assess the influence degree of the influencing factors, and the results indicate that water content is the most influential variable in the FR of the saline soil specimen, whereas content of soluble salts is the least influential variable.

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“…It also controls hydrologic processes such as groundwater recharge, infiltration and overland flow. Soil water holding capacity is one of the most important soil factors for plant growth, http (Merdun et al, 2006). Although PTFs offer a means to predict AWC, it is still necessary to determine which level of soil information would be sufficient for use with PTFs.…”

Section: Introductionmentioning

confidence: 99%

Applying Different Levels of Soil Information to Estimate Available Water Capacity

An¹

2016

Appl Ecol Env Res

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Abstract. Soil available water capacity (AWC) plays an important role in soil and water resource management, and is time-consuming and expensive to measure over large areas. AWC is often predicted with pedotransfer functions (PTFs) using easily measured soil properties (e.g., texture, bulk density and organic matter content). This study investigated the effectiveness of using neural network based-ROSETTA PTF to estimate AWC from four hierarchical levels of soil information: textural class (R1), texture (R2), texture and bulk density (R3), texture, bulk density, and water content at -33 kPa (θ -33 ) and -1500 kPa (θ -1500 ) (θ -33 and θ -1500 derived from Gupta-Larson PTF) (R4). A comprehensive dataset containing 58 soil samples collected from eastern China were used for evaluation. Results show that all the ROSETTA models except R1 estimate reliably the AWC. R2 and R3 produce similar estimates, both of which have only slightly poorer performance than R4. In this case soil texture is considered to be the most important soil property influencing AWC, making negligible the effects of the bulk density and organic matter content. It can be concluded that measuring soil texture as inputs for ROSETTA is the most affordable and reasonable approach to predict AWC in the study area.

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Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity

Cited by 139 publications

References 26 publications

An easily implemented agro-hydrological procedure with dynamic root simulation for water transfer in the crop–soil system: Validation and application

An easily implemented agro-hydrological procedure with dynamic root simulation for water transfer in the crop–soil system: Validation and application

Prediction of Frost‐Heaving Behavior of Saline Soil in Western Jilin Province, China, by Neural Network Methods

Applying Different Levels of Soil Information to Estimate Available Water Capacity

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