Evaluation and Development of Pedotransfer Functions for Predicting Saturated Hydraulic Conductivity for Mexican Soils

Abstract

In the present work, we evaluate the prediction capability of six pedotransfer functions (PTFs), reported in the literature, for the saturated hydraulic conductivity estimations (KS). We used a database with 900 measured samples obtained from the Irrigation District 023, in San Juan del Rio, Queretaro, Mexico. Additionally, six new PTFs were constructed for KS from clay percentage, bulk density, and saturation water content data. The results show, for the evaluated models, that one model presents an overestimation for KS > 0.5 cm h−1 values, three models have an underestimation for KS > 1.0 cm h−1, and two models have a good correlation (R2 > 0.98) but more than three parameters are necessary. Nevertheless, the last two models require 3–4 parameters in order to obtain optimization. On the other hand, the models proposed in this work have a similar correlation with fewer parameters. The fit is seen to be much better than using the existing ones, achieving a correlation of R2 = 0.9822 with only one variable and R2 = 0.9901 when we use two.

Saturated Hydraulic Conductivity Estimation Using Artificial Neural Networks

“…This result can be explained by the following reasons. Based on the Principal Component Analysis of [23], we noted that the principal variables contributing the most to the sample were K S , the percentage of clay, θ S , PWP, and FC, which is supported by the importance plots ( Figure 6). Additionally, for the 900 analyzed samples contained in 10 of the 12 existing types of soil (according to the USDA Textural Soil Classification), they showed that the infiltration rate depended directly on the percentage of clay and the ρ a .…”

“…In this work, we developed five Artificial Neural Networks in order to calculate the saturated hydraulic conductivity based on the sample used by [23]. All networks consist of one input layer, two hidden layers, and one output layer.…”

“…This work 0.0413 0.9780 ANN Tamari et al [31] 0.0707 NA ANN Brakensiek et al [9] 0.1370 0.9953 PTF Erzin et al [12] 0.1700 0.9970 ANN Saxton et al [32] 0.1895 0.9915 PTF Parasuraman et al [33] 0.1900 NA ANN Trejo-Alonso et al [23] 0.1983 0.9901 PTF Cosby et al [34] 0.4325 0.9546 PTF Ahuja et al [35] 0.6498 0.8910 PTF Schaap & Leij [36] 0.7130 NA ANN Vereecken et al [37] 0.7143 0.9307 PTF Minasny et al [38] 0.7330 NA ANN Ferrer-Julià et al [39] 1.3018 0.4083 PTF Merdun et al [40] 3.5110 0.…”

“…An extensive and detailed database description can be found at [23]. In summary, the database used in this study was developed from samplings in 900 plots at Irrigation District 023, San Juan del Rio Queretaro.…”

Evaluation and Development of Pedotransfer Functions and Artificial Neural Networks to Saturation Moisture Content Estimation

“…Based on the study of Trejo-Alonso et al [54], eight new PTFs were developed based on the results of a Principal Component Analysis (PCA). This decision was made due to the fact that only the PTF published by Vereecken et al [36] could be tested and the lack of PTFs for θ s in the literature.…”