Saturated Hydraulic Conductivity Estimation Using Artificial Neural Networks

Trejo-Alonso, Josué; Fuentes, Carlos; Chávez, Carlos; Quevedo, Antonio; Gutierrez-Lopez, Alfonso; González-Correa, Brandon

doi:10.3390/w13050705

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Saturated Hydraulic Conductivity Estimation Using Artificial Neural Networks

^2,*

^1,*

Alfonso Gutierrez-Lopez

and

Brandon González-Correa

⁴

Water Research Center, Department of Irrigation and Drainage Engineering, Autonomous University of Queretaro, Cerro de las Campanas SN, Col. Las Campanas, Queretaro 76010, Mexico

Mexican Institute of Water Technology, Paseo Cuauhnáhuac Núm. 8532, Jiutepec 62550, Mexico

Water Research Center, Centro de Investigaciones del Agua-Queretaro (CIAQ), International Flood Initiative, Latin-American and the Caribbean Region (IFI-LAC), International Hydrological Programme (IHP-UNESCO), Universidad Autonoma de Queretaro, Queretaro 76010, Mexico

⁴

Engineering Faculty, Autonomous University of Queretaro, Cerro de las Campanas SN, Col. Las Campanas, Queretaro 70610, Mexico

Authors to whom correspondence should be addressed.

Academic Editors: George Kargas, Petros Kerkides and Paraskevi Londra

Water 2021, 13(5), 705; https://doi.org/10.3390/w13050705

Received: 10 February 2021 / Revised: 26 February 2021 / Accepted: 27 February 2021 / Published: 5 March 2021

(This article belongs to the Special Issue Study of the Soil Water Movement in Irrigated Agriculture)

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Abstract

In the present work, we construct several artificial neural networks (varying the input data) to calculate the saturated hydraulic conductivity (K

_{S}

) using a database with 900 measured samples obtained from the Irrigation District 023, in San Juan del Rio, Queretaro, Mexico. All of them were constructed using two hidden layers, a back-propagation algorithm for the learning process, and a logistic function as a nonlinear transfer function. In order to explore different arrays for neurons into hidden layers, we performed the bootstrap technique for each neural network and selected the one with the least Root Mean Square Error (RMSE) value. We also compared these results with pedotransfer functions and another neural networks from the literature. The results show that our artificial neural networks obtained from 0.0459 to 0.0413 in the RMSE measurement, and 0.9725 to 0.9780 for R

^{2}

, which are in good agreement with other works. We also found that reducing the amount of the input data offered us better results. View Full-Text

Keywords: modeling water flow; gravity irrigation; infiltration process; artificial intelligence

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MDPI and ACS Style

Trejo-Alonso, J.; Fuentes, C.; Chávez, C.; Quevedo, A.; Gutierrez-Lopez, A.; González-Correa, B. Saturated Hydraulic Conductivity Estimation Using Artificial Neural Networks. Water 2021, 13, 705. https://doi.org/10.3390/w13050705

AMA Style

Trejo-Alonso J, Fuentes C, Chávez C, Quevedo A, Gutierrez-Lopez A, González-Correa B. Saturated Hydraulic Conductivity Estimation Using Artificial Neural Networks. Water. 2021; 13(5):705. https://doi.org/10.3390/w13050705

Chicago/Turabian Style

Trejo-Alonso, Josué, Carlos Fuentes, Carlos Chávez, Antonio Quevedo, Alfonso Gutierrez-Lopez, and Brandon González-Correa. 2021. "Saturated Hydraulic Conductivity Estimation Using Artificial Neural Networks" Water 13, no. 5: 705. https://doi.org/10.3390/w13050705

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