Abstract
Knowledge of the soil water retention (SWR) data is necessary for modeling soil water movement and assessing soil water holding capacity and availability. Since direct measurement is often time-consuming and costly, pedotransfer functions (PTFs) have been widely used to predict SWR data from basic soil physical properties. Considering the limited availability of PTFs derived from tropical soils, this paper developed artificial neural networks based on the pseudo-continuous approach (NN-PTFs) to predict SWR data for Brazilian soils. Natural logarithm of soil suction, ln (h), is considered as an extra input parameter in this approach. It enables to predict SWR data at any desired soil suction as it results in more extensive and useful database. The analysis was conducted on a previously compiled hydrophysical database for Brazilian soils representing a variety of soil compositions. The results demonstrated high accuracy and reliability in estimating SWR data, with an overall error of 0.045 cm³.cm−³, when incorporating both soil texture (i.e., clay, silt, and sand fractions) and soil structure-related properties (i.e., soil density, particle density and organic matter content) as input parameters. Moreover, the proposed NN-PTFs outperformed PTFs developed for temperate climates, as well as equation-based PTFs derived for specific tropical locals, particularly for weathered soils. The results highlight not only the potential of using NN-PTFs to predict pseudo-continuous SWR curve in preliminary studies, but also their flexibility and the benefits of not limiting the SWR data to a pre-defined function.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. The HYBRAS database is available here http://www.cprm.gov.br/en/Hydrology/HYBRAS-4208.html.
References
Al Majou H, Hassani B, Bruand A (2018) Transferability of continuous- and class-pedotransfer functions to predict water retention properties of semiarid syrian soils. Soil Use Manag 34:354–369. https://doi.org/10.1111/sum.12424
Angelaki A, Bota V, Chalkidis I (2023) Estimation of hydraulic parameters from the Soil. Water Characteristic Curve Sustainability 15:6714. https://doi.org/10.3390/su15086714
Barros AHC, de Jong Q (2014) Pedotransfer Functions for brazilian soils. Application of Soil Physics in environmental analyses. Springer International Publishing, Cham, pp 131–162. https://doi.org/10.1007/978-3-319-06013-2_6
Borgesen CD, Schaap MG (2005) Point and parameter pedotransfer functions for water retention predictions for danish soils. Geoderma 127:154–167. https://doi.org/10.1016/j.geoderma.2004.11.025
Botula YD, Cornelis WM, Baert G, Van Ranst E, Congo DR (2012) Agric Water Manag 111:1–10. https://doi.org/10.1016/j.agwat.2012.04.006
Botula YD, Nemes A, Mafuka P, Van Ranst E, Cornelis WM (2013) Prediction of Water Retention of Soils from the Humid Tropics by the nonparametric k -Nearest Neighbor Approach. Vadose Zo J 12. vzj2012.0123
Botula YD, Van Ranst E, Cornelis WM (2014) Pedotransfer Functions to predict Water Retention for Soils of the Humid Tropic: a review. R Bras Ci Solo 38:679–698
Bouma J (1989) Using Soil Survey Data for quantitative land evaluation. Advances in Soil Science. Springer, New York, NY, pp 177–213. https://doi.org/10.1007/978-1-4612-3532-3_4
Chin KB, Leong EC, Rahardjo H (2010) A simplified method to estimate the soil-water characteristic curve. Can Geotech J 47:1382–1400
Cui YJ (2022) Soil–atmosphere interaction in earth structures. J Rock Mech Geotech Eng V 14(1):35–49. https://doi.org/10.1016/j.jrmge.2021.11.004
Feuerharmel C, Gehling WYY, Bica AVD (2006) The use of filter-paper and suction-plate methods for determining the soil-water characteristic curve of undisturbed Colluvium Soils. Geotech Test J 29:419–425. https://doi.org/10.1520/GTJ14004
Fredlund DG (2006) Unsaturated soil mechanics in Engineering Practice. J Geotech Geoenvironmental Eng 132:286–321. https://doi.org/10.1061/(ASCE)10900241(2006)132:3(286)
Fredlund DG, Fredlund MD (2020) Application of ‘Estimation procedures’ in Unsaturated Soil mechanics. Geosciences 10:364. https://doi.org/10.3390/geosciences10090364
Hagan MT, Menhaj MB (1994) Training Feedforward networks with the Marquardt Algorithm. IEEE Trans Neural Networks 5:989–993. https://doi.org/10.1109/72.329697
Haghverdi A, Cornelis WM, Ghahraman B (2012) A pseudo-continuous neural network approach for developing water retention pedotransfer functions with limited data. J Hydrol 442–443:46–54. https://doi.org/10.1016/j.jhydrol.2012.03.036
Haghverdi A, Öztürk HS, Cornelis WM (2014) Revisiting the pseudo continuous pedotransfer function concept: impact of data quality and data mining method. Geoderma 226–227:31–38. https://doi.org/10.1016/j.geoderma.2014.02.026
Haghverdi A, Leib BG, Washington-Allen RA, Ayers PD, Buschermohle MJ (2015) High-resolution prediction of soil available water content within the crop root zone. J Hydrol 530:167–179. https://doi.org/10.1016/j.jhydrol.2015.09.061
Haghverdi A, Öztürk HS, Durner W (2018) Measurement and estimation of the soil water retention curve using the evaporation method and the pseudo continuous pedotransfer function. J Hydrol. https://doi.org/10.1016/j.jhydrol.2018.06.007
Haghverdi A, Öztürk HS, Durner W (2020) Studying Unimodal, Bimodal, PDI and Bimodal-PDI variants of multiple Soil Water Retention Models: II. Evaluation of Parametric Pedotransfer Functions against Direct fits. Water 2020 12:896. https://doi.org/10.3390/w12030896
Hodnet MG, Tomasella J (2002) Marked differences between van Genuchten soil water-retention parameters for temperate and tropical soils: a new water-retention pedo-transfer functions developed for tropical soils. Geoderma 108:155–180. https://doi.org/10.1016/S0016-7061(02)00105-2
IUSS Working Group WRB (2015) World reference base for soil resources 2014, update 2015. International soil classification system for naming soils and creating legends for soil maps, vol World Soil Resour Rep 106. FAO, Rome
Karube D, Kawai K (2001) The role of pore water in the mechanical behavior of unsaturated soils. Geotech Geolog Engrg 19(3):211–241
Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Model Softw 15:101–124. https://doi.org/10.1016/S1364-8152(99)00007-9
Maier HR, Jain A, Dandy GC, Sudheer KP (2010) Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions. Environ Model Softw 25:891–909. https://doi.org/10.1016/j.envsoft.2010.02.003
Masrouri F, Bicalho KV, Kawai K (2008) Laboratory Hydraulic Testing in Unsaturated Soils. Geotech Geol Eng 26:691–704. https://doi.org/10.1007/s10706-008-9202-7
Medrado E, Lima JEFW (2014) Development of pedotransfer functions for estimating water retention curve for tropical soils of the brazilian savanna. Geoderma Reg 1:59–66. https://doi.org/10.1016/j.geodrs.2014.08.003
Mermoud A, Xu D (2006) Comparative analysis of three methods to generate soil hydraulic functions Soil Tillage. Res 87:89–100. https://doi.org/10.1016/j.still.2005.02.034
Miguel MG, Bonder BH (2012) Soil-water characteristic curves obtained for a Colluvial and Lateritic Soil Profile considering the Macro and Micro Porosity. Geotech Geol Eng. https://doi.org/10.1007/s10706-012-9545-y
Minasny B, McBratney AB (2002) The Neuro-m method for fitting neural network Parametric Pedotransfer Functions. Soil Sci Soc Am J 66:352–361. https://doi.org/10.2136/sssaj2002.3520
Minasny B, McBratney AB, Bristow KL (1999) Comparison of different approaches to the development of pedotransfer functions for water-retention curves. Geoderma 93:225–253. https://doi.org/10.1016/S0016-7061(99)00061-0
Montzka C, Herbst M, Weihermüller L, Verhoef A, Vereecken H (2017) Earth Syst Sci Data 9:529–543. https://doi.org/10.5194/essd-9-529-2017. A global data set of soil hydraulic properties and sub-grid variability of soil water retention and hydraulic conductivity curves
Nguyen PM, Haghverdi A, de Pue J, Botula YD, Le KV, Waegeman W, Cornelis WM (2017) Biosyst Eng 153:12–27. https://doi.org/10.1016/j.biosystemseng.2016.10.013. Comparison of statistical regression and data-mining techniques in estimating soil water retention of tropical delta soils
Ottoni MV, Lopes-Assad MLRC, Pachepsky Y, Rotunno Filho OC (2014) A Hydrophysical database to develop Pedotransfer functions for brazilian soils: Challenges and Perspectives. In: Teixeira W, Ceddia M, Ottoni M, Donnagema G (eds) Application of Soil Physics in environmental analyses. Progress in Soil Science. Springer, Cham, pp 467–494. https://doi.org/10.1007/978-3-319-06013-2_20
Ottoni MV, Ottoni Filho TB, Schaap MG, Lopes-Assad MLRC, Rotunno Filho OC (2018) Hydrophysical Database for brazilian soils (HYBRAS) and Pedotransfer Functions for Water Retention. Vadose Zo J 17:170095. https://doi.org/10.2136/vzj2017.05.0095
Ottoni MV, Ottoni Filho TB, Lopes-Assad MLRC, Rotunno Filho OC (2019) Pedotransfer functions for saturated hydraulic conductivity using a database with temperate and tropical climate soils. J Hydrol 575:1345–1358. https://doi.org/10.1016/j.jhydrol.2019.05.050
Pachepsky YA, Timlin D, Varallyay G (1996) Artificial neural networks to Estimate Soil Water Retention from easily measurable data. Soil Sci Soc Am J 60:727–733. https://doi.org/10.2136/sssaj1996.03615995006000030007x
Pham K, Kim D, Yoon Y, Choi H (2019) Analysis of neural network based pedotransfer function for predicting soil water characteristic curve. Geoderma 351:92–102. https://doi.org/10.1016/j.geoderma.2019.05.013
Rawls WJ, Gish TJ, Brakensiek DL (1991) Estimating Soil Water Retention from Soil Physical Properties and characteristics. Communications in Soil Science and Plant Analysis. Springer, New York, NY, pp 213–234. https://doi.org/10.1007/978-1-4612-3144-8_5
Reichert JM, Albuquerque JA, Kaiser DR, Reinert DJ, Urach FL, Carlesso R (2009) Estimation of water retention and availability in soils of Rio Grande do sul. Rev Bras Ciência do Solo 33:1547–1560. https://doi.org/10.1590/S0100-06832009000600004
Reichert JM, Albuquerque JA, Solano Peraza JE, da Costa A (2020) Estimating water retention and availability in cultivated soils of southern Brazil. Geoderma Reg 21:e00277. https://doi.org/10.1016/j.geodrs.2020.e00277
Ren X, Kang J, Ren J, Chen X, Zhang M (2020) A method for estimating soil water characteristic curve with limited experimental data. Geoderma 360:114013. https://doi.org/10.1016/j.geoderma.2019.114013
Saha S, Gu F, Luo X, Lytton RL (2018) Prediction of soil-water characteristic curve for unbound material using Fredlund–Xing equation-based ANN Approach. J Mater Civ Eng 30:06018002. https://doi.org/10.1061/(ASCE)MT.1943-5533.0002241
Schaap MG, Bouten W (1996) Modeling water retention curves of sandy soils using neural networks. Water Resour Res 32:3033–3040. https://doi.org/10.1029/96WR02278
Schaap MG, Leij FJ, van Genuchten MT (1998) Neural network analysis for hierarchical prediction of Soil Hydraulic Properties. Soil Sci Soc Am J 62(4):847–855. https://doi.org/10.2136/sssaj1998.03615995006200040001x
Schaap MG, Leij FJ, van Genuchten MT (2001) Rosetta: a computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions. J Hydrol 251:163–176. https://doi.org/10.1016/S0022-1694(01)00466-8
Shahin MA (2013) Artificial Intelligence in Geotechnical Engineering. In: Yang X-S, Gandomi AH, Talatahari S, Alavi AH (eds) Metaheuristics in Water, Geotechnical and Transport Engineering. Elsevier, London, pp 169–204. https://doi.org/10.1016/B978-0-12-398296-4.00008-8
Tomasella J, Hodnett M (2004) Pedotransfer functions for tropical soils. Developments in Soil Science. Elsevier, pp 415–429. https://doi.org/10.1016/S0166-2481(04)30021-8
Tomasella J, Hodnett MG, Rossato L (2000) Pedotransfer Functions for the estimation of Soil Water Retention in brazilian soils. Soil Sci Soc Am J 64:327–338. https://doi.org/10.2136/sssaj2000.641327x
Tomasella J, Pachepsky Y, Crestana S, Rawls WJ (2003) Comparison of two techniques to develop Pedotransfer Functions for Water Retention. Soil Sci Soc Am J 67:1085–1092. https://doi.org/10.2136/sssaj2003.1085
van Genuchten MT (1980) A closed-form equation for Predicting the Hydraulic Conductivity of Unsaturated Soils. Soil Sci Soc Am J 44:892–898. https://doi.org/10.2136/sssaj1980.03615995004400050002x
Van Looy K, Bouma J, Herbst M, Koestel J, Minasny B, Mishra U, Montzka C, Nemes A, Pachepsky YA, Padarian J, Schaap MG, Tóth B, Verhoef A, Vanderborght J, van der Ploeg MJ, Weihermüller L, Zacharias S, Zhang Y, Vereecken H (2017) Pedotransfer Functions in Earth System Science: Challenges and Perspectives. Rev Geophys 55:1199–1256. https://doi.org/10.1002/2017RG000581
Vereecken H, Weynants M, Javaux M, Pachepsky Y, Schaap MG, van Genuchten MT (2010) Using Pedotransfer Functions to Estimate the van Genuchten-Mualem Soil Hydraulic Properties: a review. Vadose Zo J 9:795–820. https://doi.org/10.2136/vzj2010.0045
Weynants M, Vereecken H, Javaux M (2009) Revisiting Vereecken Pedotransfer Functions: introducing a closed-form hydraulic model. Vadose Zo J 8:86–95. https://doi.org/10.2136/vzj2008.0062
Wösten JHM, Pachepsky YA, Rawls WJ (2001) Pedotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic characteristics. J Hydrol 251:123–150. https://doi.org/10.1016/S0022-1694(01)00464-4
Yu H, Wilamowski BM (2011) Levenberg-Marquardt training. In: Intelligent systems. CRC Press, 12-1-12–16. https://doi.org/10.1201/9781315218427-12
Zhang Y, Schaap MG (2019) Estimation of saturated hydraulic conductivity with pedotransfer functions: a review. J Hydrol 575:1011–1030. https://doi.org/10.1016/j.jhydrol.2019.05.058
Funding
The authors are grateful to Brazilian agencies CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) and CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) for financial support.
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Lucas Totola, Katia Bicalho and Wilian Hisatugu. The initial draft of the manuscript was written by Lucas Totola, and all authors provided feedback on previous versions of the document. Subsequently, all authors reviewed and approved the final version of the manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare.
Additional information
Communicated by H. Babaie.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Totola, L.B., Bicalho, K.V. & Hisatugu, W.H. Artificial neural networks for predicting soil water retention data of various Brazilian soils. Earth Sci Inform 16, 3579–3595 (2023). https://doi.org/10.1007/s12145-023-01115-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12145-023-01115-3