Abstract
This study explores soil water characteristic curve (SWCC) prediction through informatics and machine learning. Utilizing these techniques, SWCC prediction was significantly simplified, enabled by the Orange.3 data mining software's integration of diverse soil properties. This integration eliminated the need for extensive programming, establishing a link between scientific insights and engineering applications. Limitations emerged in models relying solely on matric suction for SWCC prediction, evident through a Mean Absolute Error exceeding 0.08 and an R-squared value below 40% in the test dataset. To enhance accuracy, a comprehensive approach encompassing various soil properties, such as bulk density, organic carbon content, and micro-porosity characteristics, was employed. The Gradient Boosting algorithm excelled, yielding near-perfect SWCC estimations with RMSE and Pi values of 0.016 and 0.03, respectively. Likewise, AB, Random Forest, and Tree models displayed highly accurate predictions with RMSE and Pi values below 0.03 and 0.04, respectively. However, Neural Network, SVM, kNN, and Linear Regression models showed no improvements, even with added soil properties. Feature importance analysis highlighted matric suction's critical role in select models and soil micro-porosity characteristics' contribution to lowering RMSE by up to 0.04. These findings are pivotal in understanding errors in SWCC prediction, especially in cases of matric suctions surpassing the SWCC inflection point, with these errors, though present, minimally impacting model efficacy due to diminishing variations at high matric suctions.
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The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
References
Abiodun OI, Jantan A, Omolara AE, Dada KV, Mohamed NA, Arshad H (2018) State-of-the-art in artificial neural network applications: a survey. Heliyon 4(11). https://doi.org/10.1016/j.heliyon.2018.e00938
Achieng KO (2019) Modelling of soil moisture retention curve using machine learning techniques: artificial and deep neural networks vs support vector regression models. Comput Geosci 133:104320. https://doi.org/10.1016/j.cageo.2019.104320
Ahangar-Asr A, Johari A, Javadi AA (2012) An evolutionary approach to modelling the soil–water characteristic curve in unsaturated soils. Comput Geosci 43:25–33. https://doi.org/10.1016/j.cageo.2012.02.021
Amanabadi S, Vazirinia M, Vereecken H, Vakilian KA, Mohammadi MH (2019) Comparative study of statistical, numerical and machine learning-based pedotransfer functions of water retention curve with particle size distribution data. Eurasian Soil Sci 52:1555–1571. https://doi.org/10.1134/S106422931930001X
Bai J, Cui Q, Zhang W, Meng L (2019) An approach for downscaling SMAP soil moisture by combining Sentinel-1 SAR and MODIS data. Remote Sens 11(23):2736. https://doi.org/10.3390/rs11232736
Bakhshi A, Heidari A, Mohammadi MH, Ghezelbash E (2023) Estimation of water retention at low matric suctions using the micromorphological characteristics of soil pores. Euras Soil Sci 1064–2293. https://doi.org/10.1134/S1064229323600549
Belgiu M, Drăguţ L (2016) Random forest in remote sensing: a review of applications and future directions. ISPRS J Photogramm Remote Sens 114:24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011
Børgesen CD, Iversen BV, Jacobsen OH, Schaap MG (2008) Pedotransfer functions estimating soil hydraulic properties using different soil parameters. Hydrol Process Int J 22(11):1630–1639. https://doi.org/10.1002/hyp.6731
Cai Y, Zheng W, Zhang X, Zhangzhong L, Xue X (2019) Research on soil moisture prediction model based on deep learning. PLoS ONE 14(4):e0214508. https://doi.org/10.1371/journal.pone.0214508
Cheng Y, Zhou WH, Xu T (2022) Tunneling-induced settlement prediction using the hybrid feature selection method for feature optimization. Transp Geotechn 36:100808. https://doi.org/10.1016/j.trgeo.2022.100808
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297. https://doi.org/10.1007/BF00994018
Cresswell HP, Green TW, McKenzie NJ (2008) The adequacy of pressure plate apparatus for determining soil water retention. Soil Sci Soc Am J 72(1):41–49. https://doi.org/10.2136/sssaj2006.0182
Dane JH, Hopmans JW, Topp GC (2002) Pressure plate extractor. Methods Soil Anal Part 4:688–690
Demir Z (2020) Alleviation of adverse effects of sodium on soil physicochemical properties by application of vermicompost. Compost Sci Util 28(2):100–116. https://doi.org/10.1080/1065657X.2020.1789011
Dexter AR, Czyż EA, Richard G, Reszkowska A (2008) A user-friendly water retention function that takes account of the textural and structural pore spaces in soil. Geoderma 143(3–4):243–253. https://doi.org/10.1016/j.geoderma.2007.11.010
Diao W, Liu G, Zhang H, Hu K, Jin X (2021) Influences of soil bulk density and texture on estimation of surface soil moisture using spectral feature parameters and an artificial neural network algorithm. Agriculture 11(8):710. https://doi.org/10.3390/agriculture11080710
Dobarco MR, Bourennane H, Arrouays D, Saby NP, Cousin I, Martin MP (2019) Uncertainty assessment of GlobalSoilMap soil available water capacity products: a French case study. Geoderma 344:14–30. https://doi.org/10.1016/j.geoderma.2019.02.036
Eben M, Cithuraj K, Justus S, Bhagavathsingh J (2020) Synthesis and characterization of stretchable IPN polymers from biodegradable resins incorporated with styrene and methyl methacrylate monomers for enhanced mechanical strength. Eur Polym J 138:109957. https://doi.org/10.1016/j.eurpolymj.2020.109957
Fredlund DG, Rahardjo H (1993) An overview of unsaturated soil behaviour. Geotechnical special publication 1–1
Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In: icml, vol. 96, pp 148–156
Gee GW, Or D (2002) 2.4 Particle-size analysis. Methods Soil Anal: Part 4 Phys Methods 5:255–293. https://doi.org/10.2136/sssabookser5.4.c12
Grossman RB, Reinsch TG (2002) 2.1 Bulk density and linear extensibility. Methods Soil Anal: Part 4 Phys Methods 5:201–228. https://doi.org/10.2136/sssabookser5.4.c9
Guevara M, Vargas R (2019) Downscaling satellite soil moisture using geomorphometry and machine learning. PLoS ONE 14(9):e0219639. https://doi.org/10.1371/journal.pone.0219639
Gunarathna MP, Sakai K, Nakandakari T, Momii K, Kumari MN (2019) Machine learning approaches to develop pedotransfer functions for tropical Sri Lankan soils. Water 11(9):1940. https://doi.org/10.3390/w11091940
Guo G, Wang H, Bell D, Bi Y, Greer K (2003) KNN model-based approach in classification. In: On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE: OTM Confederated International Conferences, CoopIS, DOA, and ODBASE 2003, Catania, Sicily, Italy, November 3–7, 2003. Proceedings. Springer, Berlin Heidelberg, pp 986–996. https://doi.org/10.1007/978-3-540-39964-3_62
Hastie T, Tibshirani R, Friedman JH, Friedman JH (2009) The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1–758). Springer, New York. https://doi.org/10.1007/978-0-387-21606-5
Hopmans JW, Dane JH (1986) Temperature dependence of soil hydraulic properties. Soil Sci Soc Am J 50(1):4–9
Huang G, Su X, Rizwan MS, Zhu Y, Hu H (2016) Chemical immobilization of Pb, Cu, and Cd by phosphate materials and calcium carbonate in contaminated soils. Environ Sci Pollut Res 23:16845–16856. https://doi.org/10.1007/s11356-016-6885-9
Hwang SI, Powers SE (2003) Lognormal distribution model for estimating soil water retention curves for sandy soils. Soil Sci 168(3):156–166. https://doi.org/10.1097/01.ss.0000058888.60072.e3
Im J, Park S, Rhee J, Baik J, Choi M (2016) Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches. Environ Earth Sci 75:1–19. https://doi.org/10.1007/s12665-016-5917-6
Jalal FE, Xu Y, Iqbal M, Javed MF, Jamhiri B (2021) Predictive modeling of swell-strength of expansive soils using artificial intelligence approaches: ANN, ANFIS and GEP. J Environ Manag 289:112420. https://doi.org/10.1016/j.jenvman.2021.112420
Lamorski K, Šimůnek J, Sławiński C, Lamorska J (2017) An estimation of the main wetting branch of the soil water retention curve based on its main drying branch using the machine learning method. Water Resour Res 53(2):1539–1552. https://doi.org/10.1002/2016WR019533
Leij FJ, Romano N, Palladino M, Schaap MG, Coppola A (2004) Topographical attributes to predict soil hydraulic properties along a hillslope transect. Water Resour Res 40(2). https://doi.org/10.1029/2002WR001641
Li M, Zhang P, Adeel M, Guo Z, Chetwynd AJ, Ma C, Rui Y (2021) Physiological impacts of zero valent iron, Fe3O4 and Fe2O3 nanoparticles in rice plants and their potential as Fe fertilizers. Environ Pollut 269:116134. https://doi.org/10.1016/j.envpol.2020.116134
Liu Y, Yang Y, Jing W, Yue X (2017) Comparison of different machine learning approaches for monthly satellite-based soil moisture downscaling over Northeast China. Remote Sens 10(1):31. https://doi.org/10.3390/rs10010031
Long D, Bai L, Yan L, Zhang C, Yang W, Lei H, Shi C (2019) Generation of spatially complete and daily continuous surface soil moisture of high spatial resolution. Remote Sens Environ 233:111364. https://doi.org/10.1016/j.rse.2019.111364
Meskini-Vishkaee F, Mohammadi MH, Vanclooster M (2014) Predicting the soil moisture retention curve, from soil particle size distribution and bulk density data using a packing density scaling factor. Hydrol Earth Syst Sci 18(10):4053–4063. https://doi.org/10.5194/hess-18-4053-2014
Mi W, Sun Y, Xia S, Zhao H, Mi W, Brookes PC, Wu L (2018) Effect of inorganic fertilizers with organic amendments on soil chemical properties and rice yield in a low-productivity paddy soil. Geoderma 320:23–29. https://doi.org/10.1016/j.geoderma.2018.01.016
Mohammadi MH, Meskini-Vishkaee F (2012) Predicting the film and lens water volume between soil particles using particle size distribution data. J Hydrol 475:403–414. https://doi.org/10.1016/j.jhydrol.2012.10.024
Molnar C (2020) Interpretable machine learning. Lulu. com
Myles AJ, Feudale RN, Liu Y, Woody NA, Brown SD (2004) An introduction to decision tree modeling. J Chemometr 18(6):275–285. https://doi.org/10.1002/cem.873
Natekin A, Knoll A (2013) Gradient boosting machines, a tutorial. Front Neurorobot 7:21. https://doi.org/10.3389/fnbot.2013.00021
Nguyen PM, Van Le K, Cornelis WM (2014) Using categorical soil structure information to improve soil water retention estimates of tropical delta soils. Soil Res 52(5):443–452. https://doi.org/10.1071/SR13256
Nguyen PM, Haghverdi A, De Pue J, Botula YD, Le KV, Waegeman W, Cornelis WM (2017) Comparison of statistical regression and data-mining techniques in estimating soil water retention of tropical delta soils. Biosys Eng 153:12–27. https://doi.org/10.1016/j.biosystemseng.2016.10.013
Or D, Tuller M (1999) Liquid retention and interfacial area in variably saturated porous media: Upscaling from single-pore to sample-scale model. Water Resour Res 35(12):3591–3605. https://doi.org/10.1029/1999WR900262
Pachepsky YA, Rawls WJ, Lin HS (2006) Hydropedology and pedotransfer functions. Geoderma 131(3–4):308–316. https://doi.org/10.1016/j.geoderma.2005.03.012
Padarian J, Minasny B, McBratney AB (2019) Machine learning and soil sciences: a review aided by machine learning tools. https://doi.org/10.5194/soil-6-35-2020
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
Pham K, Kim D, Le CV, Won J (2023) Machine learning-based pedotransfer functions to predict soil water characteristics curves. Transp Geotechn 101052. https://doi.org/10.1016/j.trgeo.2023.101052
Rani A, Kumar N, Kumar J, Sinha NK (2022) Machine learning for soil moisture assessment. In: Deep learning for sustainable agriculture. Academic Press, pp 143–168. https://doi.org/10.1016/B978-0-323-85214-2.00001-X
Rastgou M, Bayat H, Mansoorizadeh M, Gregory AS (2020) Estimating the soil water retention curve: comparison of multiple nonlinear regression approach and random forest data mining technique. Comput Electron Agric 174:105502. https://doi.org/10.1016/j.compag.2020.105502
Rhoades JD (1983) Soluble salts. Methods Soil Anal: Part 2 Chem Microbiol Propert 9:167–179. https://doi.org/10.2134/agronmonogr9.2.2ed.c10
Rhoades JD (1996) Salinity: electrical conductivity and total dissolved solids. Methods Soil Anal: Part 3 Chem Methods 5:417–435. https://doi.org/10.2136/sssabookser5.3.c14
Ringrose-Voase AJ (1996) Measurement of soil macropore geometry by image analysis of sections through impregnated soil. Plant Soil 183:27–47. https://doi.org/10.1007/BF02185563
Sarkar D, De DK, Das R, Mandal B (2014) Removal of organic matter and oxides of iron and manganese from soil influences boron adsorption in soil. Geoderma 214:213–216. https://doi.org/10.1016/j.geoderma.2013.09.009
Schindler U, Mueller L, da Veiga M, Zhang Y, Schlindwein S, Hu C (2012) Comparison of water-retention functions obtained from the extended evaporation method and the standard methods sand/kaolin boxes and pressure plate extractor. J Plant Nutr Soil Sci 175(4):527–534. https://doi.org/10.1002/jpln.201100325
Senyurek V, Lei F, Boyd D, Kurum M, Gurbuz AC, Moorhead R (2020) Machine learning-based CYGNSS soil moisture estimates over ISMN sites in CONUS. Remote Sens 12(7):1168. https://doi.org/10.3390/rs12071168
Sermet Y, Demir I (2019) Towards an information centric flood ontology for information management and communication. Earth Sci Inf 12(4):541–551. https://doi.org/10.1007/s12145-019-00398-9
Shahraeeni E, Or D (2010) Thermo-evaporative fluxes from heterogeneous porous surfaces resolved by infrared thermography. Water Resour Res 46(9). https://doi.org/10.1029/2009WR008455
Srivastava PK, Han D, Ramirez MR, Islam T (2013) Machine learning techniques for downscaling SMOS satellite soil moisture using MODIS land surface temperature for hydrological application. Water Resour Manag 27:3127–3144. https://doi.org/10.1007/s11269-013-0337-9
Thomas GW (1996) Soil pH and soil acidity. Methods Soil Anal: Part 3 Chem Methods 5:475–490. https://doi.org/10.2136/sssabookser5.3.c16
Tuller M, Or D (2001) Hydraulic conductivity of variably saturated porous media: film and corner flow in angular pore space. Water Resour Res 37(5):1257–1276. https://doi.org/10.1029/2000WR900328
Tuller M, Or D (2005) Water films and scaling of soil characteristic curves at low water contents. Water Resour Res 41(9). https://doi.org/10.1029/2005WR004142
Tuller M, Or D, Dudley LM (1999) Adsorption and capillary condensation in porous media: liquid retention and interfacial configurations in angular pores. Water Resour Res 35(7):1949–1964. https://doi.org/10.1029/1999WR900098
Tuller M, Or D, Hillel D (2004) Retention of water in soil and the soil water characteristic curve. Encycl Soils Environ 4:278–289
Van Genuchten MT (1980) A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci Soc Am J 44(5):892–898. https://doi.org/10.2136/sssaj1980.03615995004400050002x
Vereecken H, Weynants M, Javaux M, Pachepsky Y, Schaap MG, Genuchten MV (2010) Using pedotransfer functions to estimate the van Genuchten-Mualem soil hydraulic properties: a review. Vadose Zone J 9(4):795–820. https://doi.org/10.2136/vzj2010.0045
Wadoux AMC, Molnar C (2022) Beyond prediction: methods for interpreting complex models of soil variation. Geoderma 422:115953. https://doi.org/10.1016/j.geoderma.2022.115953
Walkley A, Black IA (1934) An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Sci 37(1):29–38. https://doi.org/10.1016/j.geoderma.2021.115293
Wang S, Fan W, Zhu Y, Zhang J (2021) The effects of fitting parameters in best fit equations in determination of soil-water characteristic curve and estimation of hydraulic conductivity function. Rhizosphere 17:100291. https://doi.org/10.1016/j.rhisph.2020.100291
Wang C, Cai G, Liu X, Wu M (2022) Prediction of soil thermal conductivity based on Intelligent computing model. Heat Mass Transf 58(10):1695–1708. https://doi.org/10.1007/s00231-022-03209-y
Wei T, Fan W, Yu N, Wei YN (2019) Three-dimensional microstructure characterization of loess based on a serial sectioning technique. Eng Geol 261:105265. https://doi.org/10.1016/j.enggeo.2019.105265
Zappa L, Forkel M, Xaver A, Dorigo W (2019) Deriving field scale soil moisture from satellite observations and ground measurements in a hilly agricultural region. Remote Sens 11(22):2596. https://doi.org/10.1016/j.compgeo.2011.11.010
Zhai Q, Rahardjo H (2012) Determination of soil–water characteristic curve variables. Comput Geotech 42:37–43. https://doi.org/10.1016/j.compgeo.2011.11.010
Zhang N, Zou H, Zhang L, Puppala AJ, Liu S, Cai G (2020) A unified soil thermal conductivity model based on artificial neural network. Int J Therm Sci 155:106414. https://doi.org/10.1016/j.ijthermalsci.2020.106414
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Aida Bakhshi: made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data; or the creation of new software used in the work.
Parisa Alamdari: drafted the work or revised it critically for important intellectual content.
Ahmad Heidari: made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data; or the creation of new software used in the work.
Mohammad Hossein Mohammadi: drafted the work or revised it critically for important intellectual content.
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Bakhshi, A., Alamdari, P., Heidari, A. et al. Estimating soil–water characteristic curve (SWCC) using machine learning and soil micro-porosity analysis. Earth Sci Inform 16, 3839–3860 (2023). https://doi.org/10.1007/s12145-023-01131-3
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DOI: https://doi.org/10.1007/s12145-023-01131-3