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Evaluation of deep machine learning-based models of soil cumulative infiltration

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Abstract

Infiltration is the process by which water enters the soil, and it plays a significant role in the hydrologic cycle. Direct measurement of infiltration is time consuming; however, empirical and physical models are inaccurate. In this study, we compared the results of a deep learning-based convolutional neural network (CNN) algorithm with those of models based on standalone support vector regression (SVR) and group method of data handling (GMDH) algorithms. We also tested a hybridized SVR and GMDH-based model enhanced by three metaheuristic algorithms: gray wolf optimization (GWO), bat algorithm (BA), and particle swarm optimization (PSO). Measured variables including measurement time; sand, clay and silt contents; bulk density; and soil moisture content were used as model inputs to predict cumulative infiltration as an output. Finally, models were evaluated using the Pearson correlation coefficient, root mean square error, mean absolute error, Nash–Sutcliffe efficiency, percentage of bias, and relative error. Weighting of the model input parameters/variables demonstrated that time was the most effective variable for predicting cumulative infiltration, whereas the most powerful parameter input combination required all seven variables. The prediction model evaluation results showed that the hybridized models improved the predictive capability of the standalone models.

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Data will be provided for reasonable and formal request upon of corresponding author.

References

  • Abdalrahman G, Lai SH, Kumar P, Ahmed AN, Sherif M, Sefelnasr A, Chau KW, Elshafie A (2022) Modeling the infiltration rate of wastewater infiltration basins considering water quality parameters using different artificial neural network techniques. Engineering Applications of Computational Fluid Mechanics 16(1)397–421. https://doi.org/10.1080/19942060.2021.2019126

  • Abdel-Aal RE, Elhadidy MA, Shaahid S (2009) Modeling and forecasting the mean hourly wind speed time series using GMDH-based abductive networks. Renewable Energy 34:1686–1699

    Article  Google Scholar 

  • Abrahart RJ, Anctil F, Coulibaly P, Dawson CW, Mount NJ, See LM, Shamseldin AY, Solomatine DP, Toth E, Wilby RL (2012) Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting. Prog Phys Geogr 36:480–513

    Article  Google Scholar 

  • Ahuja L, Ma L, Green T (2010) Effective soil properties of heterogeneous areas for modeling infiltration and redistribution. Soil Sci Soc Am J 74:1469–1482

    Article  Google Scholar 

  • Akande KO, Owolabi TO, Olatunji SO, AbdulRaheem A (2017) A hybrid particle swarm optimization and support vector regression model for modelling permeability prediction of hydrocarbon reservoir. J Petrol Sci Eng 150:43–53

    Article  Google Scholar 

  • Alaoui A, Caduff U, Gerke HH, Weingartner R (2011) Preferential flow effects on infiltration and runoff in grassland and forest soils. Vadose Zone Journal 10:367–377. https://doi.org/10.2136/vzj2010.0076

    Article  Google Scholar 

  • Assouline S (2013) Infiltration into soils: Conceptual approaches and solutions. Water Resour Res 49:1755–1772

    Article  Google Scholar 

  • Assouline S, Mualem Y (2006) Runoff from heterogeneous small bare catchments during soil surface sealing. Water Resour Res 42

  • Ayele GT, Teshale EZ, Yu B, Rutherfurd ID, Jeong J (2017) Streamflow and sediment yield prediction for watershed prioritization in the Upper Blue Nile River Basin, Ethiopia. Water 9:782

    Article  Google Scholar 

  • Azimi H, Bonakdari H, Ebtehaj I (2019) Design of radial basis function-based support vector regression in predicting the discharge coefficient of a side weir in a trapezoidal channel. Appl Water Sci 9:1–12

    Article  Google Scholar 

  • Bagheri-Esfe H, Safikhani H (2017) Modeling of deviation angle and performance losses in wet steam turbines using GMDH-type neural networks. Neural Comput Appl 28:489–501

    Article  Google Scholar 

  • Bansal JC, Singh P, Saraswat M, Verma A, Jadon SS, Abraham A (2011) Inertia weight strategies in particle swarm optimization. 2011 Third world congress on nature and biologically inspired computing. IEEE. pp 633–640

  • Beven K, Germann P (1982) Macropores and water flow in soils. Water Resour Res 18:1311–1325. https://doi.org/10.1029/WR018i005p01311

    Article  Google Scholar 

  • Beven KJ, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology / Un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant. Hydrol Sci Bull 24:43–69. https://doi.org/10.1080/02626667909491834

    Article  Google Scholar 

  • Brutsaert W (2005) Hydrology: an introduction. Cambridge University Press

    Book  Google Scholar 

  • Bui DT, Pradhan B, Nampak H, Bui Q-T, Tran Q-A, Nguyen Q-P (2016) Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS. J Hydrol 540:317–330

    Article  Google Scholar 

  • Bui DT, Tsangaratos P, Nguyen V-T, Liem NV, Trinh PT (2020) Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment. CATENA 188:104426. https://doi.org/10.1016/j.catena.2019.104426

    Article  Google Scholar 

  • Carlisle A, Dozier G (2001) An off-the-shelf PSO [C/CD]. Workshop Particle Swarm Optimization, Indianapolis

  • Cheik S, Bottinelli N, Sukumar R, Jouquet P (2018) Fungus-growing termite foraging activity increases water infiltration but only slightly and temporally impacts soil physical properties in southern Indian woodlands. Eur J Soil Biol 89:20–24

    Article  Google Scholar 

  • Chen W, Hong H, Panahi M, Shahabi H, Wang Y, Shirzadi A, Pirasteh S, Alesheikh AA, Khosravi K, Panahi S (2019a) Spatial prediction of landslide susceptibility using gis-based data mining techniques of anfis with whale optimization algorithm (woa) and grey wolf optimizer (gwo). Appl Sci 9:3755

    Article  Google Scholar 

  • Chen W, Panahi M, Khosravi K, Pourghasemi HR, Rezaie F, Parvinnezhad D (2019b) Spatial prediction of groundwater potentiality using ANFIS ensembled with teaching-learning-based and biogeography-based optimization. J Hydrol 572:435–448

    Article  Google Scholar 

  • Chen W, Panahi M, Pourghasemi HR (2017) Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling. CATENA 157:310–324

    Article  Google Scholar 

  • Cherkassky V, Ma Y (2004) Practical selection of SVM parameters and noise estimation for SVM regression. Neural Netw 17:113–126

    Article  Google Scholar 

  • Choubin B, Darabi H, Rahmati O, Sajedi-Hosseini F, Kløve B (2018) River suspended sediment modelling using the CART model: a comparative study of machine learning techniques. Sci Total Environ 615:272–281

    Article  Google Scholar 

  • Das SK, Samui P, Sabat AK (2012) Prediction of field hydraulic conductivity of clay liners using an artificial neural network and support vector machine. Int J Geomech 12:606–611

    Article  Google Scholar 

  • Demand D, Selker JS, Weiler M (2019) Influences of macropores on infiltration into seasonally frozen soil. Vadose Zone Journal 18:1–14

    Article  Google Scholar 

  • Di Prima S, Marrosu R, Lassabatere L, Angulo-Jaramillo R, Pirastru M (2018) In situ characterization of preferential flow by combining plot-and point-scale infiltration experiments on a hillslope. J Hydrol 563:633–642

    Article  Google Scholar 

  • Dunkerley D (2012) Effects of rainfall intensity fluctuations on infiltration and runoff: rainfall simulation on dryland soils, Fowlers Gap, Australia. Hydrol Process 26:2211–2224

    Article  Google Scholar 

  • Ebtehaj I, Bonakdari H (2016) A support vector regression-firefly algorithm-based model for limiting velocity prediction in sewer pipes. Water Sci Technol 73:2244–2250

    Article  Google Scholar 

  • Ebtehaj I, Bonakdari H, Es-haghi MS (2019) Design of a hybrid ANFIS–PSO model to estimate sediment transport in open channels. Iran J Sci Technol Trans Civ Eng 43:851–857

    Article  Google Scholar 

  • Ebtehaj I, Bonakdari H, Moradi F, Gharabaghi B, Khozani ZS (2018) An integrated framework of Extreme Learning Machines for predicting scour at pile groups in clear water condition. Coast Eng 135:1–15

    Article  Google Scholar 

  • Ebtehaj I, Bonakdari H, Shamshirband S (2016) Extreme learning machine assessment for estimating sediment transport in open channels. Eng Comput 32:691–704

    Article  Google Scholar 

  • El-Zonkoly A (2006) Optimal tuning of power systems stabilizers and AVR gains using particle swarm optimization. Expert Syst Appl 31:551–557

    Article  Google Scholar 

  • Emary E, Yamany W, Hassanien AE, Snasel V (2015) Multi-objective gray-wolf optimization for attribute reduction. Procedia Comput Sci 65:623–632

    Article  Google Scholar 

  • Fang Z, Wang Y, Peng L, Hong H (2020) Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping. Comput Geosci 139:104470. https://doi.org/10.1016/j.cageo.2020.104470

    Article  Google Scholar 

  • Gai W, Qu C, Liu J, Zhang J (2018) A novel hybrid meta-heuristic algorithm for optimization problems. Syst Sci Control Eng 6:64–73

    Article  Google Scholar 

  • Gao Y, Peng L, Li F, Liu M, Liu W (2013) Opposition-based learning fully informed particle swarm optimizer without velocity. International Conference in Swarm Intelligence. Springer, pp 79–86

  • Green W, Ampt G (1911) The flow of air and water through soils. J Agric Sci 4:1–24

    Google Scholar 

  • Guha D, Roy PK, Banerjee S (2016) Load frequency control of large scale power system using quasi-oppositional grey wolf optimization algorithm. Eng Sci Technol Int J 19:1693–1713

    Google Scholar 

  • Hameed IA, Bye RT, Osen OL (2016) Grey wolf optimizer (GWO) for automated offshore crane design. 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, pp 1–6

  • Hardie M, Almajmaie A (2019) Measuring and estimating the hydrological properties of a soil crust. J Hydrol 574:12–22

    Article  Google Scholar 

  • Harmel RD, Smith PK (2007) Consideration of measurement uncertainty in the evaluation of goodness-of-fit in hydrologic and water quality modeling. J Hydrol 337:326–336

    Article  Google Scholar 

  • Hong W-C, Dong Y, Zhang WY, Chen L-Y, Panigrahi B (2013) Cyclic electric load forecasting by seasonal SVR with chaotic genetic algorithm. Int J Electr Power Energy Syst 44:604–614

    Article  Google Scholar 

  • Horton RE (1939) Analysis of runoff-plat experiments with varying infiltration-capacity. EOS Trans Am Geophys Union 20:693–711

    Article  Google Scholar 

  • Ilek A, Kucza J, Witek W (2019) Using undisturbed soil samples to study how rock fragments and soil macropores affect the hydraulic conductivity of forest stony soils: Some methodological aspects. J Hydrol 570:132–140

    Article  Google Scholar 

  • Ivakhnenko AG (1971) Polynomial theory of complex systems. IEEE transactions on Systems, Man, and Cybernetics, pp 364–378

  • Jie-Sheng W, Shu-Xia L (2019) An Improved Grey Wolf Optimizer Based on Differential Evolution and Elimination Mechanism. Scientific Reports (Nature Publisher Group) 9

  • Khairuzzaman AKM, Chaudhury S (2017) Multilevel thresholding using grey wolf optimizer for image segmentation. Expert Syst Appl 86:64–76

    Article  Google Scholar 

  • Khosravi K, Cooper JR, Daggupati P, Pham BT, Bui DT (2020) Bedload transport rate prediction: Application of novel hybrid data mining techniques. J Hydrol 124774

  • Khosravi K, Daggupati P, Alami MT, Awadh SM, Ghareb MI, Panahi M, Pham BT, Rezaie F, Qi C, Yaseen ZM (2019) Meteorological data mining and hybrid data-intelligence models for reference evaporation simulation: A case study in Iraq. Comput Electron Agric 167:105041

    Article  Google Scholar 

  • Khosravi K, Pham BT, Chapi K, Shirzadi A, Shahabi H, Revhaug I, Prakash I, Bui DT (2018a) A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Sci Total Environ 627:744–755

    Article  Google Scholar 

  • Khosravi K, Sartaj M, Tsai FT-C, Singh VP, Kazakis N, Melesse AM, Prakash I, Bui DT, Pham BT (2018b) A comparison study of DRASTIC methods with various objective methods for groundwater vulnerability assessment. Sci Total Environ 642:1032–1049

    Article  Google Scholar 

  • Khozani ZS, Khosravi K, Pham BT, Kløve B, Wan Mohtar WHM, Yaseen ZM (2019) Determination of compound channel apparent shear stress: application of novel data mining models. J Hydroinf 21:798–811

    Article  Google Scholar 

  • Kisi O, Dailr AH, Cimen M, Shiri J (2012) Suspended sediment modeling using genetic programming and soft computing techniques. J Hydrol 450:48–58

    Article  Google Scholar 

  • Kouadio L, Deo RC, Byrareddy V, Adamowski JF, Mushtaq S, Phuong Nguyen V (2018) Artificial intelligence approach for the prediction of Robusta coffee yield using soil fertility properties. Comput Electron Agric 155:324–338. https://doi.org/10.1016/j.compag.2018.10.014

    Article  Google Scholar 

  • Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, pp 1097–1105

  • Kuki Á, Nagy L, Zsuga M, Kéki S (2011) Fast identification of phthalic acid esters in poly (vinyl chloride) samples by direct analysis in real time (DART) tandem mass spectrometry. Int J Mass Spectrom 303:225–228

    Article  Google Scholar 

  • Kumar M, Sihag P (2019) Assessment of infiltration rate of soil using empirical and machine learning-based models. Irrig Drain 68:588–601

    Article  Google Scholar 

  • LeCun Y, Bengio Y (1995) Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks 3361:1995

  • LeCun Y, Bengio Y, Hinton G (2015a) Deep learning. Nature 521:436–444

    Article  Google Scholar 

  • LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86:2278–2324

    Article  Google Scholar 

  • Lee J-G, Jun S, Cho Y-W, Lee H, Kim GB, Seo JB, Kim N (2017) Deep learning in medical imaging: general overview. Korean J Radiol 18:570–584

    Article  Google Scholar 

  • Lee S, Panahi M, Pourghasemi HR, Shahabi H, Alizadeh M, Shirzadi A, Khosravi K, Melesse AM, Yekrangnia M, Rezaie F (2019) Sevucas: A novel gis-based machine learning software for seismic vulnerability assessment. Appl Sci 9:3495

    Article  Google Scholar 

  • Legates DR, McCabe GJ Jr (1999) Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resour Res 35:233–241

    Article  Google Scholar 

  • Lim SY, Montakhab M, Nouri H (2009) A constriction factor based particle swarm optimization for economic dispatch.

  • Liu Q, Wu L, Xiao W, Wang F, Zhang L (2018) A novel hybrid bat algorithm for solving continuous optimization problems. Appl Soft Comput 73:67–82

    Article  Google Scholar 

  • Luo K, Zhao Q (2019) A binary grey wolf optimizer for the multidimensional knapsack problem. Appl Soft Comput 83:105645

    Article  Google Scholar 

  • Luo Y, Zou J, Yao C, Zhao X, Li T, Bai G (2018) HSI-CNN: a novel convolution neural network for hyperspectral image. 2018 International Conference on Audio, Language and Image Processing (ICALIP). IEEE, pp 464–469

  • Melesse A, Ahmad S, McClain M, Wang X, Lim Y (2011) Suspended sediment load prediction of river systems: An artificial neural network approach. Agric Water Manag 98:855–866

    Article  Google Scholar 

  • Mikaeil R, Haghshenas SS, Ozcelik Y, Gharehgheshlagh HH (2018) Performance evaluation of adaptive neuro-fuzzy inference system and group method of data handling-type neural network for estimating wear rate of diamond wire saw. Geotech Geol Eng 36:3779–3791

    Article  Google Scholar 

  • Mirjalili S, Mirjalili SM, Lewis A (2014) Grey Wolf Optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  • Mittal N, Singh U, Sohi BS (2016) Modified grey wolf optimizer for global engineering optimization. Applied Computational Intelligence and Soft Computing 2016

  • Mohebbian MR, Dinh A, Wahid K, Alam MS (2020) Blind, cuff-less, calibration-free and continuous blood pressure estimation using optimized inductive group method of data handling. Biomed Signal Process Control 57:101682

    Article  Google Scholar 

  • Morbidelli R, Corradini C, Saltalippi C, Flammini A, Dari J, Govindaraju RS (2018) Rainfall infiltration modeling: a review. Water 10:1873

    Article  Google Scholar 

  • Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50:885–900

    Article  Google Scholar 

  • Muro C, Escobedo R, Spector L, Coppinger R (2011) Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations. Behav Proc 88:192–197

    Article  Google Scholar 

  • Najafzadeh M, Barani G-A, Hessami-Kermani M-R (2013) Group method of data handling to predict scour depth around vertical piles under regular waves. Scientia Iranica 20:406–413

    Google Scholar 

  • Nie W-B, Li Y-B, Liu Y, Ma X-Y (2018) An approximate explicit green-Ampt infiltration model for cumulative infiltration. Soil Sci Soc Am J 82:919–930

    Article  Google Scholar 

  • Niu M, Hu Y, Sun S, Liu Y (2018) A novel hybrid decomposition-ensemble model based on VMD and HGWO for container throughput forecasting. Appl Math Model 57:163–178

    Article  Google Scholar 

  • Niu P, Niu S, Chang L (2019) The defect of the Grey Wolf optimization algorithm and its verification method. Knowl-Based Syst 171:37–43

    Article  Google Scholar 

  • Niu P, Zhang W (2012) Model of turbine optimal initial pressure under off-design operation based on SVR and GA. Neurocomputing 78:64–71

    Article  Google Scholar 

  • Owolabi TO (2019) Modeling the magnetocaloric effect of manganite using hybrid genetic and support vector regression algorithms. Phys Lett A 383:1782–1790

    Article  Google Scholar 

  • Oyehan TA, Alade IO, Bagudu A, Sulaiman KO, Olatunji SO, Saleh TA (2018) Predicting of the refractive index of haemoglobin using the Hybrid GA-SVR approach. Comput Biol Med 98:85–92

    Article  Google Scholar 

  • Panahi M, Gayen A, Pourghasemi HR, Rezaie F, Lee S (2020a) Spatial prediction of landslide susceptibility using hybrid support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) with various metaheuristic algorithms. Sci Total Environ 139937

  • Panahi M, Sadhasivam N, Pourghasemi HR, Rezaie F, Lee S (2020b) Spatial prediction of groundwater potential mapping based on convolutional neural network (CNN) and support vector regression (SVR). J Hydrol 125033

  • Panahi M, Khosravi K, Ahmad S, Panahi S, Heddam S, Melesse AM, Omidvar E, Lee C-W (2021) Cumulative infiltration and infiltration rate prediction using optimized deep learning algorithms: a study in Western Iran. Journal of Hydrology: Regional Studies 35100825. https://doi.org/10.1016/j.ejrh.2021.100825

  • Parchami-Araghi F, Mirlatifi SM, Ghorbani Dashtaki S, Mahdian MH (2013) Point estimation of soil water infiltration process using Artificial Neural Networks for some calcareous soils. J Hydrol 481:35–47. https://doi.org/10.1016/j.jhydrol.2012.12.007

    Article  Google Scholar 

  • Parkavi R, Shanthi M, Bhuvaneshwari M, Bhuvaneshwari M (2017) Recent trends in ELM and MLELM: A review. Adv Sci, Technol Eng Syst J 2:69–75

    Article  Google Scholar 

  • Parlange J-Y, Lisle I, Braddock R, Smith R (1982) The three-parameter infiltration equation. Soil Sci 133:337–341

    Article  Google Scholar 

  • Philip JR (1969) Theory of infiltration. Advances in hydroscience. Elsevier, pp 215–296

  • Qasem SN, Samadianfard S, Kheshtgar S, Jarhan S, Kisi O, Shamshirband S, Chau K-W (2019) Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates. Eng Appl Comput Fluid Mech 13:177–187

    Google Scholar 

  • Radaideh MI, Kozlowski T (2020) Analyzing nuclear reactor simulation data and uncertainty with the group method of data handling. Nucl Eng Technol 52:287–295

    Article  Google Scholar 

  • Rahmati M (2017) Reliable and accurate point-based prediction of cumulative infiltration using soil readily available characteristics: a comparison between GMDH, ANN, and MLR. J Hydrol 551:81–91

    Article  Google Scholar 

  • Ren M, Song Y, Chu W (2019) An improved locally weighted PLS based on particle swarm optimization for industrial soft sensor modeling. Sensors 19:4099

    Article  Google Scholar 

  • Roushangar K, Koosheh A (2015) Evaluation of GA-SVR method for modeling bed load transport in gravel-bed rivers. J Hydrol 527:1142–1152

    Article  Google Scholar 

  • Salih SQ, Sharafati A, Khosravi K, Faris H, Kisi O, Tao H, Ali M, Yaseen ZM (2020) River suspended sediment load prediction based on river discharge information: application of newly developed data mining models. Hydrol Sci J 65:624–637

    Article  Google Scholar 

  • Salmalian K, Nariman-Zadeh N, Gharababei H, Haftchenari H, Varvani-Farahani A (2010) Multi-objective evolutionary optimization of polynomial neural networks for fatigue life modelling and prediction of unidirectional carbon-fibre-reinforced plastics composites. Proc Inst Mech Eng, Part l: J Mater Des Appl 224:79–91

    Google Scholar 

  • Samadianfard S, Ghorbani MA, Mohammadi B (2018) Forecasting soil temperature at multiple-depth with a hybrid artificial neural network model coupled-hybrid firefly optimizer algorithm. Inf Process Agric 5:465–476

    Google Scholar 

  • Samadianfard S, Jarhan S, Salwana E, Mosavi A, Shamshirband S, Akib S (2019a) Support vector regression integrated with fruit fly optimization algorithm for river flow forecasting in Lake Urmia Basin. Water 11:1934

    Article  Google Scholar 

  • Samadianfard S, Majnooni-Heris A, Qasem SN, Kisi O, Shamshirband S, Chau K-w (2019b) Daily global solar radiation modeling using data-driven techniques and empirical equations in a semi-arid climate. Eng Appl Comput Fluid Mech 13:142–157

    Google Scholar 

  • Sasidharan S, Bradford SA, Šimůnek J, Kraemer SR (2019) Drywell infiltration and hydraulic properties in heterogeneous soil profiles. J Hydrol 570:598–611

    Article  Google Scholar 

  • Saxena A, Soni BP, Kumar R, Gupta V (2018) Intelligent Grey Wolf Optimizer-Development and application for strategic bidding in uniform price spot energy market. Appl Soft Comput 69:1–13

    Article  Google Scholar 

  • Shahabi H, Shirzadi A, Ghaderi K, Omidvar E, Al-Ansari N, Clague JJ, Geertsema M, Khosravi K, Amini A, Bahrami S (2020) Flood detection and susceptibility mapping using sentinel-1 remote sensing data and a machine learning approach: Hybrid intelligence of bagging ensemble based on k-nearest neighbor classifier. Remote Sens 12:266

    Article  Google Scholar 

  • Shakarami M, Davoudkhani IF (2016) Wide-area power system stabilizer design based on grey wolf optimization algorithm considering the time delay. Electr Power Syst Res 133:149–159

    Article  Google Scholar 

  • Shamshirband S, Hashemi S, Salimi H, Samadianfard S, Asadi E, Shadkani S, Kargar K, Mosavi A, Nabipour N, Chau K-W (2020) Predicting standardized streamflow index for hydrological drought using machine learning models. Eng Appl Comput Fluid Mech 14:339–350

    Google Scholar 

  • Sharafati A, Khosravi K, Khosravinia P, Ahmed K, Salman SA, Yaseen ZM, Shahid S (2019) The potential of novel data mining models for global solar radiation prediction. Int J Environ Sci Technol 16:7147–7164

    Article  Google Scholar 

  • Sharma S, Bhattacharjee S, Bhattacharya A (2016) Grey wolf optimisation for optimal sizing of battery energy storage device to minimise operation cost of microgrid. IET Gener Transm Distrib 10:625–637

    Article  Google Scholar 

  • Sheikholeslami M, Sheykholeslami FB, Khoshhal S, Mola-Abasia H, Ganji DD, Rokni HB (2014) Effect of magnetic field on Cu–water nanofluid heat transfer using GMDH-type neural network. Neural Comput Appl 25:171–178

    Article  Google Scholar 

  • Sigaroodi SK, Chen Q, Ebrahimi S, Nazari A, Choobin B (2014) Long-term precipitation forecast for drought relief using atmospheric circulation factors: a study on the Maharloo Basin in Iran. Hydrol Earth Syst Sci 18:1995

    Article  Google Scholar 

  • Sihag P, Karimi SM, Angelaki A (2019a) Random forest, M5P and regression analysis to estimate the field unsaturated hydraulic conductivity. Appl Water Sci 9:129

    Article  Google Scholar 

  • Sihag P, Singh B, Sepah Vand A, Mehdipour V (2020a) Modeling the infiltration process with soft computing techniques. ISH J Hydraul Eng 26:138–152

    Article  Google Scholar 

  • Sihag P, Singh VP, Angelaki A, Kumar V, Sepahvand A, Golia E (2019b) Modelling of infiltration using artificial intelligence techniques in semi-arid Iran. Hydrol Sci J 64:1647–1658

    Article  Google Scholar 

  • Sihag P, Tiwari N, Ranjan S (2017) Modelling of infiltration of sandy soil using gaussian process regression. Model Earth Syst Environ 3:1091–1100

    Article  Google Scholar 

  • Sihag P, Tiwari N, Ranjan S (2020b) Support vector regression-based modeling of cumulative infiltration of sandy soil. ISH J Hydraul Eng 26:44–50

    Google Scholar 

  • Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556

  • Šimůnek J, van Genuchten MT, Šejna M (2016) Recent Developments and Applications of the HYDRUS Computer Software Packages. Vadose Zone J 15:vzj2016.2004.0033. https://doi.org/10.2136/vzj2016.04.0033

  • Singh B, Sihag P, Parsaie A, Angelaki A (2021) Comparative analysis of artificial intelligence techniques for the prediction of infiltration process. Geol Ecol Landsc 5:109–118

    Article  Google Scholar 

  • Singh B, Sihag P, Singh K (2017) Modelling of impact of water quality on infiltration rate of soil by random forest regression. Model Earth Syst Environ 3:999–1004

    Article  Google Scholar 

  • Smith RE, Smettem KR, Broadbridge P (2002) Infiltration theory for hydrologic applications. American Geophysical Union

    Book  Google Scholar 

  • Stewart RD (2018) A dynamic multidomain green-Ampt infiltration model. Water Resour Res 54:6844–6859

    Article  Google Scholar 

  • Su H, Li X, Yang B, Wen Z (2018) Wavelet support vector machine-based prediction model of dam deformation. Mech Syst Signal Process 110:412–427

    Article  Google Scholar 

  • Suárez Cano P, Iglesias Prieto A, Gálvez Tomida A (2018) Make robots Be Bats: Specializing robotic swarms to the Bat algorithm.

  • Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. Thirty-first AAAI conference on artificial intelligence

  • Taheri K, Shahabi H, Chapi K, Shirzadi A, Gutiérrez F, Khosravi K (2019) Sinkhole susceptibility mapping: A comparison between Bayes-based machine learning algorithms. Land Degrad Dev 30:730–745

    Article  Google Scholar 

  • Talbi E-G (2009) Metaheuristics: from design to implementation. John Wiley & Sons

    Book  Google Scholar 

  • Termeh SVR, Khosravi K, Sartaj M, Keesstra SD, Tsai FT-C, Dijksma R, Pham BT (2019) Optimization of an adaptive neuro-fuzzy inference system for groundwater potential mapping. Hydrogeol J 27:2511–2534

    Article  Google Scholar 

  • Vand AS, Sihag P, Singh B, Zand M (2018) Comparative evaluation of infiltration models. KSCE J Civ Eng 22:4173–4184

    Article  Google Scholar 

  • Vapnik V, Vapnik V (1998) Statistical Learning Theory Wiley New York 1:624

    Google Scholar 

  • Vapnik VN (1995) The nature of statistical learning theory. Springer 2 Verlag, New York

  • Walton R, Binns A, Bonakdari H, Ebtehaj I, Gharabaghi B (2019) Estimating 2-year flood flows using the generalized structure of the Group Method of Data Handling. J Hydrol 575:671–689

    Article  Google Scholar 

  • Wang J, Li L, Niu D, Tan Z (2012) An annual load forecasting model based on support vector regression with differential evolution algorithm. Appl Energy 94:65–70

    Article  Google Scholar 

  • Wang J, Zhou Q, Jiang H, Hou R (2015) Short-term wind speed forecasting using support vector regression optimized by cuckoo optimization algorithm. Mathematical Problems in Engineering 2015

  • Wang K, Yang X, Liu X, Liu C (2017) A simple analytical infiltration model for short-duration rainfall. J Hydrol 555:141–154

    Article  Google Scholar 

  • Wang Y, Fang Z, Hong H, Peng L (2020a) Flood susceptibility mapping using convolutional neural network frameworks. J Hydrol 582:124482. https://doi.org/10.1016/j.jhydrol.2019.124482

    Article  Google Scholar 

  • Wang Y, Fang Z, Wang M, Peng L, Hong H (2020b) Comparative study of landslide susceptibility mapping with different recurrent neural networks. Comput Geosci 138:104445. https://doi.org/10.1016/j.cageo.2020.104445

    Article  Google Scholar 

  • Yang X-S (2010) A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, pp 65–74

  • Yaseen ZM, Jaafar O, Deo RC, Kisi O, Adamowski J, Quilty J, El-Shafie A (2016) Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq. J Hydrol 542:603–614

    Article  Google Scholar 

  • Yildizdan G, Baykan ÖK (2020) A novel modified bat algorithm hybridizing by differential evolution algorithm. Expert Syst Appl 141:112949

    Article  Google Scholar 

  • Zaller JG, Heigl F, Ruess L, Grabmaier A (2014) Glyphosate herbicide affects belowground interactions between earthworms and symbiotic mycorrhizal fungi in a model ecosystem. Sci Rep 4:5634

    Article  Google Scholar 

  • Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. European conference on computer vision. Springer, pp 818–833

  • Zhang Y, Wang S, Ji G (2015) A comprehensive survey on particle swarm optimization algorithm and its applications. Mathematical Problems in Engineering 2015

  • Zhong Z, Sun AY, Yang Q, Ouyang Q (2019) A deep learning approach to anomaly detection in geological carbon sequestration sites using pressure measurements. J Hydrol 573:885–894

    Article  Google Scholar 

  • Zhu H, Wang Y, Wang K, Chen Y (2011) Particle Swarm Optimization (PSO) for the constrained portfolio optimization problem. Expert Syst Appl 38:10161–10169

    Article  Google Scholar 

  • Zhu W, Wang J, Zhang W, Sun D (2012) Short-term effects of air pollution on lower respiratory diseases and forecasting by the group method of data handling. Atmos Environ 51:29–38

    Article  Google Scholar 

  • Zounemat-Kermani M, Seo Y, Kim S, Ghorbani MA, Samadianfard S, Naghshara S, Kim NW, Singh VP (2019) Can decomposition approaches always enhance soft computing models? Predicting the dissolved oxygen concentration in the St. Johns River, Florida. Appl Sci 9:2534

    Article  Google Scholar 

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Funding

Khabat Khosravi was partially supported by a grant from the Ferdowsi University of Mashhad (No. FUM-1399102452809).

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Authors and Affiliations

  1. Department of Range and Watershed Management, Lorestan University, Khorramabad, Iran

    Alireza Sepahvand

  2. Department of Watershed Management, Ferdowsi University of Mashhad, Mashhad, Iran

    Ali Golkarian & Khabat Khosravi

  3. School of Environmental and Geographical Sciences, University of Nottingham Malaysia, Selangor, Malaysia

    Lawal Billa

  4. Institute of Geographic Sciences and Natural Resources, Chinese Academy of Sciences, Beijing, China

    Kaiwen Wang

  5. Geoscience Platform Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro Yuseong-gu, Daejeon, 34132, Korea

    Fatemeh Rezaie

  6. Department of Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-roYuseong-gu, Daejeon, 305-350, Republic of Korea

    Fatemeh Rezaie

  7. Department of Computer Engineering, Faculty of Valiasr, Tehran Branch, Technical and Vocational University (TVU), Tehran, Iran

    Somayeh Panahi

  8. Department of Civil Engineering, University of Tabriz, Tabriz, Iran

    Saeed Samadianfard

  9. Department of Earth and Environment, Florida International University, Miami, FL, USA

    Khabat Khosravi

Authors
  1. Alireza Sepahvand
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  2. Ali Golkarian
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  3. Lawal Billa
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  4. Kaiwen Wang
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  5. Fatemeh Rezaie
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  6. Somayeh Panahi
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  7. Saeed Samadianfard
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  8. Khabat Khosravi
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Contributions

Conceptualization, K.K; and A.G.; methodology, K.K; A.G; and F.R.; software, F.R; formal analysis, K.K and F.R.; writing—original draft preparation, K.K; K.W; F.R.; L.B, S.P and S.S.; Data curation: A.S; writing—review and editing, K.K; A.G; L.B. supervision, A.G. All authors have read and agreed to the published version of the manuscript.

Corresponding authors

Correspondence to Ali Golkarian or Khabat Khosravi.

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Communicated by H. Babaie

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Sepahvand, A., Golkarian, A., Billa, L. et al. Evaluation of deep machine learning-based models of soil cumulative infiltration. Earth Sci Inform 15, 1861–1877 (2022). https://doi.org/10.1007/s12145-022-00830-7

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