Multiple AI model integration strategy—Application to saturated hydraulic conductivity prediction from easily available soil properties

Adamowski, 2012, Comparison of multivariate adaptive regression splines with coupled wavelet transform artificial neural networks for runoff forecasting in Himalayan micro-watersheds with limited data, J. Hydroinformatics, 14, 731, 10.2166/hydro.2011.044

Agyare, 2007, Artificial neural network estimation of saturated hydraulic conductivity, Vadose Zone J., 6, 423, 10.2136/vzj2006.0131

Ahuja, 1984, Macroporosity to characterize spatial variability of hydraulic conductivity and effects of land management1, Soil Sci. Soc. Am. J., 48, 699, 10.2136/sssaj1984.03615995004800040001x

Aiello, 2014, An assessment of the beerkan method for determining the hydraulic properties of a sandy loam soil, Geoderma, 235

Arshad, 2013, Predicting saturated hydraulic conductivity by artificial intelligence and regression models, ISRN Soil Sci., 2013, 1, 10.1155/2013/308159

Bagarello, 2000, Factors affecting measurement of the near-saturated soil hydraulic conductivity, Soil Sci. Soc. Am. J., 64, 1203, 10.2136/sssaj2000.6441203x

Bagarello, 2009, Comparing two methods of analysis of single-ring infiltrometer data for a sandy–loam soil, Geoderma, 149, 415, 10.1016/j.geoderma.2008.12.022

Çimen, 2009, Comparison of two different data-driven techniques in modeling lake level fluctuations in Turkey, J. Hydrol., 378, 253, 10.1016/j.jhydrol.2009.09.029

Comegna, 2000, Scaling the saturated hydraulic conductivity of a vertic ustorthens soil under conventional and minimum tillage, Soil Tillage Res., 54, 1, 10.1016/S0167-1987(99)00098-7

Das, 2012, Prediction of field hydraulic conductivity of clay liners using an artificial neural network and support vector machine, Int. J. Geomech., 12, 606, 10.1061/(ASCE)GM.1943-5622.0000129

De Andrés, 2011, Bankruptcy forecasting: a hybrid approach using Fuzzy c-means clustering and Multivariate Adaptive Regression Splines (MARS), Expert Syst. Appl., 38, 1866, 10.1016/j.eswa.2010.07.117

Deo, 2017, Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model, Atmos. Res., 184, 149, 10.1016/j.atmosres.2016.10.004

Ding, 2015, Extreme learning machine: algorithm, theory and applications, Artif. Intell. Rev., 44, 103, 10.1007/s10462-013-9405-z

Elbisy, 2006, Prediction of saturated hydraulic conductivity of sandy soil using neural network, Ain Shams Eng. J., 41, 480

Elbisy, 2015, Support Vector Machine and regression analysis to predict the field hydraulic conductivity of sandy soil, KSCE J. Civ. Eng., 19, 2307, 10.1007/s12205-015-0210-x

Fetter, 2000

Friedman, 1991, Multivariate adaptive regression splines, Ann. Stat., 1, 10.1214/aos/1176347963

Frind, 1973, Galerkin solution of the inverse problem for aquifer transmissivity, Water Resour. Res., 9, 1397, 10.1029/WR009i005p01397

Goyal, 2014, Modeling of daily pan evaporation in sub-tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS, Expert Syst. Appl., 41, 5267, 10.1016/j.eswa.2014.02.047

Huang, 2004, Extreme learning machine : a New learning Scheme of feedforward neural networks, IEEE International Joint Conference on Neural Networks (IJCNN), 985

Huang, 2006, Extreme learning machine: theory and applications, Neurocomputing, 70, 489, 10.1016/j.neucom.2005.12.126

Huang, 2011, Extreme learning machines: a survey, Int. J. Mach. Learn. Cybern., 2, 107, 10.1007/s13042-011-0019-y

Kisi, 2015, Pan evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree, J. Hydrol., 528, 312, 10.1016/j.jhydrol.2015.06.052

Kim, 2012, Pan evaporation modeling using neural computing approach for different climatic zones, Water Resour. Manage., 26, 3231, 10.1007/s11269-012-0069-2

Kisi, 2009, Evapotranspiration modelling using support vector machines, Hydrol. Sci. J., 54, 918, 10.1623/hysj.54.5.918

Lamorski, 2008, Using support vector machines to develop pedotransfer functions for water retention of soils in Poland, Soil Sci. Soc. Am. J., 72, 1243, 10.2136/sssaj2007.0280N

LeBlanc, 1991, Large-scale natural gradient tracer test in sand and gravel, Cape Cod, Massachusetts: 1. Experimental design and observed tracer movement, Water Resour. Res., 27, 895, 10.1029/91WR00241

Martínez-Martínez, 2011, Regularized extreme learning machine for regression problems, Neurocomputing, 74, 3716, 10.1016/j.neucom.2011.06.013

Merdun, 2006, Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity, Soil Tillage Res., 90, 108, 10.1016/j.still.2005.08.011

Minasny, 2004, Neural networks prediction of soil hydraulic functions for alluvial soils using multistep outflow data, Soil Sci. Soc. Am. J., 68, 417, 10.2136/sssaj2004.4170

Naganna, 2017, Factors influencing streambed hydraulic conductivity and their implications on stream–aquifer interaction: a conceptual review, Environ. Sci. Pollut. Res. - Int., 24, 24765, 10.1007/s11356-017-0393-4

Nakhaei, 2005, Estimating the saturated hydraulic conductivity of granular material using artificial neural network based on grain size distribution curve, J. Sci. Islam Repub. Iran, 16, 55

Quinlan, 1992, Learning with continuous classes, 5th Australian Joint Conference on Artificial Intelligence, 343

Rahimikhoob, 2014, Comparison between M5 model tree and neural networks for estimating reference evapotranspiration in an arid environment, Water Resour. Manag., 28, 657, 10.1007/s11269-013-0506-x

Raghavendra, 2014, Support vector machine applications in the field of hydrology: a review, Appl. Soft Comput., 19, 372, 10.1016/j.asoc.2014.02.002

Reynolds, 2000, Comparison of tension infiltrometer, pressure infiltrometer, and soil core estimates of saturated hydraulic conductivity, Soil Sci. Soc. Am. J., 64, 478, 10.2136/sssaj2000.642478x

Rezaie-balf, 2017, Wavelet coupled MARS and M5 Model Tree approaches for groundwater level forecasting, J. Hydrol., 553, 356, 10.1016/j.jhydrol.2017.08.006

Rogiers, 2012, Estimation of hydraulic conductivity and its uncertainty from grain-size data using GLUE and artificial neural networks, Math. Geosci., 10.1007/s11004-012-9409-2

Sattari, 2013, M5 model tree application in daily river flow forecasting in Sohu Stream, Turkey, Water Resour., 40, 233, 10.1134/S0097807813030123

Sedaghat, 2016, Estimation of soil saturated hydraulic conductivity by artificial neural networks ensemble in smectitic soils, Eurasian Soil Sci., 49, 347, 10.1134/S106422931603008X

Sharda, 2006, Modeling runoff from middle Himalayan watersheds employing artificial intelligence techniques, Agric. Water Manage., 83, 233, 10.1016/j.agwat.2006.01.003

Singh, 2010, Estimation of mean annual flood in Indian catchments using backpropagation neural network and M5 model tree, Water Resour. Manag., 24, 2007, 10.1007/s11269-009-9535-x

Suleiman, 2001, 44

Tayfur, 2014, Supervised intelligent committee machine method for hydraulic conductivity estimation, Water Resour. Manage., 28, 1173, 10.1007/s11269-014-0553-y

Vapnik, 1995

Wagner, 1997, Experimental design for estimating parameters of rate-limited mass transfer: analysis of stream tracer studies, Water Resour. Res., 33, 1731, 10.1029/97WR01067

Wang, 2011, A study on effectiveness of extreme learning machine, Neurocomputing, 74, 2483, 10.1016/j.neucom.2010.11.030

Wang, 2017, Pan evaporation modeling using four different heuristic approaches, Comput. Electron. Agric., 140, 203, 10.1016/j.compag.2017.05.036

Wu, 1999, Measuring saturated hydraulic conductivity using a generalized solution for single-ring infiltrometers, Soil Sci. Soc. Am. J., 63, 788, 10.2136/sssaj1999.634788x

Yang, 2001, A natural-gradient field tracer test for evaluation of pollutant-transport parameters in a porous-medium aquifer, Hydrogeol. J., 9, 313, 10.1007/s100400100127