References
- Bhave, S., and Sreeja, P. (2013). “Influence of initial soil condition on infiltration characteristics determined using a disk infiltrometer.” ISH J. Hydraul. Eng., 19(3), 291–296.10.1080/09715010.2013.808445
- Boser, B.E., Guyon, I.M., and Vapnik, V.N. 1992. “A training algorithm for optimal margin classifiers.” Proc., of Fifth Annual Workshop on Computational Learning Theory, ACM, 144–152.
- Devices, D. (2014). Mini disk infiltrometer user’s manual, Version 9, Decagon Devices, Pullman, WA.
- Elbisy, M.S. (2015). “Support Vector Machine and regression analysis to predict the field hydraulic conductivity of sandy soil.” KSCE J. Civil Eng., 19(7), 2307–2316.10.1007/s12205-015-0210-x
- Emami, H., SHorafa, M., and Neyshabouri, M.R. (2012). “Evaluation of hydraulic conductivity at inflection point of soil moisture characteristic curve as a matching point for some soil unsaturated hydraulic conductivity models.” JWSS-Isfahan Univ. Technol., 16(59), 169–182.
- Fereshte, F.H. (2014). “Evaluation of artificial neural network and regression PTFS in estimating some soil hydraulic parameters.” ProEnvironment/ProMediu, 7 (17), 10–20.
- Gifford, G.F. (1976). “Applicability of some infiltration formulae to rangeland infiltrometer data.” J. Hydrol., 28(1), 1–11.10.1016/0022-1694(76)90048-2
- Green, W.H., and Ampt, G. (1911). “Studies on soil physics, 1. The flow of air and water through soils.” J. Agric. Sci., 4, 1–24.10.1017/S0021859600001441
- Horton, R.E., Gaebe, R.R., and Beutner, E.L. (1940). “Sprinkled-plat runoff-and infiltration-experiments on Arizona desert-soils.” Eos, Trans. Amer. Geophys. Union, 21(2), 550–558.
- Huang, S., Chang, J., Huang, Q., and Chen, Y. (2014). “Monthly stream flow prediction using modified EMD-based support vector machine.” J. Hydrol., 511, 764–775.10.1016/j.jhydrol.2014.01.062
- Jury, W.A., Gardner, W.R., and Gardner, W.H. (1991). Soil physics, 5th edn, Wiley, New York, NY.
- Kalkhajesh, Y.K., Arshad, R.R., Amerikhan, H., and Sami, M. (2012). “Multiple linear regression, artificial neural network (MLP, RBF) and ANFIIS models for modeling the saturated hydraulic conductivity of tropical region soils (a case study: Khuzestan province: southwest Iran).” Int. J. Agric. Res. Rev., 2(3), 255–265.
- Karandish, F., and Šimůnek, J. (2016). “A comparison of numerical and machine-learning modeling of soil water content with limited input data.” J. Hydrol., 543, 892–909. doi:10.1016/j.jhydrol.2016.11.007.
- Kostiakov, A.N. (1932). “On the dynamics of the coefficient of water-percolation in soils and on the necessity for studying it from a dynamic point of view for purposes of amelioration.” Trans, 6, 17–21.
- Kumar, M., Ranjan, S., Tiwari, N.K., and Gupta, R. (2017). “Plunging hollow jet aerators-oxygen transfer and modelling.” ISH J. Hydraul. Eng., 23(1), 1–7.10.1080/09715010.2017.1408434
- Lamorski, K., Pachepsky, Y., Sławiński, C., and Walczak, R.T. (2008). “Using support vector machines to develop pedotransfer functions for water retention of soils in Poland.” Soil Sci. Soc. Amer. J., 72(5), 1243–1247.10.2136/sssaj2007.0280 N
- Machiwal, D., Jha, M.K., and Mal, B.C. (2006). “Modelling infiltration and quantifying spatial soil variability in a wasteland of Kharagpur, India.” Biosyst. Eng., 95(4), 569–582.10.1016/j.biosystemseng.2006.08.007
- Minasny, B., and Perfect, E. (2004). “Solute adsorption and transport parameters.” Dev. Soil Sci., 30, 195–224.10.1016/S0166-2481(04)30012-7
- Pal, M., Singh, N.K., and Tiwari, N.K. (2011). “Support vector regression based modeling of pier scour using field data.” Eng. Appl. Artif. Intell., 24(5), 911–916.10.1016/j.engappai.2010.11.002
- Philip, J.R. (1957). “The theory of infiltration: 1. The infiltration equation and its solution.” Soil Sci., 83(5), 345–358.10.1097/00010694-195705000-00002
- Quinlan, J.R. 1992, November. “Learning with continuous classes.” 5th Australian Joint Conference on Artificial Intelligence, Vol. 92, World Scientific Publishing Company Incorporated, Hobart, Tosmania, 343–348.
- Richards, L.A. (1931). “Capillary conduction of liquids through porous mediums.” Physics, 1(5), 318–333.10.1063/1.1745010
- Schuh, W.M., and Bauder, J.W. (1986). “Effect of soil properties on hydraulic conductivity-moisture relationships.” Soil Sci. Soc. Amer. J., 50(4), 848–855.10.2136/sssaj1986.03615995005000040004x
- Sihag, P., Jain, P., and Kumar, M. (2018). “Modelling of impact of water quality on recharging rate of storm water filter system using various kernel function based regression.” Model. Earth Syst. Environ., 1–8.
- Sihag, P., Tiwari, N.K., and Ranjan, S. (2017a). “Estimation and inter-comparison of infiltration models.” Water Sci., 31(1), 34–43.10.1016/j.wsj.2017.03.001
- Sihag, P., Tiwari, N.K., and Ranjan, S. (2017b). “Modelling of infiltration of sandy soil using gaussian process regression.” Model. Earth Syst. Environ., 3(3), 1091–1100.10.1007/s40808-017-0357-1
- Sihag, P., Tiwari, N.K., and Ranjan, S. (2017c). “Prediction of unsaturated hydraulic conductivity using adaptive neuro-fuzzy inference system (ANFIS).” ISH J. Hydraul. Eng., 7(1), 1–11.10.1080/09715010.2017.1381861
- Singh, V.P., and Yu, F.X. (1990). “Derivation of infiltration equation using systems approach.” J. Irrig. Drainage Eng., 116(6), 837–858.
- Singh, B., Sihag, P., and Singh, K. (2017). “Modelling of impact of water quality on infiltration rate of soil by random forest regression.” Model. Earth Syst. Environ., 3(3), 999–1004.10.1007/s40808-017-0347-3
- Smola, A.J. 1996. “Regression estimation with support vector learning machines.” Doctoral dissertation, Master’s thesis, Technische Universität München.
- Specht, D.F. (1991). “A general regression neural network.” IEEE Trans. Neural Netw., 2(6), 568–576.10.1109/72.97934
- Tiwari, N.K., Sihag, P., and Ranjan, S. (2017). “Modeling of infiltration of soil using adaptive neuro-fuzzy inference system (ANFIS).” J. Eng. Technol. Educ., 11(1), 13–21.
- Vapnik, V.N. (1995). The nature of statistical learning theory, Springer-Verlag, New York, NY.10.1007/978-1-4757-2440-0
- Vereecken, H. (1995). “Estimating the unsaturated hydraulic conductivity from theoretical models using simple soil properties.” Geoderma, 65(1–2), 81–92.10.1016/0016-7061(95)92543-X
- Wang, Y., Witten, I.H., van Someren, M., and Widmer, G. 1997. “Inducing models trees for continuous classes.” Proceedings of the Poster Papers of the European Conference on Machine Learning, Department of Computer Science, University of Waikato, New Zealand.
- Wasserman, P.D. (1993). Advanced methods in neural computing, Van Nostrand Reinhold, New York, NY.
- Xing, B., Gan, R., Liu, G., Liu, Z., Zhang, J., and Ren, Y. (2015). “Monthly mean streamflow prediction based on bat algorithm-support vector machine.” J. Hydrol. Eng., 21(2), 04015057.
- Zolfaghari, A.A., Mirzaee, S., and Gorji, M. (2012). “Comparison of different models for estimating cumulative infiltration.” Int. J. Soil Sci., 7(3), 108–115.10.3923/ijss.2012.108.115