Abstract
Infiltration models are very helpful in designing and evaluating surface irrigation systems. The main purpose of this study is to compare infiltration models which are used to evaluate infiltration rates of Davood Rashid, Kelat and Honam in Iran. Field infiltration tests were carried out at sixteen different locations comprising of 155 observations by use of double ring infiltrometer. The potential of four conventional infiltration models (Kostiakov, Modified Kostiakov, Novel and Philip’s models) were evaluated by least–square fitting to observed infiltration data. Three statistical comparison criteria including coefficient of correlation (C.C), coefficient of determination (R2) and root mean square error (RMSE) were used to determine the best performing infiltration models. The novel infiltration model suggests improved performance out of other three models. Further a Multi-linear Regression (MLR) equation has been developed using field infiltration data and compare with Support Vector Machine and Gaussian Process based regression with two kernels (Pearson VII and radial basis) modeling. Results suggest that Pearson VII based SVM works well than other modeling approaches in estimating the infiltration rate of soils. Sensitivity analysis concludes that the parameter, time, plays the most significant role in the estimation of infiltration rate. Comparison of results suggests that there is no significant difference between conventional and soft-computing based infiltration models.
Similar content being viewed by others
References
Angelaki, A., Sakellariou-Makrantonaki, M., and Tzimopoulos, C., (2013). “Theoretical and experimental research of cumulative infiltration.” Transport in Porous Media, Vol. 100, No. 2, pp. 247–257, DOI: 10.1007/s11242-013-0214-2.
Cortes, C. and Vapnik, V. (1995). “Support-vector networks.” Machine Learning, Vol. 20, No. 3, pp. 273–297, DOI: 10.1007/BF00994018.
Gill, M. K., Asefa, T., Kemblowski, M. W., and McKee, M. (2006). “Soil moisture prediction using support vector machines.” JAWRA Journal of the American Water Resources Association, Vol. 42, No. 4, pp. 1033–1046, DOI: 10.1111/j.1752-1688.2006.tb04512.x.
Green, W. H. and Ampt, G. (1911). “Studies on soil physics, 1. The flow of air and water through soils.” The Journal of Agricultural Science, Vol. 4, pp. 1–24, DOI: 10.1017/S0021859600001441.
Holtan, H. N. (1961). Concept for infiltration estimates in watershed engineering. Agricultural Research Service, U.S Dept. of Agriculture, pp. 34.
Horton, R. E. (1941). “An approach toward a physical interpretation of infiltration-capacity.” Soil Science Society of America Journal, Vol. 5, No. C, pp. 399–417, DOI: 10.2136/sssaj1941.036159950005000c0075x.
Kostiakov, A. N. (1932). “On the dynamics of the coefficient of waterpercolation in soils and on the necessity for studying it from a dynamic point of view for purposes of amelioration.” Trans, Vol. 6, pp. 17–21, https://doi.org/ci.nii.ac.jp/naid/10011005232/en/.
Kuss, M. (2006). Gaussian process models for robust regression, classification, and reinforcement learning, Doctoral dissertation, PhD thesis, Technische Universität, Darmstadt, pp. 189, https://doi.org/tuprints.ulb.tu-darmstadt.de/id/eprint/674.
Mirzaee, S., Zolfaghari, A. A., Gorji, M., Dyck, M., and Ghorbani Dashtaki, S. (2014). “Evaluation of infiltration models with different numbers of fitting parameters in different soil texture classes.” Archives of Agronomy and Soil Science, Vol. 60, No. 5, pp. 681–693, DOI: 10.1080/03650340.2013.823477.
Mishra, S. K., Tyagi, J. V., and Singh, V. P. (2003). “Comparison of infiltration models.” Hydrological Processes, Vol. 17, No. 13, pp. 2629–2652, DOI: 10.1002/hyp.1257.
Pal, M. and Foody, G. M. (2010). “Feature selection for classification of hyper spectral data by SVM.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 48, No. 5, pp. 2297–2307, DOI: 10.1109/TGRS.2009.2039484.
Pedretti, D., Barahona-Palomo, M., Bolster, D., Sanchez-Vila, X., and Fernandez-Garcia, D. (2012). “A quick and inexpensive method to quantify spatially variable infiltration capacity for artificial recharge ponds using photographic images.” Journal of Hydrology, Vol. 430, pp. 118–126, DOI: 10.1016/j.jhydrol.2012.02.008.
Philips, J. R. (1957). “The theory of infiltration: The infiltration equation and its solution.” Soil Sci., Vol. 83, No. 5, pp. 345–358, DOI: 10.1097/00010694-195705000-00002.
Rasmussen, C.E. and Williams, C.K., (2006). Gaussian processes for machine learning (Vol.1). MIT press, Cambridge, pp 248.
Rawls, W. J., Ahuja, L. R., Brakensiek, D. L., and Shirmohammadi, A. (1993). “Infiltration and soil water movement.” Handbook of Hydrology, D. Maidment, Ed., McGraw-Hill.
Richards, L. A. (1931). “Capillary conduction of liquids through porous mediums.” Journal of Applied Physics, Vol. 1, No. 5, pp. 318–333, DOI: 10.1063/1.1745010.
Sihag, P., Tiwari, N. K., and Ranjan, S. (2017a). “Estimation and intercomparison of infiltration models.” Water Science, Vol. 31, No. 1, pp. 34–43, DOI: 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.” Modelling Earth Systems and Environment, pp. 1091–1100, DOI: 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 Journal of Hydraulic Engineering, pp. 1–11, DOI: 10.1080/09715010.2017.1381861.
Singh, B., Sihag, P., and Singh, K. (2017). “Modelling of impact of water quality on infiltration rate of soil by random forest regression.” Modelling Earth Systems and Environment, pp. 999–1004, DOI: 10.1007/s40808-017-0347-3.
Singh, V. P. and Yu, F. X. (1990). “Derivation of infiltration equation using systems approach.” Journal of Irrigation and Drainage Engineering, Vol. 116, No. 6, pp. 837–858, DOI: 10.1061/(ASCE) 0733-9437(1990)116:6(837).
Smola, A. J. (1996). Regression estimation with support vector learning machines, Doctoral dissertation, Master’s thesis, Technische Universität München, pp. 78.
Sy, N. L. (2006). “Modelling the infiltration process with a multi-layer perceptron artificial neural network.” Hydrological sciences journal, Vol. 51, No. 1, pp. 3–20, DOI: 10.1623/hysj.51.1.3.
Tiwari, N. K., Sihag, P., and Ranjan, S. (2017). “Modeling of Infiltration of Soil using Adaptive Neuro-fuzzy Inference System (ANFIS).” Journal of Engineering & Technology Education, Vol. 11, No. 1, pp. 13–21.
Üstün, B., Melssen, W. J., and Buydens, L. M. (2006). “Facilitating the application of support vector regression by using a universal Pearson VII function based kernel.” Chemometrics and Intelligent Laboratory Systems, Vol. 81, No. 1, pp. 29–40, DOI: 10.1016/j.chemolab.2005.09.003.
Vapnik, V. (1998). Statistical learning theory. Wiley, New York, pp. 768. https://doi.org/www.cs.waikato.ac.nz
Yusof, K. W., Babangida, N. M., Mustafa, M. R., and Isa, M. H. (2017). “Linear kernel support vector machines for modelling pore-water pressure responses.” Journal of Engineering Science and Technology, Vol. 12, No. 8, pp. 2202–2212.
Zolfaghari, A. A., Mirzaee, S., and Gorji, M. (2012). “Comparison of different models for estimating cumulative infiltration.” International Journal of Soil Science, Vol. 7, No. 3, pp. 108, DOI: 10.3923/ijss.2012.108.115.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Vand, A.S., Sihag, P., Singh, B. et al. Comparative Evaluation of Infiltration Models. KSCE J Civ Eng 22, 4173–4184 (2018). https://doi.org/10.1007/s12205-018-1347-1
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12205-018-1347-1