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Articles

Modelling of infiltration using artificial intelligence techniques in semi-arid Iran

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Pages 1647-1658 | Received 07 Dec 2018, Accepted 23 Jul 2019, Published online: 24 Sep 2019

References

  • Achari, M.S., Dhakshinamurthy, M., and Arunachalam, G., 1999. Studies of the influence on paper mill effluents on the yield, availability and uptake of nutrients in rice. Journal of the Indian Society of Soil Science, 47 (2), 276–280.
  • Al-Sulaiman, M.A., 2015. Applying of an adaptive neuro fuzzy inference system for prediction of unsaturated soil hydraulic conductivity. Biosciences, Biotechnology Research Asia, 12 (3), 2261–2272. doi:10.13005/bbra/1899
  • Anari, P.L., Darani, H.S., and Nafarzadegan, A.R., 2011. Application of ANN and ANFIS models for estimating total infiltration rate in an arid rangeland ecosystem. Research Journal of Environmental Sciences, 5 (3), 236. doi:10.3923/rjes.2011.236.247
  • Angelaki, A., et al., 2018. Estimation of models for cumulative infiltration of soil using machine learning methods. ISH Journal of Hydraulic Engineering, 1–8. doi:10.1080/09715010.2018.1531274
  • Angelaki, A., Sakellariou-Makrantonaki, M., and Tzimopoulos, C., 2013. Theoretical and experimental research of cumulative infiltration. Transport in Porous Media, 100 (2), 247–257. doi:10.1007/s11242-013-0214-2
  • Arshad, M., Shoaib, A., and Beg, I., 2013. Fixed point of a pair of contractive dominated mappings on a closed ball in an ordered dislocated metric space. Fixed Point Theory and Applications, 2013 (1), 115. doi:10.1186/1687-1812-2013-115
  • Azamathulla, H.M., Haghiabi, A.H., and Parsaie, A., 2016. Prediction of side weir discharge coefficient by support vector machine technique. Water Science and Technology: Water Supply, 16 (4), 1002–1016.
  • Babaei, F., et al., 2018. Spatial analysis of infiltration in agricultural lands in arid areas of Iran. CATENA, 170, 25–35. doi:10.1016/j.catena.2018.05.039
  • Breiman, L., 1999. Random forests. Berkeley, CA: University of Calfornia at Berkeley, Reoprt TR567.
  • Corradini, C., Melone, F., and Smith, R.E., 1994. Modeling infiltration during complex rainfall sequences. Water Resources Research, 30 (10), 2777–2784. doi:10.1029/94WR00951
  • Cortes, C. and Vapnik, V., 1995. Support-vector networks. Machine Learning, 20 (3), 27297. doi:10.1007/BF00994018
  • Das, L., et al., 2011. Centennial scale warming over Japan: are the rural stations really rural? Atmospheric Science Letters, 12 (4), 362–367. doi:10.1002/asl.v12.4
  • Elbisy, M.S., 2015. Support Vector Machine and regression analysis to predict the field hydraulic conductivity of sandy soil. KSCE Journal of Civil Engineering, 19 (7), 2307–2316. doi:10.1007/s12205-015-0210-x
  • Erzin, Y., et al., 2009. Artificial neural network (ANN) models for determining hydraulic conductivity of compacted fine-grained soils. Canadian Geotechnical Journal, 46 (8), 955–968. doi:10.1139/T09-035
  • Green, W.H. and Ampt, G.A., 1911. Studies on soil phyics. The Journal of Agricultural Science, 4 (1), 1–24. doi:10.1017/S0021859600001441
  • Holtan, H.N., 1961. Concept for infiltration estimates in watershed engineering. Washington, DC: US Department of Agriculture.
  • Horton, R.E., 1941. An approach toward a physical interpretation of infiltration-capacity 1. Soil Science Society of America Journal, 5 (C), 399–417. doi:10.2136/sssaj1941.036159950005000C0075x
  • Kostiakov, A.N., 1932. On the dynamics of the coefficient of water percolation in soils and the necessity of studying it from the dynamic point of view for the purposes of amelioration. Transactions of the Sixth Committee of the International Society of Soil Science, 1, 7–21.
  • Kumar, M. and Sihag, P., 2019. Assessment of infiltration rate of soil using empirical and machine learning‐based models. Irrigation and Drainage, 68, 588–601. doi:10.1002/ird.2332
  • Kumar, M., Sihag, P., and Singh, V., 2019. Enhanced soft computing for ensemble approach to estimate the compressive strength of high strength concrete. Journal of Materials and Engineering Structures, JMES, 6 (1), 93–103.
  • Loague, K. and Gander, G.A., 1990. R‐5 revisited: 1. Spatial variability of infiltration on a small rangeland catchment. Water Resources Research, 26 (5), 957–971. doi:10.1029/WR026i005p00957
  • Mehdipour, V., et al., 2018. Comparing different methods for statistical modeling of particulate matter in Tehran, Iran. Air Quality, Atmosphere & Health, 11 (10), 1155–1165. doi:10.1007/s11869-018-0615-z
  • Mohanty, S., et al., 2019. Estimating the strength of stabilized dispersive soil with cement clinker and fly ash. Geotechnical and Geological Engineering, 37 (4), 1–12. doi:10.1007/s10706-019-00808-1
  • Nain, S.S., Sihag, P., and Luthra, S., 2018. Performance evaluation of fuzzy-logic and BP-ANN methods for WEDM of aeronautics super alloy. MethodsX, 5, 890–908. doi:10.1016/j.mex.2018.04.006
  • Parlange, J.Y., 1971a. Theory of water movement in soils: 1. One-dimensional absorption. Soil Science, 111 (2), 134–137. doi:10.1097/00010694-197102000-00010
  • Parlange, J.Y., 1971b. Theory of water movement in soils: 2. One-imensional infiltration. Soil Science, 111 (3), 170–174. doi:10.1097/00010694-197103000-00004
  • Parlange, J.Y., 1972a. Theory of water movement in soils. 6. Effect of water depth over soil. Soil Science, 133, 308–312. doi:10.1097/00010694-197205000-00002
  • Parlange, J.Y., 1972b. Theory of water movement in soils. 8. One-dimensional infiltration with constant flux at the surface. Soil Science, 114, 1–4. doi:10.1097/00010694-197207000-00001
  • Parlange, J.Y., 1975. A note of the Green & Ampt equation. Soil Science, 119, 466–467. doi:10.1097/00010694-197506000-00009
  • Parlange, J.Y., et al., 1982. The three parameter infiltration equation. Soil Science, 133, 337–341. doi:10.1097/00010694-198206000-00001
  • Parlange, J.Y., Havercamp, R., and Touma, J., 1985. Infiltration under ponded conditions: 1. Optimal analytical solution and comparison with experimental observations. Soil Science, 139, 305–311. doi:10.1097/00010694-198504000-00003
  • Parsaie, A., et al., 2017b. Predication of discharge coefficient of cylindrical weir-gate using adaptive neuro fuzzy inference systems (ANFIS). Frontiers of Structural and Civil Engineering, 11 (1), 111–122. doi:10.1007/s11709-016-0354-x
  • Parsaie, A., Yonesi, H., and Najafian, S., 2017a. Prediction of flow discharge in compound open channels using adaptive neuro fuzzy inference system method. Flow Measurement and Instrumentation, 54, 288–297. doi:10.1016/j.flowmeasinst.2016.08.013
  • Philip, J.R., 1957a. Theory of infiltration: 1. The infiltration equation and its solution. Soil Science, 83, 435–448. doi:10.1097/00010694-195706000-00003
  • Philip, J.R., 1957b. Theory of infiltration: 4. Sorptivity and algebraic infiltration equations. Soil Science, 84, 257–264. doi:10.1097/00010694-195709000-00010
  • Rahmati, M., 2017. Reliable and accurate point-based prediction of cumulative infiltration using soil readily available characteristics: a comparison between GMDH, ANN, and MLR. Journal of Hydrology, 551, 81–91. doi:10.1016/j.jhydrol.2017.05.046
  • Schaap, M.G. and Leij, F.J., 1998. Using neural networks to predict soil water retention and soil hydraulic conductivity. Soil and Tillage Research, 47 (1–2), 37–42. doi:10.1016/S0167-1987(98)00070-1
  • SCS (Soil Conservation Service), 1972. National engineering handbook, section 4: hydrology. Washington, DC: Department of Agriculture, 762.
  • Sepahvand, A., et al., 2019. Assessment of the various soft computing techniques to predict sodium absorption ratio (SAR). ISH Journal of Hydraulic Engineering, 1–12. doi:10.1080/09715010.2019.1595185
  • Sihag, P., 2018. Prediction of unsaturated hydraulic conductivity using fuzzy logic and artificial neural network. Modeling Earth Systems and Environment, 4 (1), 189–198. doi:10.1007/s40808-018-0434-0
  • Sihag, P., et al., 2018a. Modeling the infiltration process with soft computing techniques. ISH Journal of Hydraulic Engineering, 1–15. doi:10.1080/09715010.2018.1464408
  • Sihag, P., et al., 2019a. Modeling unsaturated hydraulic conductivity by hybrid soft computing techniques. Soft Computing, 1–14. doi:10.1007/s00500-019-03847-1
  • Sihag, P., et al., 2019b. Model-based soil temperature estimation using climatic parameters: the case of Azerbaijan Province, Iran. Geology, Ecology, and Landscapes, 1–13. doi:10.1080/24749508.2019.1610841
  • Sihag, P., Jain, P., and Kumar, M., 2018b. Modeling of impact of water quality on recharging rate of storm water filter system using various kernel function based regression. Modelling Earth Systems and Environment, 4 (1), 61–68. doi:10.1007/s40808-017-0410-0
  • Sihag, P., Tiwari, N.K., and Ranjan, S., 2017a. Estimation and inter-comparison of infiltration models. Water Science, 31 (1), 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. Modeling Earth Systems and Environment, 3 (3), 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, 1–11. doi:10.1080/09715010.2017.1381861
  • Sihag, P., Tiwari, N.K., and Ranjan, S., 2018c. Support vector regression-based modeling of cumulative infiltration of sandy soil. ISH Journal of Hydraulic Engineering, 1–7. doi:10.1080/09715010.2018.1439776
  • Singh, B., et al., 2018a. Estimation of trapping efficiency of vortex tube silt ejector. International Journal of River Basin Management, 1–38. doi:10.1080/15715124.2018.1476367
  • Singh, B., Sihag, P., and Deswal, S., 2019. Modelling of the impact of water quality on the infiltration rate of the soil. Applied Water Science, 9 (1), 15. doi:10.1007/s13201-019-0892-1
  • Singh, B., Sihag, P., and Singh, K., 2017. Modelling of impact of water quality on infiltration rate of soil by random forest regression. Modeling Earth Systems and Environment, 3 (3), 999–1004. doi:10.1007/s40808-017-0347-3
  • Singh, B., Sihag, P., and Singh, K., 2018b. Comparison of infiltration models in NIT Kurukshetra campus. Applied Water Science, 8 (2), 63. doi:10.1007/s13201-018-0708-8
  • Singh, V.P. and Yu, F.X., 1990. Derivation of infiltration equation using systems approach. Journal of Irrigation and Drainage Engineering, 116 (6), 837–858. doi:10.1061/(ASCE)0733-9437(1990)116:6(837)
  • Smola, A.J. and Schölkopf, B., 2004. A tutorial on support vector regression. Statistics and Computing, 14 (3), 199–222. doi:10.1023/B:STCO.0000035301.49549.88
  • Sy, N.L., 2006. Modelling the infiltration process with a multi-layer perceptron artificial neural network. Hydrological Sciences Journal, 51 (1), 3–20. doi:10.1623/hysj.51.1.3
  • Taha, A., Gresillon, J.M., and Clothier, B.E., 1997. Modelling the link between hillslope water movement and streamflow: application to a small Mediterranean forest watershed. Journal of Hydrology, 203 (1–4), 11–20. doi:10.1016/S0022-1694(97)00081-4
  • Takagi, T. and Sugeno, M., 1985. Fuzzy identification of systems and its application to modeling and control. IEEE Transaction on System, Man and Cybernetics, SMC-15, 116–132. doi:10.1109/TSMC.1985.6313399
  • Tiwari, N.K., et al., 2018. Prediction of trapping efficiency of vortex tube ejector. ISH Journal of Hydraulic Engineering, 1–9. doi:10.1080/09715010.2018.1441752
  • Tiwari, N.K., et al., 2019. Estimation of tunnel desilter sediment removal efficiency by ANFIS. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 1–16. doi:10.1007/s40996-019-00261-3
  • Tiwari, N.K. and Sihag, P., 2018. Prediction of oxygen transfer at modified Parshall flumes using regression models. ISH Journal of Hydraulic Engineering, 1–12. doi:10.1080/09715010.2018.1473058
  • 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, 11 (1), 13–21.
  • Vapnik, V.N., 1995. The nature of statistical learning theory. New York, NY: Springer-Verlag.
  • Vapnik, V.N., 1999. An overview of statistical learning theory. IEEE Transactions on Neural Networks, 10 (5), 988–999. doi:10.1109/72.788640

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