1,633
Views
14
CrossRef citations to date
0
Altmetric
Research Articles

Prediction of the sodium absorption ratio using data-driven models: a case study in Iran

ORCID Icon
Pages 1-10 | Received 17 Jul 2018, Accepted 07 Jan 2019, Published online: 21 Jan 2019

References

  • Asadollahfardi, G., Hemati, A., Moradinejad, S., & Asadollahfardi, R. (2013). Sodium adsorption ratio (SAR) prediction of the Chalghazi river using artificial neural network (ANN) Iran. Current World Environment, 8(2), 169–178.
  • Asadollahfardi, G., Khodadadi, A., & Gharayloo, R. (2010). The assessment of effective factors on Anzali wetland pollution using artificial neural networks. Asian Journal of Water Environment and Pollution, 7(2), 23–30.
  • Ayers, R. S., & Tanji, K. K. (1981). Agronomic aspects of crop irrigation with wastewater. Proceedings of the Specialty Conference, Water Forum ’81, San Francisco, 578–586.
  • Bartram, J., & Ballance, R., World Health Organization. (1996). Water quality monitoring: A practical guide to the design and implementation of freshwater quality studies and monitoring programs. UNEP/WHO
  • Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational learning theory(pp. 144-152). ACM.
  • Bouwer, H., & Idelovitch, E. (1987). Quality requirements for irrigation with sewage water. Journal of Irrigation and Drainage Engineering, 113(4), 516–535.
  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
  • Haykin, S. (1999). Multilayer perceptrons. Neural networks. A Comprehensive Foundation, 2, 156–255.
  • Haykin, S. S., Haykin, S. S., Haykin, S. S., & Haykin, S. S. (2009). Neural networks and learning machines (Vol. 3). Upper Saddle River: Pearson.
  • Kuss, M. (2006). Gaussian process models for robust regression, classification, and reinforcement learning. Doctoral dissertation, Ph.D. thesis, Technische Universität, Darmstadt, 189.
  • Mehdipour, V., Stevenson, D. S., Memarianfard, M., & Sihag, P. (2018). Comparing different methods for statistical modeling of particulate matter in Tehran, Iran. Air Quality, Atmosphere & Health, 11(10), 1155-1165.
  • Micke, W. C. (1996). Almond production manual (Vol. 3364). California: University of California, Division of Agriculture and Natural Resources.
  • Nain, S. S., Sihag, P., & Luthra, S. (2018). Performance evaluation of fuzzy-logic and BP-ANN methods for WEDM of aeronautics super alloy. MethodsX, 5, 890–908.
  • Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I—A discussion of principles. Journal of Hydrology, 10(3), 282–290.
  • Pal, M., & Deswal, S. (2011). Support vector regression based shear strength modelling of deep beams. Computers & Structures, 89(13–14), 1430–1439.
  • Palmer, M. D. (2001). Water quality modeling: A guide to effective practice. Washington, DC: World bank publications.
  • Parsaie, A. (2016). Predictive modeling the side weir discharge coefficient using neural network. Modeling Earth Systems and Environment, 2(2), 63.
  • Parsaie, A., Azamathulla, H. M., & Haghiabi, A. H. (2018). Prediction of discharge coefficient of cylindrical weir–Gate using GMDH-PSO. ISH Journal of Hydraulic Engineering, 24(2), 116-123.
  • Parsaie, A., & Haghiabi, A. (2015). The effect of predicting discharge coefficient by neural network on increasing the numerical modeling accuracy of flow over side weir. Water Resources Management, 29(4), 973–985.
  • Parsaie, A., & Haghiabi, A. H. (2017). Improving modelling of discharge coefficient of triangular labyrinth lateral weirs using SVM, GMDH and MARS techniques. Irrigation Drainage, 66(4), 636–654.
  • Parsaie, A., Najafian, S., Omid, M. H., & Yonesi, H. (2017). Stage discharge prediction in heterogeneous compound open channel roughness. ISH Journal of Hydraulic Engineering, 23(1), 49–56.
  • Rasmussen, C. E., & Williams, C. K. (2006). Gaussian processes for machine learning (Vol. 1, pp. 248). Cambridge: MIT Press.
  • Saha, J. K., Selladurai, R., Coumar, M. V., Dotaniya, M. L., Kundu, S., & Patra, A. K. (2017).oil and its role in the ecosystem. In Saha J. K. et al. (Eds.), Soil pollution – an emerging threat to agriculture (pp. 11–36). Singapore: Springer.
  • Sattari, M. T., Pal, M., Mirabbasi, R., & Abraham, J. (2018). Ensemble of M5 model tree based modelling of sodium adsorption ratio. Journal of AI and Data Mining, 6(1), 69–78.
  • Seilsepour, M., & Rashidi, M. (2008). Modeling of soil sodium adsorption ratio based on soil electrical conductivity. ARPN Journal of Agricultural and Biological Science, 3(5&6), 27–31.
  • Sihag, P. (2018). Prediction of unsaturated hydraulic conductivity using fuzzy logic and artificial neural network. Modeling Earth Systems and Environment, 4(1), 189-198.
  • Sihag, P., Singh, B., Gautam, S., & Debnath, S. (2018c). Evaluation of the impact of fly ash on infiltration characteristics using different soft computing techniques. Applied Water Science, 8(6), 187.
  • Sihag, P., Singh, B., Vand, A. S., & Mehdipour, V. (2018a). Modeling the infiltration process with soft computing techniques. ISH Journal of Hydraulic Engineering, 1–15.
  • Sihag, P., Tiwari, N. K., & Ranjan, S. (2017). Prediction of unsaturated hydraulic conductivity using adaptive neuro-fuzzy inference system (ANFIS). ISH Journal of Hydraulic Engineering, 1–11.
  • Sihag, P., Tiwari, N. K., & Ranjan, S. (2018b). Support vector regression- based modeling of cumulative infiltration of sandy soil. ISH Journal of Hydraulic Engineering, 1–7.
  • Singh, B., Sihag, P., & 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.
  • Singh, B., Sihag, P., Singh, K., & Kumar, S. (2018). Estimation of trapping efficiency of vortex tube silt ejector. International Journal of River Basin Management, 1–38.
  • Smola, A. J. (1996). Regression estimation with support vector learning machines ( Doctoral dissertation, Master’s thesis, Technische Universität München).
  • Sposito, G., & Mattigod, S. V. (1977). On the chemical foundation of the sodium adsorption ratio 1. Soil Science Society of America Journal, 41(2), 323–329.
  • Suarez, D. L. (1981). Relation between pHc and sodium adsorption ratio (SAR) and an alternative method of estimating SAR of soil or drainage waters1. Soil Science Society of America Journal, 45(3), 469–475.
  • Tiwari, N. K., Sihag, P., Kumar, S., & Ranjan, S. (2018). Prediction of trapping efficiency of vortex tube ejector. ISH Journal of Hydraulic Engineering, 1–9.
  • U. S. Salinity Laboratory Staff, L. A. Richards, ed. (1954). Diagosis and improvement of saline and alkali soil(p. 60). U. S. Department of Agriculture Handbook.
  • Üstün, B., Melssen, W. J., & 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, 81(1), 29–40.
  • Vand, A. S., Sihag, P., Singh, B., & Zand, M. (2018). Comparative evaluation of infiltration models. KSCE Journal of Civil Engineering, 22(10), 4173-4184.
  • Vapnik, V. (1998). Statistical learning theory. New York: Wiley.
  • Weiner, E. R. (2012). Applications of environmental aquatic chemistry: A practical guide. Florida: CRC press.