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Development of a Predictive Equation for Modelling the Infiltration Process Using Gene Expression Programming

Author

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  • Tabasum Rasool

    (National Institute of Technology Srinagar, Hazratbal)

  • A. Q. Dar

    (National Institute of Technology Srinagar, Hazratbal)

  • M. A. Wani

    (HMAARI)

Abstract

In this study, the soft computing technique of Gene expression programming (GEP) has been employed to generate a predictive equation of infiltration rate (fp). Infiltration experiments were conducted at 124 different sites and soil samples were collected to assess various soil properties throughout the Himalayan lake catchment. Parameters determined from observed data using nonlinear-Levenberg Marquardt algorithm were substituted in Horton, Kostiakov and Philip infiltration models and fp were predicted. Using soil data generated by laboratory investigation of soil samples, the GEP model was developed. Training and testing of the GEP model was performed using 70% and 30% of data respectively. Performance of GEP developed functional relationship was evaluated by comparing predictions from it and aforementioned infiltration models with field observed fp, and by applying overall performance index (OPI) computed using Coefficient of Determination (R2), Nash–Sutcliffe Efficiency (ENS), Willmott’s Index of Agreement (W), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Expression developed using GEP indicated feasibility of developed equation with ENS, R2, W, RMSE and MAE of 0.84, 0.84, 0.96, 1.9, and 0.8, respectively for training data-set and 0.84, 0.85, 0.95, 1.2, and 0.95, respectively for testing data-set. Comparative analysis revealed that though with a slightly higher OPI value (0.7–0.8), the performance of conventional models is better compared to the GEP model (0.66) but the GEP model having satisfactory performance may be used for fp prediction particularly in absence of observed data.

Suggested Citation

  • Tabasum Rasool & A. Q. Dar & M. A. Wani, 2021. "Development of a Predictive Equation for Modelling the Infiltration Process Using Gene Expression Programming," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1871-1888, April.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:6:d:10.1007_s11269-021-02816-4
    DOI: 10.1007/s11269-021-02816-4
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    References listed on IDEAS

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    1. Seydou Traore & Yufeng Luo & Guy Fipps, 2017. "Gene-Expression Programming for Short-Term Forecasting of Daily Reference Evapotranspiration Using Public Weather Forecast Information," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4891-4908, December.
    2. Ravindra Kale & Bhabagrahi Sahoo, 2011. "Green-Ampt Infiltration Models for Varied Field Conditions: A Revisit," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(14), pages 3505-3536, November.
    3. Seydou Traore & Aytac Guven, 2012. "Regional-Specific Numerical Models of Evapotranspiration Using Gene-Expression Programming Interface in Sahel," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(15), pages 4367-4380, December.
    4. Prabeer Parhi & S. Mishra & Ranvir Singh, 2007. "A Modification to Kostiakov and Modified Kostiakov Infiltration Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(11), pages 1973-1989, November.
    5. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    6. Hazi Azamathulla & Aminuddin Ghani & Cheng Leow & Chun Chang & Nor Zakaria, 2011. "Gene-Expression Programming for the Development of a Stage-Discharge Curve of the Pahang River," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(11), pages 2901-2916, September.
    7. Damodhara Rao, M. & Raghuwanshi, N.S. & Singh, R., 2006. "Development of a physically based 1D-infiltration model for irrigated soils," Agricultural Water Management, Elsevier, vol. 85(1-2), pages 165-174, September.
    8. Aytac Guven & Özgür Kişi, 2011. "Estimation of Suspended Sediment Yield in Natural Rivers Using Machine-coded Linear Genetic Programming," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(2), pages 691-704, January.
    9. Shoja Ghorbani Dashtaki & Mehdi Homaee & Mohammad Mahdian & Mehdi Kouchakzadeh, 2009. "Site-Dependence Performance of Infiltration Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(13), pages 2777-2790, October.
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    1. Shaohong Li & Peng Cui & Ping Cheng & Lizhou Wu, 2022. "Modified Green–Ampt Model Considering Vegetation Root Effect and Redistribution Characteristics for Slope Stability Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2395-2410, May.

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