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Modeling Soil Water Retention Under Different Pressures Using Adaptive Neuro-Fuzzy Inference System

Author

Listed:
  • Ahmed Elbeltagi

    (Mansoura University)

  • R. K. Jaiswal

    (National Institute of Hydrology)

  • R. V. Galkate

    (National Institute of Hydrology)

  • Manish Kumar

    (Dr. R.P.C.A.U.)

  • A. K. Lohani

    (National Institute of Hydrology)

  • Jaiveer Tyagi

    (National Institute of Hydrology)

Abstract

Soil Water Retention (SWR) is an important process in drainage, surface, and groundwater partitioning, hydrological modeling, water supply for irrigation, etc. Assessment of SWR characteristics is complex and difficult to conduct spatially in varied locations. Therefore, Pedotransfer Functions (PTF) which are empirical relations with easily available physical properties are commonly used. In the present study, the evaluation of soil moisture at different suction pressure using the adaptive neuro-fuzzy inference systems (ANFIS) approach based on soil texture (percentage of gravel, sand, silt, and clay) and compare with the PTF approach. The analysis was conducted for a total of eleven sites of two adjoining commands in India. The pressure plate apparatus along with coarse and fine sieve analysis, titration, and other tests were carried out to determine SWR, texture, organic carbon, and bulk density. The comparative analysis of Nash–Sutcliffe efficiencies of the best-fitted PTF models and ANFIS model confirmed that the ANFIS model can capture all variations of soil texture across all sites with Nash–Sutcliffe efficiency of nearly 1.0 indicative of an exact match, while no single PTF-based model can be used for all the sites. Therefore, the ANFIS model can be used to model soil water retention for the central India region using easily available texture properties of soils.

Suggested Citation

  • Ahmed Elbeltagi & R. K. Jaiswal & R. V. Galkate & Manish Kumar & A. K. Lohani & Jaiveer Tyagi, 2023. "Modeling Soil Water Retention Under Different Pressures Using Adaptive Neuro-Fuzzy Inference System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(4), pages 1519-1538, March.
  • Handle: RePEc:spr:waterr:v:37:y:2023:i:4:d:10.1007_s11269-023-03439-7
    DOI: 10.1007/s11269-023-03439-7
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    References listed on IDEAS

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    1. Mehrbakhsh Nilashi & Fausto Cavallaro & Abbas Mardani & Edmundas Kazimieras Zavadskas & Sarminah Samad & Othman Ibrahim, 2018. "Measuring Country Sustainability Performance Using Ensembles of Neuro-Fuzzy Technique," Sustainability, MDPI, vol. 10(8), pages 1-20, August.
    2. Tabbi Wilberforce & Abdul Ghani Olabi, 2020. "Performance Prediction of Proton Exchange Membrane Fuel Cells (PEMFC) Using Adaptive Neuro Inference System (ANFIS)," Sustainability, MDPI, vol. 12(12), pages 1-16, June.
    3. Reham Alhindawi & Yousef Abu Nahleh & Arun Kumar & Nirajan Shiwakoti, 2019. "Application of a Adaptive Neuro-Fuzzy Technique for Projection of the Greenhouse Gas Emissions from Road Transportation," Sustainability, MDPI, vol. 11(22), pages 1-17, November.
    4. Jinliang Zhang & YiMing Wei & Zhong-fu Tan & Wang Ke & Wei Tian, 2017. "A Hybrid Method for Short-Term Wind Speed Forecasting," Sustainability, MDPI, vol. 9(4), pages 1-10, April.
    5. Panapakidis, Ioannis P. & Dagoumas, Athanasios S., 2017. "Day-ahead natural gas demand forecasting based on the combination of wavelet transform and ANFIS/genetic algorithm/neural network model," Energy, Elsevier, vol. 118(C), pages 231-245.
    6. Zhongrong Zhang & Yiliao Song & Feng Liu & Jinpeng Liu, 2016. "Daily Average Wind Power Interval Forecasts Based on an Optimal Adaptive-Network-Based Fuzzy Inference System and Singular Spectrum Analysis," Sustainability, MDPI, vol. 8(2), pages 1-30, January.
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