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Prediction of Sodium Hazard of Irrigation Purpose using Artificial Neural Network Modelling

Author

Listed:
  • Vinay Kumar Gautam

    (Department of Soil and Water Engineering, CTAE, Maharana Pratap University of Agriculture and Technology, Udaipur 313001, Rajasthan, India)

  • Chaitanya B. Pande

    (Indian Institute of Tropical Meteorology, Pune 411008, Maharashtra, India
    Abdullah Alrushaid Chair for Earth Science Remote Sensing Research, Geology and Geophysics Department, College of Science, King Saud University, Riyadh 11451, Saudi Arabia)

  • Kanak N. Moharir

    (Department of Earth Science, Banasthali University, Jaipur 302001, Rajasthan, India)

  • Abhay M. Varade

    (Department of Geology, Rashtrasant Tukadoji Maharaj Nagpur University, Nagpur 440001, Maharashtra, India)

  • Nitin Liladhar Rane

    (Vivekanand Education Society’s College of Architecture, Mumbai 400074, Maharashtra, India)

  • Johnbosco C. Egbueri

    (Department of Geology, Chukwuemeka Odumegwu Ojukwu University, Uli 6059, Nigeria)

  • Fahad Alshehri

    (Abdullah Alrushaid Chair for Earth Science Remote Sensing Research, Geology and Geophysics Department, College of Science, King Saud University, Riyadh 11451, Saudi Arabia)

Abstract

The present study was carried out using artificial neural network (ANN) model for predicting the sodium hazardness, i.e., sodium adsorption ratio (SAR), percent sodium (%Na) residual, Kelly’s ratio (KR), and residual sodium carbonate (RSC) in the groundwater of the Pratapgarh district of Southern Rajasthan, India. This study focuses on verifying the suitability of water for irrigational purpose, wherein more groundwater decline coupled with water quality problems compared to the other areas are observed. The southern part of the Rajasthan State is more populated as compared to the rest of the parts. The southern part of the Rajasthan is more populated as compared to the rest of the Rajasthan, which leads to the industrialization, urbanization, and evolutionary changes in the agricultural production in the southern region. Therefore, it is necessary to propose innovative methods for analyzing and predicting the water quality (WQ) for agricultural use. The study aims to develop an optimized artificial neural network (ANN) model to predict the sodium hazardness of groundwater for irrigation purposes. The ANN model was developed using ‘nntool’ in MATLAB software. The ANN model was trained and validated for ten years (2010–2020) of water quality data. An L-M 3-layer back propagation technique was adopted in ANN architecture to develop a reliable and accurate model for predicting the suitability of groundwater for irrigation. Furthermore, statistical performance indicators, such as RMSE, IA, R, and MBE, were used to check the consistency of ANN prediction results. The developed ANN model, i.e., ANN4 (3-12-1), ANN4 (4-15-1), ANN1 (4-5-1), and ANN4 (3-12-1), were found best suited for SAR, %Na, RSC, and KR water quality indicators for the Pratapgarh district. The performance analysis of the developed model (3-12-1) led to a correlation coefficient = 1, IA = 1, RMS = 0.14, and MBE = 0.0050. Hence, the proposed model provides a satisfactory match to the empirically generated datasets in the observed wells. This development of water quality modeling using an ANN model may help to useful for the planning of sustainable management and groundwater resources with crop suitability plans as per water quality.

Suggested Citation

  • Vinay Kumar Gautam & Chaitanya B. Pande & Kanak N. Moharir & Abhay M. Varade & Nitin Liladhar Rane & Johnbosco C. Egbueri & Fahad Alshehri, 2023. "Prediction of Sodium Hazard of Irrigation Purpose using Artificial Neural Network Modelling," Sustainability, MDPI, vol. 15(9), pages 1-17, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7593-:d:1140012
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    References listed on IDEAS

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    2. Arun Pratap Mishra & Sipu Kumar & Rounak Patra & Amit Kumar & Himanshu Sahu & Naveen Chandra & Chaitanya B. Pande & Fahad Alshehri, 2023. "Physicochemical Parameters of Water and Its Implications on Avifauna and Habitat Quality," Sustainability, MDPI, vol. 15(12), pages 1-17, June.

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