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Assessment and Prediction of Groundwater using Geospatial and ANN Modeling

Author

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  • Ankita P. Dadhich

    (Malaviya National Institute of Technology, J.L.N. Marg)

  • Rohit Goyal

    (Malaviya National Institute of Technology, J.L.N. Marg)

  • Pran N. Dadhich

    (Poornima Institute of Engineering & Technology)

Abstract

In semi-arid regions, the deterioration in groundwater quality and drop in water level upshots the importance of water resource management for drinking and irrigation. Therefore geospatial techniques could be integrated with mathematical models for accurate spatiotemporal mapping of groundwater risk areas at the village level. In the present study, changes in water level, quality patterns, and future trends were analyzed using eight years (2012–2019) groundwater data for 171 villages of the Phagi tehsil, Jaipur district. Kriging interpolation method was used to draw spatial maps for the pre-monsoon season. These datasets were integrated with three different time series forecasting models (Simple Exponential Smoothing, Holt's Trend Method, ARIMA) and Artificial Neural Network models for accurate prediction of groundwater level and quality parameters. Results reveal that the ANN model can describe groundwater level and quality parameters more accurately than the time series forecasting models. The change in groundwater level was observed with more than 4.0 m rise in 81 villages during 2012–2013, whereas ANN predicted results of 2023–2024 predict no rise in water level > 4.0 m. However, based on predicted results of 2024, the water level will drop by more than 6.0 m in 16 villages of Phagi. Assessment of water quality index reveals unfit groundwater in 74% villages for human consumption in 2024. This time series and projected groundwater level and quality at the micro-level can assist decision-makers in sustainable groundwater management.

Suggested Citation

  • Ankita P. Dadhich & Rohit Goyal & Pran N. Dadhich, 2021. "Assessment and Prediction of Groundwater using Geospatial and ANN Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2879-2893, July.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:9:d:10.1007_s11269-021-02874-8
    DOI: 10.1007/s11269-021-02874-8
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    References listed on IDEAS

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    1. Ozgur Kisi & Armin Azad & Hamed Kashi & Amir Saeedian & Seyed Ali Asghar Hashemi & Salar Ghorbani, 2019. "Modeling Groundwater Quality Parameters Using Hybrid Neuro-Fuzzy Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 847-861, January.
    2. Sudhir Singh & Prashant Srivastava & Avinash Pandey & Sandeep Gautam, 2013. "Integrated Assessment of Groundwater Influenced by a Confluence River System: Concurrence with Remote Sensing and Geochemical Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(12), pages 4291-4313, September.
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    1. Shima Kamali & Keyvan Asghari, 2023. "The Effect of Meteorological and Hydrological Drought on Groundwater Storage Under Climate Change Scenarios," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(8), pages 2925-2943, June.
    2. Danieli Mara Ferreira & Marcelo Coelho & Cristovao Vicente Scapulatempo Fernandes & Eloy Kaviski & Daniel Henrique Marco Detzel, 2021. "Deterministic and Stochastic Principles to Convert Discrete Water Quality Data into Continuous Time Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(11), pages 3633-3647, September.
    3. Lidia Yadira Perez-Aguilar & Wenseslao Plata-Rocha & Sergio Alberto Monjardin-Armenta & Cuauhtémoc Franco-Ochoa, 2022. "Aridity Analysis Using a Prospective Geospatial Simulation Model in This Mid-Century for the Northwest Region of Mexico," Sustainability, MDPI, vol. 14(22), pages 1-22, November.
    4. Saeed Mozaffari & Saman Javadi & Hamid Kardan Moghaddam & Timothy O. Randhir, 2022. "Forecasting Groundwater Levels using a Hybrid of Support Vector Regression and Particle Swarm Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 1955-1972, April.
    5. Yuqi Dong & Jianzhou Wang & Xinsong Niu & Bo Zeng, 2023. "Combined water quality forecasting system based on multiobjective optimization and improved data decomposition integration strategy," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 260-287, March.

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