Assessment and Prediction of Groundwater using Geospatial and ANN Modeling

Ankita P. Dadhich (), Rohit Goyal () and Pran N. Dadhich ()
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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

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2021, vol. 35, issue 9, No 11, 2879-2893

Abstract: 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.

Keywords: Groundwater; Water quality; Geospatial; ARIMA; Artificial neural network (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11269-021-02874-8

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