Implication of Artificial Intelligence Approach On Groundwater Level


  • Pratyush Saini Research Scholar, Department of Environment & Sustainable Development, Banaras Hindu University, Varanasi, India
  • Rajani Srivastava Assistant Professor, Department of Environment & Sustainable Development, Banaras Hindu University, Varanasi, India


Artificial intelligence, Artificial neural network, Climate change, Groundwater level forecasting


Groundwater is considered as one of the most important water resources for humans and environment, but now this is declining at faster rate. Various models have been developed to analyze the hydrological aspects of water resources. Due to complexity in data acquisition and extensive data requirement for physical models, they are very hard to model the water resources problems. Now-a-days with the development of advanced computational techniques artificial intelligence or machine learning has emerged as a new field in data science or data mining. Artificial neural network is one such part of artificial intelligence. This is very simple approach in machine learning/artificial intelligence which requires preliminary knowledge of problem and solution and then predicts the upcoming solutions arising due to different future problems. The research aims to simulate and model groundwater level and finding how much impact climate change is imposing in groundwater scenario using Artificial Intelligence as an alternative approach over physical based models. The climate change parameters (rainfall, solar radiation, maximum temperature, minimum temperature) were obtained through regional climate model (REMO) RCP 4.5 scenario. Water table data for historical scenario was obtained from India-WRIS a web-based GIS for water information developed by National Remote Sensing Center (NRSC). The findings of study show that climate change has significant impact on groundwater level depletion. Not only humans are responsible but also climate change plays a significant role in groundwater table fall.


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How to Cite

P. Saini and R. Srivastava, “Implication of Artificial Intelligence Approach On Groundwater Level”, IJRESM, vol. 3, no. 8, pp. 356–359, Aug. 2020.