Revolutionizing Groundwater Suitability with AI-Driven Spatial Decision Support—A Remote Sensing and GIS Approach for Visakhapatnam District, Andhra Pradesh, India — Oak Academic Publishing
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Revolutionizing Groundwater Suitability with AI-Driven Spatial Decision Support—A Remote Sensing and GIS Approach for Visakhapatnam District, Andhra Pradesh, India
Department of Geology, College of Science and Technology, Andhra University, Visakhapatnam, India
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Department of Geology, College of Science and Technology, Andhra University, Visakhapatnam, India
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Department of Geo-Engineering & RDT, A.U. College of Engineering (A), Andhra University, Visakhapatnam, India
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Department of GeoSciences, University College of Science and Technology, Adikavi Nannaya University, Rajamahendravaram, India
1 Department of Geology, College of Science and Technology, Andhra University, Visakhapatnam, India
2 Department of Geology, College of Science and Technology, Andhra University, Visakhapatnam, India
3 Department of Geo-Engineering & RDT, A.U. College of Engineering (A), Andhra University, Visakhapatnam, India
4 Department of GeoSciences, University College of Science and Technology, Adikavi Nannaya University, Rajamahendravaram, India
This study presents an AI-driven Spatial Decision Support System (SDSS) aimed at transforming groundwater suitability assessments for domestic and irrigation uses in Visakhapatnam District, Andhra Pradesh, India. By employing advanced remote sensing, GIS, and machine learning techniques, groundwater quality data from 50 monitoring wells, sourced from the Central Ground Water Board (CGWB), was meticulously analysed. Key parameters, including pH, electrical conductivity, total dissolved solids, and major ion concentrations, were evaluated against World Health Organization (WHO) standards to determine domestic suitability. For irrigation, advanced metrics such as Sodium Adsorption Ratio (SAR), Kelly’s Ratio, Residual Sodium Carbonate (RSC), and percentage sodium (% Na) were utilized to assess water quality. The integration of GIS for spatial mapping and AI models for predictive analytics allows for a comprehensive visualization of groundwater quality distribution across the district. Additionally, the irrigation water quality was evaluated using the USA Salinity Laboratory diagram, providing essential insights for effective agricultural water management. This innovative SDSS framework promises to significantly enhance groundwater resource management, fostering sustainable practices for both domestic use and agriculture in the region.
KeywordsGroundwater SuitabilityGeospatial AnalysisGeospatial Modeling of Water QualitySpatial Decision Support SystemRemote SensingMachine LearningVisakhapatnam District
Gleick, P.H. (1996) Water Resources. In: Schneider, S.H., Ed., Encyclopedia of Climate and Weather , Oxford University Press, 817-823.
UNESCO (2012) Managing Water under Uncertainty and Risk. The United Nations World Water Development Report 4.
Custodio, E. (2002) Groundwater Quality Assessment and Management. Water Science and Technology , 45, 1-10.
Mohan, K. and Sinha, S. (2016) Groundwater Quality Issues in India: A Review. Environmental Monitoring and Assessment , 188, 1-14.
Choudhury, A., Bhatia, M. and Yadav, B. (2018) Industrial and Domestic Effluents: Impact on Groundwater Quality. Journal of Water and Health , 16, 36-48.
Bureau of Indian Standards (BIS) (2012) IS 10500: Drinking Water Specification.
Sharma, A., Kumar, P. and Gupta, S. (2021) Impact of Anthropogenic Activities on Groundwater Quality in Visakhapatnam District. Environmental Earth Sciences , 80, 1-15.
Rao, D.S. and Kumar, M. (2017) Hydrochemical Assessment of Groundwater in Visakhapatnam District, India. International Journal of Water Resources Development , 33, 151-164.
Jayanthi, K., Sangeetha, K. and Rajasekar, K. (2020) Salinity and Its Impact on Groundwater Quality and Agricultural Productivity. Journal of Environmental Science and Technology , 13, 45-56.
Fischer, J., Huber, M. and Maier, H. (2020) Using GIS and Remote Sensing for Groundwater Quality Assessment. Journal of Environmental Management , 259, Article ID: 110041.
Mishra, A., Verma, S. and Tiwari, S. (2019) Integrating Remote Sensing and GIS for Groundwater Quality Assessment. Journal of Hydrology , 575, 123-135.
Papadopoulos, A. and Kordatos, A. (2018) AI-Driven Decision Support Systems for Groundwater Management: A Review. Water , 10, Article 234.
Zhou, Y. and Liu, W. (2020) Applications of Machine Learning in Water Quality Assessment: A Review. Water Research , 168, Article ID: 115124.
Zhu, M.Y., Wang, J.W., Yang, X. et al. (2022) A Review of the Application of Machine Learning in Water Quality Evaluation. Eco-Environment & Health , 1, 107-116. https://doi.org/10.1016/j.eehl.2022.06.001
Ahmed, S., Khan, T. and Sharma, V. (2022) Enhancing Stakeholder Collaboration in Groundwater Management Using AI-Driven Decision Support Systems. Journal of Water Policy and Governance , 24, 45-60.
Bharath, M., Reddy, G. R. and Suresh, K. (2021) Groundwater Quality Assessment Using GIS and Remote Sensing Techniques in Urban Areas: A Case Study of Visakhapatnam District. Water Research , 186, 115-128.
Sharma, P., Singh, D. and Das, S. (2020) Assessment of Groundwater Quality and Suitability in the Indo-Gangetic Plain: A Remote Sensing and GIS Approach. Applied Water Science , 10, 56-70.
Rani, A., Patel, P. and Sharma, N. (2022) AI-Driven Approaches for Sustainable Water Resource Management. Water Resources Management , 36, 215-234.
Ghosh, S., Sharma, R. and Kumar, S. (2023) Machine Learning Applications in Groundwater Management: A Comprehensive Review. Hydrology , 5, 98-110.
Kumar, V. and Gupta, A. (2021) Predicting Groundwater Quality Using Machine Learning Algorithms: A Case Study in India. Environmental Monitoring and Assessment , 193, 78.
Subramanian, R., Mohan, A. and Kumar, R. (2019) Assessing Groundwater Salinity and Its Impact on Soil: A Case Study in Coastal Regions. Journal of Soil and Water Conservation , 74, 640-649.
Saha, D., Choudhury, A. and Dey, S. (2022) Spatial Analysis of Groundwater Quality Using GIS Techniques in Eastern India. Environmental Science and Pollution Research , 29, 13610-13625.
Sharma, R., Gupta, A. and Verma, P. (2018) Impact of Anthropogenic Activities on Groundwater Quality in Semi-Arid Regions of India. Environmental Science and Pollution Research , 25, 6732-6743.
Kumar, S., Mishra, S. and Singh, N. (2020) Groundwater Contamination and Public Health Implications in Urban and Rural Areas of India. Journal of Water Resources and Environmental Engineering , 45, 182-194.
WHO (2021) Guidelines for Drinking-Water Quality. World Health Organization.
Kumar, J. et al . (2021) Assessment of Groundwater Quality for Drinking and Irrigation Purpose using Geospatial and Statistical techniques in a Semi-arid Region of Rajasthan, India. Journal of the Geological Society of IndiaSearch Dropdown Menu , 97, 405-415. https://doi.org/10.1007/s12594-021-1699-x
Pandey, V., Kumar, S. and Tiwari, A.K. (2019) Evaluation of Groundwater Quality Using Water Quality Index and GIS: A Case Study of Ranchi City, India. Environmental Earth Sciences , 78, 13.
Hamid, S.H., Golub, A., and Leung, H.H. (2021) Application of Machine Learning Models in Predicting Water Quality Parameters: A Review. Journal of Environmental Management , 290, e112668.
Chakraborty, P., Ramesh, R. and Nath, B. (2016) Geochemical Controls on Uranium Contamination in Groundwater of Eastern India. Journal of Environmental Geochemistry , 23, 189-204.
Gupta, S., Yadav, R. and Singh, M. (2021) Fluoride Hotspots in Groundwater of India: A Systematic Review. Hydrogeology Journal , 29, 1267-1280.
Ahmed, S., Khan, M. and Ullah, F. (2022) Integrating Artificial Intelligence with Remote Sensing for Groundwater Quality Prediction: A Multi-Parameter Approach. Journal of Hydrology , 610, Article ID: 127801. https://doi.org/10.1016/j.jhydrol.2022.127801
Zhou, L., Wang, X. and Zhang, H. (2021) Resilient Water Management Frameworks Enabled by Artificial Intelligence and Big Data Technologies. Sustainable Water Resources Management , 37, 401-418.