Computer Sciences and Information Technology
Title : | AI/ML-based Qualitative and quantitative assessment of groundwater in Raipur district, Chhattisgarh, Central India |
Area of research : | Computer Sciences and Information Technology |
Focus area : | Environmental Sciences, Artificial Intelligence |
Principal Investigator : | Dr. Sudhakar Singha, Gandhi Institute of Technology and Management (Gitam) University, Andhra Pradesh |
Timeline Start Year : | 2024 |
Timeline End Year : | 2027 |
Contact info : | ssingha@gitam.edu |
Details
Executive Summary : | The sustainable development of groundwater and its management is now a prime concern as because of very limited potable surface water availability. Hence, to secure potable groundwater for future generations, a modern way of sustainable development approach to protecting groundwater resources is of utmost importance. The widely adopted water quality index (WQI) computation method is mainly based on subjective approaches as it requires the inclusion of various quality standards during the estimation of each quality variable weights. As a result, the output of those computed WQI provides an overall scenario of current water quality for any specific region. The proposed research work will focus on objective-based WQI approaches to minimize subjectivity during the computation of WQI. Monitoring of water quality involves steps and although, being a time taking and tedious job, the conventional method is practiced in most of cases frequently. In this approach, huge manpower is required and cost of work is also a notable concern. Hence, to minimize this effort and achieve an economical approach, the application of Artificial Intelligence/Machine Learning (AI/ML) for the prediction of water quality can be a robust way to assess the water quality. The proposed work will be to assess groundwater quality by applying different AI/ML models. Based on the prediction performances through various AI/ML models the optimal model algorithm can be suggested as the best AI/ML model to assess groundwater quality. The assessment of groundwater availability mainly depends on rainfall and other favorable surface as well as sub-surface features. Assessment of groundwater potentiality comprising vertical electrical sounding is a very popular method to achieve precise results. In some of the research work, application of RS and GIS is also been used for delineation of Groundwater Potential Zones (GPZ) by developing geospatial model. The development of geospatial model requires different potential factors. However, the selection of these specific features mainly depends on the availability of relevant data for any particular area. The proposed work will be focused on to predict the GPZ by applying AI/ML models. In the proposed work, GPZ prediction model will be developed by using geospatial technique. A GPZ prediction model will also be developed by building algorithms of newly developed AI/ML models. This proposed prediction model will be a ready reckoner for the assessment of GPZ for areas with similar areas with geological as well as hydro-geological features. Contamination in drinking water mainly causes various severe human diseases and also is potential threat to the local population. Long-term consumption of polluted water may lead to cancer in various parts of the human body. The proposed work will also focus in the human health risk aspect, where elevated toxic chemicals will be considered for the non-carcinogenic as well as carcinogenic human health risk analysis. |
Co-PI: | Dr. Soumya Sucharita Singha, KG Reddy College Of Engineering And Technology, Telangana, Hyderabad-500075 |
Total Budget (INR): | 29,81,264 |
Organizations involved