Artificial Neural Networks-Based Machine learning for Analysis of Sub-surface Water quality
Ground water is the most important source of water for the entire world. In India, almost 65% of the water needs for various purposes are met by ground water. Improper disposal of several waste, the ground water is being contaminated includes presence of heavy metals and toxic substance. There are other certain technologies adopted to predict the quality of water. The accuracy will not be high due to several factors like climate change, Temperature change, and pollution. Machine Learning technologies can be adapted to predict the quality of water. Different Algorithms are used to train the model and the accuracy of all algorithms is then compared to check which algorithm has the highest accuracy to predict the quality of the water.
Authors
- N.K. TIMOSHENKO N.K.
- J. Hemamalini
- S. Packialakshmi (ORCID: https://orcid.org/0000-0002-6196-7093)
- B. Anuradha (ORCID: https://orcid.org/0000-0003-1641-434X)
- Senthil Vadivelan D (ORCID: https://orcid.org/0000-0003-2596-2567)
- T.UdayaBanu
Institutions
- Vels University (IN)
- Chennai Mathematical Institute (IN)
- Anna University, Chennai (IN)
- Sathyabama Institute of Science and Technology (IN)
Publication Details
- Published
- 2025-03-28
- DOI
- https://doi.org/10.1109/icdsaai65575.2025.11011649
- Primary Topic
- Water Quality Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00