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Research Article

ECB. 2022; 11(11): 90-97


CLASSIFICATION OF NITRATE CHEMICALCONCENTRATION IN GROUND WATER USING CLOUD BASED ARTIFICIAL INTELLIGENCE

D. Leela Dharani, Jagadeesan.S, N.Bindu Madhavi, Senthilkumar N C,M. Sunil Kumar, K.Prabhu.




Abstract

In order to ensure effective management and the prevention of groundwater contamination within the watershed, it is required to conduct an accurate susceptibility study. Because it is a resource that is necessary for human existence, the creation of goods from agriculture, and the operation of machinery in industrial settings, groundwater needs to be adequately monitored and preserved for future use. Therefore, it is of the utmost importance that an accurate evaluation of the groundwater contamination vulnerability index be performed using ResNet-18. The groundwater vulnerability index is a helpful tool for analyzing the state of the environment in various parts of the world, and the ResNet-18 model is able to lend a hand in the effort to accomplish this goal. The research indicated that ResNet-18 was a powerful tool for improving the evaluation of groundwater contamination vulnerability, and that it may help in ensuring the safety of the environment by reducing the likelihood of contamination. These findings were presented in the form of conclusions that were drawn from the research. This was demonstrated by the fact that it assisted in improving the assessment of the potential for contaminating groundwater supplies.

Key words: Chemical, Nitrate, ResNet, Deep Learning






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