Physico‐chemical and bacteriological study of ground water quality of Belpahar area, Odisha, India using artificial neural networks

Abstract

AbstractThe major objective of this work is to determine the degree of contamination of ground water in the Belpahar area of Odisha, India, by calculating WQI, assessing the presence of toxic metals, studying bacteriological analysis, and ultimately predicting the suitability of ground water for human use. A machine learning approach, the ANN model, was used to compare the experimental values with the predicted value of water quality and predict the level of contamination in the future. The level of performance due to modelling was evaluated by applying numerous statistical indices. The whole area was divided into five zones, such as residential, tourist, industrial, village, and coal mining areas, and ten ground water samples were collected in each zone. The WQI of the tourist area is 76.017, the village area is 70.721, the industrial area is 122.423, the residential area is 74.880, and the coal mining area is 115.507, indicating the industrial area is extensively polluted because of the impact of extensive industrialization. From the analysis of metals, it was estimated that toxic metals like Cr6+ (VI), Se, Mn, Zn, and Cu are much higher than the permissible limit, particularly in mining and industrial areas. From bacteriological analysis, it was found that the number of viable E. coli bacteria is highest in the TRL colony, that is, 18.2 × 107, and lowest in village areas such as Sarbahal (17.9 × 107), Ujjalpur (15.5 × 107), and tourist, industrial, and coal mining areas because of fewer human activities. Hence, suitable management, viable remedial measures, and the distribution of ground water in the Belpahar area must be recommended.