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Article

Neural Network and Random Forest-Based Analyses of the Performance of Community Drinking Water Arsenic Treatment Plants

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School of Environmental Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721 302, India
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School of Water Resources, Indian Institute of Technology Kharagpur, Kharagpur 721 302, India
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Department of Civil Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721 302, India
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Graduate School of Public Health, San Diego State University, San Diego, CA 92182, USA
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Department of Mining Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721 302, India
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Department of Geology and Geophysics, Indian Institute of Technology Kharagpur, Kharagpur 721 302, India
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Authors to whom correspondence should be addressed.
Academic Editors: Caetano C. Dorea and Yanshan Cui
Water 2021, 13(24), 3507; https://doi.org/10.3390/w13243507
Received: 13 September 2021 / Revised: 1 December 2021 / Accepted: 3 December 2021 / Published: 8 December 2021
A plethora of technologies has been developed over decades of extensive research on arsenic remediation, although the technical and financial perspective of arsenic removal plants in the field requires critical evaluation. In the present study, focusing on some of the pronounced arsenic-affected areas in West Bengal, India, we assessed the implementation and operation of different arsenic removal technologies using a dataset of 4000 spatio-temporal data collected from an in-depth field survey of 136 arsenic removal plants engaged in the public water supply. Our statistical analysis of this dataset indicates a 120% rise in the average cumulative capacity of the plants during 2014–2021. The majorities of the plants are based on the activated alumina with FeCl3 technology and serve about 49% of the population in the study area. The average cost of water production for the activated alumina with FeCl3 technology was found to be ₹7.56/m3 (USD $1 ≈ INR ₹70), while the lowest was ₹0.39/m3 for granular ferric hydroxide technology. A machine learning-based framework was employed to analyze the impact of water quality and treatment plant parameters on the removal efficiency, capital, and operational cost of the plants. The artificial neural network model exhibited adequate statistical significance, with a high F-value and R2 of 5830.94 and 0.72 for the capital cost model, 136,954, and 0.98 for the operational cost model, respectively. The relative importance of the process variables was identified through random forest models. The models indicated that flow rate, media, and chemicals are the predominant costs, while contaminant loading in influent water and a coagulating agent was important for removal efficiency. The established framework may be instrumental as a decision-making tool for water providers to assess the expected performance and financial involvement for proposed or ongoing arsenic removal plants concerning various design and quality parameters. View Full-Text
Keywords: groundwater; arsenic removal; cost analysis; removal efficiency; machine learning groundwater; arsenic removal; cost analysis; removal efficiency; machine learning
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MDPI and ACS Style

Bhattacharya, A.; Sahu, S.; Telu, V.; Duttagupta, S.; Sarkar, S.; Bhattacharya, J.; Mukherjee, A.; Ghosal, P.S. Neural Network and Random Forest-Based Analyses of the Performance of Community Drinking Water Arsenic Treatment Plants. Water 2021, 13, 3507. https://doi.org/10.3390/w13243507

AMA Style

Bhattacharya A, Sahu S, Telu V, Duttagupta S, Sarkar S, Bhattacharya J, Mukherjee A, Ghosal PS. Neural Network and Random Forest-Based Analyses of the Performance of Community Drinking Water Arsenic Treatment Plants. Water. 2021; 13(24):3507. https://doi.org/10.3390/w13243507

Chicago/Turabian Style

Bhattacharya, Animesh, Saswata Sahu, Venkatesh Telu, Srimanti Duttagupta, Soumyajit Sarkar, Jayanta Bhattacharya, Abhijit Mukherjee, and Partha S. Ghosal. 2021. "Neural Network and Random Forest-Based Analyses of the Performance of Community Drinking Water Arsenic Treatment Plants" Water 13, no. 24: 3507. https://doi.org/10.3390/w13243507

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