Open Access Journal of Engineering Applications
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Analysis of Groundwater Arsenic Contamination using Adaptive Neuro-Fuzzy Inference

Rafiqul Nair Corresponding 0000-0003-9095-4542
Technical University of Munich
Thabo Ahmed 0000-0001-9444-3784
University of Tehran
Akira Kumar
Universiti Teknologi Malaysia
Ananya Islam 0000-0002-3596-5995
Bangladesh Agricultural University
30 Sep 2018
pp. 91-112
https://doi.org/10.55555/oajea.2018.4202.007

Abstract

There remains a pressing need for cost-effective methods to assess groundwater arsenic contamination in hydropower generation. Performance is benchmarked against conventional practice using metrics relevant to hydropower generation. The strongest effect was associated with groundwater arsenic contamination, which accounted for nearly 8% of the response. The study demonstrates that meaningful improvements are achievable without major capital outlay.
Keywords: groundwater arsenic contaminationadaptive neuro-fuzzy inferencestate of chargesupport vector machineseismic analysiscorrosion

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