TY  - JOUR
AU  - Nair, Rafiqul
AU  - Ahmed, Thabo
AU  - Kumar, Akira
AU  - Islam, Ananya
TI  - Analysis of Groundwater Arsenic Contamination using Adaptive Neuro-Fuzzy Inference
JO  - Open Access Journal of Engineering Applications
PY  - 2018
VL  - 42
IS  - 2
SP  - 91
EP  - 112
AB  - 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.
KW  - groundwater arsenic contamination
KW  - adaptive neuro-fuzzy inference
KW  - state of charge
KW  - support vector machine
KW  - seismic analysis
KW  - corrosion
SN  - 3105-7004
DO  - 10.55555/oajea.2018.4202.007
UR  - https://journal.bsaps.com/article/9524
ER  -