On the Application of the Statistical and Fuzzy Systems Regression and Clustering to Analyze the Multivariate Chemical Composition of Groundwater: Case Study of Arsenic-Contaminated Groundwater in Bangladesh

Arsenic (As) contamination in groundwater in Bangladesh has significantly impacted public health. This chapter aims to demonstrate how a combination of traditional statistical and fuzzy systems analysis can describe the relationship between arsenic concentration and other variables derived from the chemical analysis of water samples collected in 1997. This chapter also includes a clustering zonation of the studied area in Bangladesh. A fuzzy systems analysis serves as a valuable tool to improve statistical analysis by enabling the inclusion of vague or imprecise data, which standard statistical methods may have difficulty addressing. The fuzzy regression and clustering models are applied to identify the relationship between the chemical changes of the coastal aquifer due to sea level rise and the increased release of arsenic from sediments into Bangladesh’s drinking well water. Overall, this chapter highlights the potential of combining statistical and fuzzy systems analysis to address the pressing issue of groundwater arsenic contamination in Bangladesh. By incorporating uncertainty and imprecision, these advanced modeling techniques can provide a more comprehensive understanding of the factors driving arsenic contamination and enable more effective and targeted interventions to protect public health.