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Application of logistic regression analysis in prediction of groundwater vulnerability in gold mining environment: a case of Ilesa gold mining area, southwestern, Nigeria
详细信息       发布日期:2021年2月18日
  • 标题:Application of logistic regression analysis in prediction of groundwater vulnerability in gold mining environment: a case of Ilesa gold mining area, southwestern, Nigeria
  • 关键词:Logistic regression analysis. Groundwater,vulnerability . Hydrogeological, indices. Groundwater,quality . Groundwater contamination
  • 作者:K. A. N. Adiat & B. E. Akeredolu , A. A. Akinlalu & G.M. Olayanju

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内容简介

Reports of environmental problems occasioned from gold mining activities had prompted the groundwater vulnerability prediction/assessment of the study area. This was with a view to identifying factors responsible for the probability of groundwater contamination as well as developing empirical (LR) model and map that predict the probability of occurrence of contaminant(s) with respect to threshold level in the groundwater resources in the study area. In order to achieve the objectives of the study, logistic regression was applied to independent variables obtained from results of the analysis of remote sensing and geophysical data on one hand and dependent variables obtained from analysis of water samples on the other hand. The results of the analysis obtained from water chemistry established that all the physio-chemical parameters and major metallic ions are within the permissible limit. However, zinc concentration (Zn), being the only dependent variable that had two categorical outcomes, was the contaminant utilized for the study. Similarly, only five (5) independent (predictive) variables, which are percent clay in soil, drainage, slope, unsaturated zone thickness, and total longitudinal conductance, were established to have good correlation and statistically significant with the dependent variable, the contaminant, and thus utilized in logistic regression model development. The quantitative assessment of the developed model established that the overall model prediction accuracy was 85.7% suggesting that the model had a very good fit. The probability prediction model was also accurate and reliable with percentage reliability established to be 90%. In conclusion, it is evident from the results obtained from the study that since the model developed was assessed to be accurate and reliable, the model, and hence the technique, can be replicated in another area of similar geologic condition.

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