ABSTRACT
Chloride ion is an important indicator of water quality. Field measurement of chloride is difficult whereas laboratory measurement is both time-consuming and chemical intensive. The conservative nature of chloride and good correlation with electrical conductivity (EC) justifies its use as proxy for chloride estimations. Comparison of the best regression models (RMs) and data-driven decision tree (DT) model enables appreciation of relative merits of the two approaches for this purpose. Quantitative improvements over the models from literature are, increase in correlation (RM: 0.70 to 0.77; DT: 0.70 to 0.78) and decrease in relative errors (RM: MARE: 0.88 to 0.65 and RMSRE: 1.91 to 0.92; DT: MARE: 0.88 to 0.40; RMSRE: 1.91 to 0.54); thereby, DT has emerged as the better modeling approach for this case. Considering the influence of seasonality (pre-or post-monsoon) and degree of saturation of soil (water logged or water depleted) enabled the reduction of the correlation range (0.24-0.87) of the basic variables to a smaller range (0.44-0.89) for estimates of Cl-, along with relative error ranging from 0.35 to 0.57, the improvement being more pronounced for lower value of variable correlations. The overall comparison using the evaluation datasets between RM from literature and RM/DT models from this study exemplified that for the study area, the case-specific models developed using the data-driven tool: DT resulted in the most accurate estimation of chloride in groundwater from the chosen proxy: EC.
Subject(s)
Groundwater , Water Pollutants, Chemical , Decision Trees , Environmental Monitoring , Soil , Water Pollutants, Chemical/analysisABSTRACT
Analysis of trend of epidemiological data helps to appreciate the progression of an epidemic and to develop monitoring and control strategies by the government agencies. Sen's Innovative Method suggests a graphical analysis, which can overcome many limitations of data such as short length, non-Gaussian nature, skewness or serial correlation. In this article, this method is applied for the first time on epidemiological data. For the case study, Covid-19 or SARS-CoV-2 data from India were employed. The results show that Sen's Innovative Method is capable of indicating the shift in epidemiological trend quite efficiently, before it is reflected in the time series or moving average plots. The graphical analysis worked particularly well in comparing the trends of monthly data. It is concluded that this method would be especially suitable for monitoring the epidemiological trend by breaking up the data into smaller segments, as was illustrated in the study.
ABSTRACT
Despite some researches indicating the possibility of correlation being induced by the common variable effect, correlation plots of ionic ratio (Na+/Cl-) versus ionic concentration (Cl-) still remain popular for interpreting the causes of groundwater salinization. There were doubts about relevance of spurious correlation in groundwater and its detection using the randomization process, owing to the fact that groundwater is charge-balanced and randomization would result in abnormal ionic ratios. In this context, the relevance of spurious correlation and its detection using randomization of common variable was established in this study, which was missing from the literature. The study used qualitative and quantitative tools for detecting the possibility of induced correlation and demonstrated the efficiency of the proposed method using published datasets from a variety of geochemical processes of groundwater salinization. In five out of the eight cases examined, the correlations observed in the plots appeared to be induced by the common variable effect and, as such, were deemed unreliable as positive indicators of the stated salinization processes. Even when the correlations appear not to be induced, it is recommended to always support the inferences with other independent evidence(s).
