Fitting probability distributions to component water quality data from a treatment plant

Nwaiwu, E Nkeiruka; Bitrus, A.

doi:10.4314/gjes.v4i2.2457

Cited by 2 publications

(1 citation statement)

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“…The probability distribution model helps to analyze and interpret spatial and temporal variations of hydro-morphological as well as physical and chemical parameters of groundwater easily and can serve as a reference model for future investigations (Kishore et al, 2011). Commonly used probability distributions known as normal, lognormal, gamma, Weibull, and log-logistic distributions have been applied to the analysis of rainfall and surface water datasets (Nwaiwu et al, 2005;Maryam et al, 2018;Karim and Hossein 2019). Recently, several probabilistic risk assessment models were to evaluate groundwater quality (Roshni et al, 2020;Opoku et al, 2020;Suleiman et al, 2020a).…”

Section: Introductionmentioning

confidence: 99%

Monitoring Groundwater Quality using Probability Distribution in Gwale, Kano state, Nigeria

Ibrahim

Suleiman

Abdullahi

et al. 2021

JOSMA

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Groundwater is the water present beneath the earth’s surface in soil pore spaces and the fractures of rock formations. Establishing a probability distribution that provides a good fit to groundwater quality has recently become a topic of interest in the fields of hydrology, meteorology among others. In this paper, three groundwater datasets including calcium, magnesium, and chloride are fitted to the normal, lognormal, gamma, Weibull, logistic, and log-logistic distributions to select the best groundwater model. The measures of goodness of fits such as the Akaike information criterion (AIC), Bayesian information criterion (BIC), and log-likelihood are computed to compare the fitted models. The results show that the gamma distribution gives better fits for calcium and magnesium datasets while the lognormal distribution provides a better fit for the chloride dataset than other competing models. This research describes an application of probability distributions and the best-fitted distribution to a practical problem involving groundwater data analysis. By assuming the distribution of data, analysts can utilize the characteristics of the distribution to make predictions on outcomes.

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Section: Introductionmentioning

confidence: 99%