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GLM and joint GLM techniques in hydrogeology: an illustration

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In regression models with positive observations, estimation is often based on either the log–normal or the gamma model. Generalised linear models and joint generalised linear models are appropriate for analysing positive data with constant and non–constant variance, respectively. This article focuses on the use of these two techniques in hydrogeology. As an illustration, groundwater quality factors are analysed. Softness, non–alkalinity, content dissolved oxygen, chemical oxygen demand, chloride content and electrical conductivity are all the basic positive characteristics (i.e., values are positive in nature) for good drinking water. This article identifies the causal factors of these basic quality characteristics of groundwater at Muzaffarpur Town, Bihar, India, using the above techniques. Many statistical significant factors for these six basic quality characteristics of groundwater are detected. In the process, probabilistic model for each characteristic is developed. Effects of different factors on each characteristic are examined.

Keywords: QTL, alkalinity, gamma model, generalised linear models, hardness, joint GLM, JGLM, non–constant variance, data analysis, hydrogeology, groundwater quality, drinking water, India, probabilistic modelling

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