Abstract
A new methodology is developed to analyse existingwater quality monitoring networks. This methodology incorporates different aspects ofmonitoring, including vulnerability/probability assessment, environmental health risk, the value of information, and redundancy redu ction. The work starts with a formulation of a conceptual framework for groundwater quality monitoringto represent the methodology’s context. This work presents the development of Bayesian techniques for the assessment of groundwater quality. The primary aim is to develop a predictive model and a computer system to assess and predict the impact of pollutants on the water column. The process of the analysis begins by postulating a model in light of al available knowledge taken from relevant phenomenon. The previous knowledge as represented by the prior distribution of the model parameters is then combined with the new data through Bayes’ theorem to yield the current knowledge represented by the posterior distribution of model parameters. This process of updating information about the unknown model parameters is then repeated in a sequential manner as more and more new information becomes available.
Highlights
Water is an essential requirement for irrigated agriculture, domestic uses, including drinking, cooking and sanitation
The previous knowledge as represented by the prior distribution of the model parameters is combined with the new data through Bayes’ theorem to yield the current knowledge. This process of updating information about the unknown model parameters is repeated in a sequential manner as more and more new information becomes available
The purpose of this section is to apply the classical time series analysis to groundwater quality data and to compare the results with that obtained by the application of Dynamic Bayesian Networks (DBNs)
Summary
Water is an essential requirement for irrigated agriculture, domestic uses, including drinking, cooking and sanitation. The relevance vector machine RVM model has been used in hydrological applications and groundwater quality modelling showed good results These methods ignore the dependencies between water quality variables. This approach incorporates prior knowledge about the possible state of a system, and adds new data in a pre-posterior analysis to produce posterior knowledge of full information about possible system states [1,2,3] This approach enables a comprehensive evaluation of water quality variables and allows establishing public health concepts. Bayesian networks use statistical techniques that tolerate subjectivity and small data sets These methods are simple to apply and have sufficient flexibility to allow reaction to scientific complexity free from impediment from purely technical limitations [8]. In order to overcome this problem and to have representative data, we, used the following modified Bayesian model to that developed by Banerjee, Planting and Ramirez [10], to preprocessing the datasets used for the development of the Bayesian Networks
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