Probabilistic models for assessing the impact of salinization and chemical pollutants

Development and rapid population growth have impacted Oman's water resources significantly. Increasing the degradation of groundwater quality by salinization and chemical contaminants threaten primary sources of drinking water, especially in the coastal agricultural areas. Hence, there is a substantial demand in the country for water conservation technology. This work elaborates the quality deterioration of groundwater due to chemical contaminants, which are the prime environmental issue of the Salalah region to the south of Oman. First, we describe the development and application of Dynamic Bayesian Networks (DBNs) combine with a preprocessing Bayesian technique to determine the impact of these contaminants on groundwater quality. These paradigm address the probabilistic and dynamic characteristics that are significant in the understanding of pollution generation from various information sources, but in a fashion which manages the uncertainties in these sources. Second, we discuss and compare the results produced by these methods with that produced by the application of classical time series models.