Groundwater resources in Oman are considered very precious and play a great role in the economical development. However, groundwater contamination is one of the major concerns facing the country and, therefore, it needs an accurate measurement technique. In this work, the Bayesian Technique is applied to groundwater quality data sets obtained from various locations in the Salalah area to the south of Oman. This technique emphasizes not only theprobabilistic dependencies between pollutants but also the precision and the accuracy of the tested methods used by environmental laboratories. First, we present a new technique for data preprocessing. Then we describe the network models we developed, as l1ell as the methods used to build these models. Various challenges, such as acquiring groundwater datasets, identifying pollutants and anticipating potential problem contaminants, are addressed. Finally, we present the results of applications of these models.
This work presents the development of Bayesian techniques for the assessment of groundwater quality. Its 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 all 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.
Image retrieval systems are becoming increasingly important in areas of research and commercial use. The storage of digital objects in the traditional databases is considered inadequate because of the extensive precise data required for successful retrieval. In addition, the retrieval process has been implemented using content-based image retrieval (CBIR) that relies on retrieving stored images from a collection by comparing low level features (binary form) that are automatically extracted from the images themselves. Data retrieval requires knowledge of attributes stored along with an adequate and flexible query language. For image repositories and retrieval, we noted that the integration of XML technology and case-based reasoning is more efficient and of great benefit in this area. This is mainly because users both in indexing and retrieval processes, tend to use old cases by associating images that reveal similar features. It is also because XML extends the original theory and offers a flexible approach with accurate data modelling and management tools. In this work, we also used fuzzy reasoning to convert the quantitative attributes into qualitative terms for indexing and retrieval.
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