Semantics orientated spatial temporal data mining for water resource decision support

Water resource management is becoming more complex and relies heavily on computer software processing to help data queries for common and rare patterns for analyzing critical water events. For example, it is vital for decision makers to know if certain types of water quality problems are isolated (e.g. rare) or ubiquitous (e.g. common) and whether the conditions are changing spatially or temporally for a proper management plan. This thesis aims to automatically detect spatiotemporal common and rare patterns by significantly addressing the uncertainty and heterogeneity in water quality data, in order to enhance the accuracy and efficiency of common and rare pattern mining models underpinning many of the water resource management strategies and planning decisions. Therefore, we propose two novel semantics-oriented mining methods: the Correcting Imprecise Readings and Compressing Excrescent Points (CIRCE) method and the Exceptional Object Analysis for Finding Rare Environmental Events (EOAFREE) method. The CIRCE method resolves uncertainty problems in retrieving common patterns based on spatiotemporal semantic points, such as inflexions. The EOAFREE method tackles the heterogeneity problem by summarizing raw water data into a water quality index, that is, water semantics, in discovering rare patterns. We demonstrate the efficiency and effectiveness of the two methods by using simulation and real world datasets, and then implement them in a Semantics-Oriented Mining Application for Detecting Water Quality Events (SOMAwater) prototype system, which is used to query spatiotemporal common and rare patterns for a real world water quality dataset of 93 sites in 10 river basins in Victoria, Australia from 1975 to 2010