Performance Evaluation of South Esk Hydrological Sensor Web: Unsupervised Machine Learning and Semantic Linked Data Approach

Abstract

Technological progress has lead the sensor network domain to an era where environmental and agricultural domain applications are completely dependent on hydrological sensor networks. Data from the sensor networks are being used for knowledge management and critical decision support system. The quality of data can, however, vary widely. Existing automated quality assurance approach based on simple threshold rulebase could potentially miss serious errors requiring robust and complex domain knowledge to identify. This paper proposes a linked data concept, unsupervised pattern recognition, and semantic ontologies based dynamic framework to assess the reliability of hydrological sensor network and evaluate the performance of the sensor network. Newly designed framework is used successfully to evaluate the South Esk hydrological sensor web in Tasmania, indicating that domain ontology based linked data approach could be a very useful methodology for quality assurance of the complex data.