Intelligent detection of contaminated soil through sensor fusion

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Accidental spills and contamination while transporting materials through pipelines and underground storage tanks require time-efficient evaluation of contaminated sites. For sustainable development as well as environment conservation, the University of Calgary and Trium Environmental have cooperated to develop an Artificial Intelligence (AI)-assisted multi-sensor fusion system to remedy contaminated sites in an efficient manner. Current practices of soil contamination assessments can take up to several weeks by sending to analytical chemistry labs. In order to investigate a non-destructive measurement of soil parameters on-site, a range of sensors and artificial intelligence/machine learning algorithms will be utilized for data analysis and prediction. Based on the collected data, several AI algorithms will contribute to building a database which contains the preprocessed data to be used for prediction models. By exploring a rapid but precise site-assessment technology, sustainable development can be maintained while allowing efficient responses to contaminated zones for remediation. With cost-efficient analysis of contaminated soil samples and improved environmental remediation, it would enable the partner company to have an advantage in the competition of the global market. The collaborative research will benefit Canada by providing economic, social, and environmental benefits as well as facilitate training of HQP with a variety of professional and technical skills.