Intelligent detection of contaminated soil through sensor fusion

通过管道和地下储罐运输材料时发生的意外泄漏和污染需要对受污染地点进行及时有效的评估。为了可持续发展和环境保护，卡尔加里大学和Trium Environmental合作开发了一种人工智能(AI)辅助的多传感器融合系统，以有效地修复受污染的场地。目前土壤污染评估的做法可能需要长达几周的时间才能送到分析化学实验室。为了研究现场土壤参数的无损测量，将利用一系列传感器和人工智能/机器学习算法进行数据分析和预测。基于收集的数据，几种人工智能算法将有助于建立一个数据库，其中包含用于预测模型的预处理数据。通过探索一种快速但精确的场地评估技术，可以保持可持续发展，同时允许对受污染地区进行有效的补救反应。通过对受污染的土壤样本进行具有成本效益的分析和改进环境修复，这将使合作伙伴公司在全球市场的竞争中具有优势。合作研究将使加拿大受益，提供经济、社会和环境效益，并促进对HQP进行各种专业和技术技能的培训。

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.