Development of Machine Learning Based Predictive Modeling for Forest Biomass Estimation**

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The objective of this research project is to develop efficient predictive modeling methods for forest biomass estimation applications as part of the industrial partner's (Hegyi Geomatics International Inc.) ongoing projects that deal with the quantification of forest biomass using satellite images and data analytics tools. Hegyi Geomatics develops decision support tools for natural resources management by integrating field observation, ecosystem models, Geographic Information System (GIS) and remote sensing. Toward this effort, Hegyi Geomatics is looking to develop a machine learning approach to analyze satellite images and estimate the forest biomass. Efficient predictive modeling approaches can be utilized to correlate band surface reflectance with forest biomass. To take full advantage of data-driven approaches, the specific problem investigated in this work is to develop a methodology to automatically select the most dominant inputs and to generate parsimonious predictive models using limited data sets for estimating forest biomass. The proposed predictive modeling methods will be developed utilizing advanced machine learning techniques by the research team from the University of Waterloo in close collaboration with the technical experts from the industrial partner. The benefits of the proposed predictive modeling methods include improved prediction accuracy, faster and more cost-effective predictive models, and better interpretations of constructed models. The proposed research effort to develop machine learning based predictive modeling methods for enabling effective and efficient data-driven modeling of complex systems also has significant implications for the GIS solutions and software that Hegyi Geomatics provides. Incorporation of the proposed predictive modeling methods into its data analytics and other decision support tools will help Hegyi Geomatics to further establish world-leading competitiveness of its services and products. The success of this project will enable the industrial partner to create new source of revenue generation and reach out to new clientele.