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

基于机器学习的森林生物量估算预测模型的开发**

• 批准号: 537549-2018
• 负责人: Heppler, Glenn
• 金额: $ 1.82万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=653464
• 关键词: Development; Machine; Learning; Based; Predictive

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.

该研究项目的目的是为森林生物量估算应用开发有效的预测建模方法，作为工业合作伙伴（Hegyi Geomatics International Inc.）的一部分，该项目正在进行的项目，这些项目使用卫星图像和数据分析工具来处理森林生物量的量化。 Hegyi Geomatics通过整合现场观察，生态系统模型，地理信息系统（GIS）和遥感来开发自然资源管理的决策支持工具。为了这一努力，Hegyi Geomatics寻求开发一种机器学习方法来分析卫星图像并估算森林生物量。有效的预测建模方法可用于将带表面反射率与森林生物量相关联。为了充分利用数据驱动的方法，这项工作中研究的具体问题是开发一种方法，以自动选择最主要的输入，并使用有限的数据集来生成简约的预测模型，以估计森林生物量。拟议的预测建模方法将利用滑铁卢大学的研究团队与工业合作伙伴的技术专家密切合作，利用高级机器学习技术开发。提出的预测建模方法的好处包括提高预测准确性，更快，更具成本效益的预测模型以及对构造模型的更好解释。提出的研究工作旨在开发基于机器学习的预测建模方法，以实现复杂系统的有效和有效的数据驱动的建模也对Hegyi Geomatics提供的GIS解决方案和软件也具有重要意义。将拟议的预测建模方法纳入其数据分析和其他决策支持工具将帮助Hegyi Geomatics进一步建立其服务和产品的世界领先竞争力。该项目的成功将使工业合作伙伴能够创建新的收入来源并与新客户联系。