Examining ensemble machine-learning approaches to improve precipitation forecasting

检查集合机器学习方法以改进降水预报

• 批准号: 568786-2021
• 负责人: Ramanna, Sheela
• 金额: $ 2.18万
• 依托单位: University of Winnipeg
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=720651
• 关键词: Examining; ensemble; machine; learning; approaches

The water cycle on our planet is driven by complex physical processes, and difficult to be modelled accurately for precipitation prediction. The weather models used for this purpose have to make simplifying assumptions based on time and location considerations and thus, they cannot work universally. Typically, multiple models are utilized to mitigate the issue, but the best method to combine them is yet to be found. Machine learning techniques have proven useful to discover the underlying relations between complex functions, exceeding the abilities of traditional statistical methods, and in some cases even humans, provided that adequate data and computing resources are available. Both techniques will be experimented with and compared in-depth. The research team at UW brings twenty years of research and industry collaboration experience in machine learning which includes a recent machine learning research project with Weatherlogics. Our partner provides specialized products and services, by taking traditional weather information and transforming it into industry-specific data. Some key specialized datasets produced include road condition forecasts, hailstorm tracking, agriculture weather, and platforms for governments to manage winter road maintenance operations. These services allow companies and governments to make crucial weather-dependent decisions using the best available data. With ever-growing hardware capabilities and the amounts of data generated, we will examine two prominent machine learning approaches i) neural networks and ii) random forests to develop an ensemble weather forecast model for predicting precipitation. The research outcome of this project will lead to more accurate precipitation forecasts, which are used to assist the partner's client to improve decision-making. Example of improved decisions include better agricultural decisions, better predictions of snow or ice in road forecasts, and more timely alerts of heavy precipitation events. All of these use cases benefit Canadians, both from a public safety and economic point of view.

我们星球上的水循环是由复杂的物理过程驱动的，很难准确地建模以进行降水预测。用于此目的的天气模型必须基于时间和地点考虑做出简化的假设，因此它们不能普遍适用。通常，会利用多个模型来缓解该问题，但尚未找到将它们结合起来的最佳方法。事实证明，机器学习技术对于发现复杂函数之间的潜在关系非常有用，超出了传统统计方法的能力，在某些情况下甚至超出了人类的能力，只要有足够的数据和计算资源即可。这两种技术都将进行深入的实验和比较。华盛顿大学的研究团队在机器学习领域拥有二十年的研究和行业合作经验，其中包括最近与 Weatherlogics 合作的机器学习研究项目。我们的合作伙伴通过获取传统天气信息并将其转化为行业特定数据来提供专业产品和服务。生成的一些关键专业数据集包括路况预测、冰雹跟踪、农业天气以及政府管理冬季道路维护操作的平台。这些服务使公司和政府能够利用最佳可用数据做出与天气相关的重要决策。随着硬件能力和生成数据量的不断增长，我们将研究两种著名的机器学习方法：i) 神经网络和 ii) 随机森林，以开发用于预测降水的集合天气预报模型。该项目的研究成果将带来更准确的降水预报，用于协助合作伙伴的客户改进决策。改进决策的例子包括更好的农业决策、道路预报中更好的雪或冰预测以及更及时的强降水事件警报。从公共安全和经济的角度来看，所有这些用例都使加拿大人受益。