Deep Learning for Enhanced Multisensor Quantitative Precipitation Estimation

用于增强型多传感器定量降水估算的深度学习

• 批准号: RGPIN-2018-05769
• 负责人: Coulibaly, Paulin
• 金额: $ 3.13万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=688608
• 关键词: Deep; Learning; Enhanced; Multisensor; Quantitative

Accurate and reliable quantitative precipitation estimations (QPE) are of particular importance for diverse hydrologic applications, sustainable water resources planning and management, forest fire risk rating, and drought risk management in agriculture. Specifically, high-resolution QPE are essential for hydrologic modelling and forecasting, optimal reservoir operation and scheduling, improved forecasts of landslides, extreme events (floods, droughts), including seasonal outlooks for domestic and industrial water supply. However, existing QPE are limited in accuracy, reliability, and in their spatial and temporal resolution. The proposed research seeks to address these limitations by resorting to recent advances in artificial intelligence, land surface monitoring technology (satellites, radars, weather stations), and in meteorology (improved numerical weather predictions).******The main objective of this research program is to develop an innovative quantitative precipitation estimation system to enhance the accuracy and reliability of estimated precipitation. The specific research objectives include : (1) investigating emerging machine learning techniques (namely deep learning algorithms) for developing QPE models; (2) developing new methods for merging QPE from multiple models, (3) developing and assessing the novel multi-model QPE system across Canada. For the first research objective, adequate deep learning methods will be identified and used to develop effective QPE models. To achieve the second objective, a copula-Bayesian averaging approach will be extended based on entropy theory, to develop a more robust model fusion technique. Finally, the latter will be used with the deep learning based models to develop the multi-model QPE system.***Given the importance of QPE in hydrology and the water sector at large, and the diverse societal needs of QPE, the proposed QPE system will be highly beneficial to a wide range of users across Canada and beyond. Furthermore, the research program will train a new generation of highly skilled personnel who will be well equipped to exploit recent advances in machine learning technology to take full advantage of the large datasets been made available by modern observing technology.

准确可靠的定量降水估算（QPE）对于多种水文应用、可持续水资源规划和管理、森林火灾风险评级和农业干旱风险管理尤为重要。具体来说，高分辨率QPE对于水文建模和预测、水库优化运行和调度、改进滑坡、极端事件（洪水、干旱）预测、包括家庭和工业供水的季节性前景至关重要。然而，现有的QPE在精度、可靠性和时空分辨率方面存在一定的局限性。拟议的研究旨在通过诉诸人工智能、陆地表面监测技术（卫星、雷达、气象站）和气象学（改进的数值天气预报）的最新进展来解决这些限制。******本研究计划的主要目标是开发一种创新的定量降水估计系统，以提高估计降水的准确性和可靠性。具体研究目标包括：(1)研究用于开发QPE模型的新兴机器学习技术（即深度学习算法）；(2)开发从多个模型合并QPE的新方法；(3)在加拿大开发和评估新的多模型QPE系统。对于第一个研究目标，将确定并使用适当的深度学习方法来开发有效的QPE模型。为了实现第二个目标，将在熵理论的基础上扩展copula-Bayesian平均方法，以开发更鲁棒的模型融合技术。最后，将后者与基于深度学习的模型相结合，开发多模型QPE系统。***考虑到QPE在水文学和整个水部门的重要性，以及QPE的各种社会需求，拟议的QPE系统将对加拿大和其他地区的广泛用户非常有益。此外，该研究计划将培养新一代高技能人才，他们将有能力利用机器学习技术的最新进展，充分利用现代观测技术提供的大型数据集。