BRITE Pivot: Learning-based Optimal Control of Streamflow with Potentially Infeasible Time-bound Constraints for Flood Mitigation

BRITE Pivot：基于学习的水流优化控制，具有可能不可行的防洪时限约束

基本信息

批准号：
2226936
负责人：
Shaoping Xiao
金额：
$ 54.71万
依托单位：
University of Iowa
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-01-01 至 2025-12-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2226936&HistoricalAwards=false
关键词：
BRITE Pivot Learning based Optimal

项目摘要

This Boosting Research Ideas for Transformative and Equitable Advances in Engineering (BRITE) Pivot award will fund research that enables the intelligent deployment of optimal strategies for mitigating the damaging effects of rain-induced flooding, thereby promoting the progress of science and advancing the national prosperity, welfare, and health. Climate change is causing more frequent extreme weather events, like heavy rains, with disastrous consequences to infrastructure, public health, and national security. As the population grows and new urban centers develop, flood mitigation becomes a complex task that requires high-level coordination, is time critical, occurs in the presence of uncertainty and lack of full observability, and may be only partially feasible due to infrastructure constraints. This project will build a control framework powered by artificial intelligence to operate reservoirs in an optimal way for regulating streamflow while accounting for incomplete data acquisition, unpredictable effects of extreme weather, and ethical decision-making. The results from this research will benefit the scientific communities of hydrologic systems, control, and robotics, with applications also to intelligent systems with machine ethics. In addition, this project will provide undergraduate research opportunities and outreach activities, including educational materials for K-6 students to learn how climate change affects people’s lives, with emphasis on enhancing diversity, equity, and inclusion.This research aims to make fundamental contributions to methods for combining physics-informed and recurrent neural networks to predict the evolution of dynamic systems while also quantifying the effects of uncertainty, as well as for constructing learning-based control synthesis algorithms for complex high-level tasks that are temporally constrained and potentially infeasible in a partially observable environment. Data collected from US Geological Survey stations will be used to parameterize hillslope-link hydrologic models for streamflow forecasts. Small model simulations will then be combined with machine learning techniques to forecast streamflows with uncertainty quantification. Next, a formal description of the flood mitigation task that also accounts for lack of observability will be used to characterize the cost of violating temporal and economic constraints and ethical preferences. Finally, reinforcement learning techniques will be used to train a control agent to intelligently accomplish infeasible tasks to the greatest possible degree. A case study of the flood of 2008 of the Iowa-Cedar Watershed will be used to demonstrate the model development and control framework.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项促进工程变革和公平进步的研究理念 (BRITE) 枢轴奖将资助能够智能部署最佳策略的研究，以减轻降雨引起的洪水的破坏性影响，从而促进科学进步和促进国家繁荣，气候变化导致暴雨等极端天气事件更加频繁，给基础设施、公共卫生和国家安全带来灾难性后果。随着人口的增长和新城市中心的发展，防洪成为一项复杂的任务。高层协调时间紧迫，在存在不确定性和缺乏全面可观测性的情况下发生，并且由于基础设施的限制，可能仅部分可行。该项目将建立一个由人工智能驱动的控制框架，以最佳方式运行水库。用于调节水流，同时考虑不完整的数据采集、极端天气的不可预测影响以及道德决策。这项研究的结果将有利于水文系统、控制和机器人技术的科学界，也可应用于具有机器道德的智能系统。此外，该项目还将提供本科生研究机会和外展活动，包括为 K-6 学生提供教育材料，以了解气候变化如何影响人们的生活，重点是增强多样性、公平性和包容性。这项研究旨在为将物理知识和循环神经网络相结合的方法做出根本性贡献预测动态系统的演化，同时量化不确定性的影响，以及为复杂的高级任务构建基于学习的控制合成算法，这些任务在从美国地质调查局收集的数据中受到时间限制且可能不可行。车站将是然后，将用于对山坡连接水文模型进行参数化以进行水流预测，然后将小型模型模拟与机器学习技术相结合，以不确定性量化来预测水流。接下来，将使用洪水缓解任务的正式描述，该描述也考虑了可观测性的缺乏。最后，强化学习技术将用于训练控制代理以最大程度地智能地完成不可行的任务。爱荷华州雪松流域将用于演示模型开发和控制框架。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。