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

这个促进工程变革和公平进步的研究思路（BRITE）枢轴奖将资助研究，使智能部署最佳策略，以减轻降雨引发的洪水的破坏性影响，从而促进科学进步，促进国家繁荣，福利和健康。气候变化导致暴雨等极端天气事件更加频繁，对基础设施、公共卫生和国家安全造成灾难性后果。随着人口的增长和新的城市中心的发展，防洪减灾成为一项复杂的任务，需要高层次的协调，是时间紧迫的，在存在不确定性和缺乏充分的可观测性，并可能只是部分可行的基础设施的限制。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，以调节径流，同时考虑不完整的数据采集，极端天气的不可预测影响和道德决策。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，并应用于具有机器伦理的智能系统。此外，该项目将提供本科生研究机会和外联活动，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，重点是加强多样性，公平，和包容性。这项研究旨在为结合物理学的方法做出根本性贡献-信息和循环神经网络来预测动态系统的演变，同时也量化不确定性的影响，以及用于为在部分可观察环境中时间受限且潜在不可行的复杂高级任务构建基于学习的控制合成算法。从美国地质调查站收集的数据将用于参数化山坡连接水文模型，用于径流预测。然后将小型模型模拟与机器学习技术相结合，以预测具有不确定性量化的流量。接下来，防洪减灾任务，也占缺乏可观察性的正式描述将被用来描述违反时间和经济约束和道德偏好的成本。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。2008年爱荷华州雪松流域洪水的案例研究将被用来展示模型开发和控制框架。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。

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.