Flood mitigation in coastal urban catchments using real-time stormwater infrastructure control and reinforcement learning
Flood mitigation in coastal urban catchments using real-time stormwater infrastructure control and reinforcement learning
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DOI：
10.2166/hydro.2020.080
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发表时间：
2020-10
影响因子：
2.7
通讯作者：
Benjamin D. Bowes;A. Tavakoli;Cheng Wang;Arsalan Heydarian;Madhur Behl;P. Beling;J. Goodall
中科院分区：
文献类型：
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作者：
Benjamin D. Bowes;A. Tavakoli;Cheng Wang;Arsalan Heydarian;Madhur Behl;P. Beling;J. Goodall
由于气候变化和海平面上升，沿海城市的洪水越来越多，这些社区所依赖的传统雨水系统面临压力。这些系统的自动实时控制（RTC）可以提高性能，为智能雨水系统制定控制策略是一个活跃的研究领域。本研究探索强化学习（RL）来创建控制策略以减轻洪水风险。RL是使用一个模型训练的假设城市集水区的潮汐边界和两个保留池与可控阀。RL的性能相比，被动系统，模型预测控制（MPC）策略，和基于规则的控制策略（RBC）。RL学习使用当前和预测条件主动管理池塘水位，并将被动系统的洪水减少了32%。与使用基于物理的模型和遗传算法的MPC方法相比，RL实现了几乎相同的洪水减少，仅比MPC少3%，运行时速度显著提高88倍。与RBC相比，RL能够快速学习类似的控制策略，并将洪水减少了19%。这项研究表明，RL可以有效地控制一个简单的系统，并提供了一个计算效率高的方法，可以扩展到更复杂的雨水系统的RTC。
Flooding in coastal cities is increasing due to climate change and sea-level rise, stressing the traditional stormwater systems these communities rely on. Automated real-time control (RTC) of these systems can improve performance, and creating control policies for smart stormwater systems is an active area of study. This research explores reinforcement learning (RL) to create control policies to mitigate flood risk. RL is trained using a model of hypothetical urban catchments with a tidal boundary and two retention ponds with controllable valves. RL's performance is compared to the passive system, a model predictive control (MPC) strategy, and a rule-based control strategy (RBC). RL learns to proactively manage pond levels using current and forecast conditions and reduced flooding by 32% over the passive system. Compared to the MPC approach using a physics-based model and genetic algorithm, RL achieved nearly the same flood reduction, just 3% less than MPC, with a significant 88× speedup in runtime. Compared to RBC, RL was able to quickly learn similar control strategies and reduced flooding by an additional 19%. This research demonstrates that RL can effectively control a simple system and offers a computationally efficient method that could scale to RTC of more complex stormwater systems.