PySWMM: The Python Interface to Stormwater Management Model (SWMM)

McDonnell, Bryant E.; Ratliff, Katherine; Tryby, Michael E.; Wu, Jennifer Jia Xin; Mullapudi, Abhiram

doi:10.21105/joss.02292

Cited by 95 publications

(39 citation statements)

References 8 publications

(8 reference statements)

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“…This interpreter is loosely based around the OpenAI Gym environment [30], a standardized framework for RL and environment interaction and RL is implemented with the keras-rl python package [31]. The python PySWMM wrapper for SWMM allows for the SWMM model to be run incrementally, an essential requirement for RL [32].…”

Section: Deep Deterministic Policy Gradientmentioning

confidence: 99%

Deep Reinforcement Learning with Uncertain Data for Real-Time Stormwater System Control and Flood Mitigation

Saliba

Bowes

Adams

et al. 2020

Water

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Flooding in many areas is becoming more prevalent due to factors such as urbanization and climate change, requiring modernization of stormwater infrastructure. Retrofitting standard passive systems with controllable valves/pumps is promising, but requires real-time control (RTC). One method of automating RTC is reinforcement learning (RL), a general technique for sequential optimization and control in uncertain environments. The notion is that an RL algorithm can use inputs of real-time flood data and rainfall forecasts to learn a policy for controlling the stormwater infrastructure to minimize measures of flooding. In real-world conditions, rainfall forecasts and other state information are subject to noise and uncertainty. To account for these characteristics of the problem data, we implemented Deep Deterministic Policy Gradient (DDPG), an RL algorithm that is distinguished by its capability to handle noise in the input data. DDPG implementations were trained and tested against a passive flood control policy. Three primary cases were studied: (i) perfect data, (ii) imperfect rainfall forecasts, and (iii) imperfect water level and forecast data. Rainfall episodes (100) that caused flooding in the passive system were selected from 10 years of observations in Norfolk, Virginia, USA; 85 randomly selected episodes were used for training and the remaining 15 unseen episodes served as test cases. Compared to the passive system, all RL implementations reduced flooding volume by 70.5% on average, and performed within a range of 5%. This suggests that DDPG is robust to noisy input data, which is essential knowledge to advance the real-world applicability of RL for stormwater RTC.

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Section: Deep Deterministic Policy Gradientmentioning

confidence: 99%

Deep Reinforcement Learning with Uncertain Data for Real-Time Stormwater System Control and Flood Mitigation

Saliba

Bowes

Adams

et al. 2020

Water

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“…Finally, the uncertainty analysis was performed to evaluate the robustness of all the RL agents in flooding and overflow mitigation. All the systems were established using python 3.7, sklearn (https://scikit-learn.org/), tensorflow (https://www.tensorflow.org/), SWMM (Rossman, 2015), and pyswmm 0.6.0 (McDonnell et al., 2020). The route map of the methodology is presented in Figure 3.…”

Section: Methodsmentioning

confidence: 99%

Flooding and Overflow Mitigation Using Deep Reinforcement Learning Based on Koopman Operator of Urban Drainage Systems

Tian

Liao

Zhang

et al. 2022

Water Resources Research

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“…"Smartin" is capable of modeling and simulating IoT-based RWH systems in a coupled model of a UDN and water distribution network (WDN) in real-time. The two main components of "Smartin" are the following Python packages: (1) PySWMM [19] as a Phyton wrapper for the hydrodynamic Storm Water Management Model (SWMM5) [20] to simulate run-off processes in UDN; and (2) Python Epanet Toolkit provided from Open Water Analytics (https://github.com/OpenWaterAnalytics/epanet-python/tree/ dev/epanet_python/epanet_python, (accessed on 14 August 2019) for Epanet 2.2 [21] to analyze pressure and water quality in WDNs. Additionally, "Smartin" provides a very high spatial (property level) and temporal data resolution (seconds in UDN; hours in WDN) and supports individual control of each implemented RWH unit based on actual system states.…”

Section: Integrated Modeling Of Urban Water Infrastructurementioning

confidence: 99%

Revealing the Challenges of Smart Rainwater Harvesting for Integrated and Digital Resilience of Urban Water Infrastructure

Oberascher

Dastgir

et al. 2021

Water

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Smart rainwater harvesting (RWH) systems can automatically release stormwater prior to rainfall events to increase detention capacity on a household level. However, impacts and benefits of a widespread implementation of these systems are often unknown. This works aims to investigate the effect of a large-scale implementation of smart RWH systems on urban resilience by hypothetically retrofitting an Alpine municipality with smart rain barrels. Smart RWH systems represent dynamic systems, and therefore, the interaction between the coupled systems RWH units, an urban drainage network (UDN) and digital infrastructure is critical for evaluating resilience against system failures. In particular, digital parameters (e.g., accuracy of weather forecasts, or reliability of data communication) can differ from an ideal performance. Therefore, different digital parameters are varied to determine the range of uncertainties associated with smart RWH systems. As the results demonstrate, smart RWH systems can further increase integrated system resilience but require a coordinated integration into the overall system. Additionally, sufficient consideration of digital uncertainties is of great importance for smart water systems, as uncertainties can reduce/eliminate gained performance improvements. Moreover, a long-term simulation should be applied to investigate resilience with digital applications to reduce dependence on boundary conditions and rainfall patterns.

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PySWMM: The Python Interface to Stormwater Management Model (SWMM)

Cited by 95 publications

References 8 publications

Deep Reinforcement Learning with Uncertain Data for Real-Time Stormwater System Control and Flood Mitigation

Deep Reinforcement Learning with Uncertain Data for Real-Time Stormwater System Control and Flood Mitigation

Flooding and Overflow Mitigation Using Deep Reinforcement Learning Based on Koopman Operator of Urban Drainage Systems

Revealing the Challenges of Smart Rainwater Harvesting for Integrated and Digital Resilience of Urban Water Infrastructure

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