Hydrograph peak-shaving using a graph-theoretic algorithm for placement of hydraulic control structures

Abstract

The need to attenuate hydrograph peaks is central to the design of stormwater and flood control systems. However, few guidelines exist for siting hydraulic control structures such that system-scale benefits are maximized. This study presents a graph-theoretic algorithm for stabilizing the hydrologic response of watersheds by placing controllers at strategic locations in the drainage network. This algorithm identifies subcatchments that dominate the peak of the hydrograph, and then finds the "cuts" in the drainage network that maximally isolate these subcatchments, thereby flattening the hydrologic response. Evaluating the performance of the algorithm through an ensemble of hydrodynamic simulations, we find that our controller placement algorithm produces consistently flatter discharges than randomized controller configurations-both in terms of the peak discharge and the overall variance of the hydrograph. By attenuating flashy flows, our algorithm provides a powerful methodology for mitigating flash floods, reducing erosion, and protecting aquatic ecosystems. More broadly, we show that controller placement exerts an important influence on the hydrologic response and demonstrate that analysis of drainage network structure can inform more effective stormwater control policies.

Exploring Cost-effective Implementation of Real-time Control to Enhance Flooding Resilience against Future Rainfall and Land Cover Changes

“…One solution to reduce the subjectivity is to consider all criteria equally important if the suitable rationale to define the weights of criteria is missing (Wang et al, 2017). Beyond this multicriteria decision-making approach, various theoretical optimization approaches, such as the exhaustive performance-ranking method (Wong and Kerkez, 2018), evolutionary algorithm (Zamani Sabzi et al, 2016), multiple-objective genetic algorithm (Eulogi et al, 2021), and graph theory (Bartos and Kerkez, 2019), are useful methods to support the placement of control points. Future work should explore a more robust optimization solution for sitting control structures.…”

Observability‐Based Sensor Placement Improves Contaminant Tracing in River Networks

“…Future research should also investigate simplified sensor placement algorithms that do not require computing the full Observability Gramian of the system. Sensor placement algorithms based purely on network topology for instance (Bartos & Kerkez, 2019), could provide a scalable alternative to our theoretically motivated approach.…”

A data-driven improved fuzzy logic control optimization-simulation tool for reducing flooding volume at downstream urban drainage systems

“…RTC adaptability to watershed alterations such as land-use land-cover (LULC) change and rainfall pattern variation might not be fully exploited due to the unpredicted hydraulic stress, exceptional flood loading, heavy computational expense, and low operating efficiency (Bilodeau et al, 2018). So far, the limitation regarding RTC settings has motivated researchers to develop controller setting optimization algorithms, in order to make most of RTC effectiveness and efficiency in mitigating urban flooding (Bartos et al, 2018;Bartos and Kerkez, 2019;Duchesne et al, 2001;Muschalla et al, 2014).…”