Probabilistic Modelling of Sustainable Urban Drainage Systems

Sustainable Urban Drainage Systems (SuDS) gatherer effective strategies and control systems for stormwater management especially in highly urbanized areas characterized by large impervious surfaces that increase runoff peak flow and volume. The main goal is to restore the natural water balance by increasing infiltration, evapotranspiration and promoting rainwater reuse. This paper proposes an analytical probabilistic approach for the modelling SuDS applicable to different structures and goals. Developed equations allow to estimate the probability of overflow and the probability of pre-filling at the end of dry periods, to evaluate the efficiency of the storage in rainwater management and its ability to empty between consecutive events. A great advantage of the proposed method is that it allows to consider a chain of rainfall events; this aspect is particularly important for control systems SuDS characterized by low outflow rates which storage capacity is often not completely available at the end of a dry period because pre-filled by previous events. Suggested formulas were tested to two cases studies in Milan and Genoa, Italy.

Urban Flood Risk and Economic Viability Analyses of a Smart Sustainable Drainage System

Urban drainage systems are in transition from functioning simply as a transport system to becoming an important element of urban flood protection measures providing considerable influence on urban infrastructure sustainability. Rapid urbanization combined with the implications of climate change is one of the major emerging challenges. The increased concerns with water security and the ageing of existing drainage infrastructure are new challenges in improving urban water management. This study carried out in the Seixal area in Portugal examines flood risk analyses and mitigation techniques performed by computational modelling using MIKE SHE from the Danish Hydraulic Institute (DHI). Several scenarios were compared regarding flood risk and sustainable urban drainage systems (SuDS) efficiency. To obtain a more accurate analysis, the economic viability of each technique was analyzed as well through (i) life cost analysis and (ii) taking into account the damages caused by a certain type of flood. The results present that the best scenario is the one that will minimize the effects of great urbanization and consequently the flood risk, which combines two different measures: permeable pavement and detention basin. This alternative allows us to fully explore the mitigation capacity of each viable technique, demonstrating a very important improvement in the flood mitigation system in Seixal.

Machine Learning and Urban Drainage Systems: State-of-the-Art Review

In the last decade, machine learning (ML) technology has been transforming daily lives, industries, and various scientific/engineering disciplines. In particular, ML technology has resulted in significant progress in neural network models; these enable the automatic computation of problem-relevant features and rapid capture of highly complex data distributions. We believe that ML approaches can address several significant new and/or old challenges in urban drainage systems (UDSs). This review paper provides a state-of-the-art review of ML-based UDS modeling/application based on three categories: (1) operation (real-time operation control), (2) management (flood-inundation prediction) and (3) maintenance (pipe defect detection). The review reveals that ML is utilized extensively in UDSs to advance model performance and efficiency, extract complex data distribution patterns, and obtain scientific/engineering insights. Additionally, some potential issues and future directions are recommended for three research topics defined in this study to extend UDS modeling/applications based on ML technology. Furthermore, it is suggested that ML technology can promote developments in UDSs. The new paradigm of ML-based UDS modeling/applications summarized here is in its early stages and should be considered in future studies.

Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods

Sustainable urban drainage systems (SuDS) are decentralized stormwater management practices that mimic natural drainage processes. The hydrological processes of SuDS are often modeled using process-based models. However, it can require considerable effort to set up these models. This study thus proposes a machine learning (ML) method to directly learn the statistical correlations between the hydrological responses of SuDS and the forcing variables at sub-hourly timescales from observation data. The proposed methods are applied to two SuDS catchments with different sizes, SuDS practice types, and data availabilities in the USA for discharge prediction. The resulting models have high prediction accuracies (Nash–Sutcliffe efficiency, NSE, >0.70). ML explanation methods are then employed to derive the basis of each ML prediction, based on which the hydrological processes being modeled are then inferred. The physical realism of the inferred hydrological processes is then compared to that would be expected based on the domain-specific knowledge of the system being modeled. The inferred processes of some models, however, are found to be physically implausible. For instance, negative contributions of rainfall to runoff have been identified in some models. This study further empirically shows that an ML model's ability to provide accurate predictions can be uncorrelated with its ability to offer plausible explanations to the physical processes being modeled. Finally, this study provides a high-level overview of the practices of inferring physical processes from the ML modeling results and shows both conceptually and empirically that large uncertainty exists in every step of the inference processes. In summary, this study shows that ML methods are a useful tool for predicting the hydrological responses of SuDS catchments, and the hydrological processes inferred from modeling results should be interpreted cautiously due to the existence of large uncertainty in the inference processes.

Impacts of Climate Change on Urban Drainage Systems by Future Short-Duration Design Rainstorms

The adverse impacts of climate change and urbanization are converging to challenge the waterlogging control measures established in the Dong Hao Chong (DHC) Basin. Based on representative concentration pathway (RCP) scenarios, the future (2030–2050) waterlogging was assessed for the DHC basin and combined with future design rainfall. The delta change factors were projected using the regional climate model, RegCM4.6, and the annual maximum one-day rainstorm was modified to develop the annual maximum value method. By combining the delta change and annual maximum value methods, a future short-duration design rainstorm formula is developed in this study. The Chicago hyetograph shapes indicated that the peak rainfall intensity and amount both increase in the five return periods with two RCP scenarios. The InfoWorks ICM urban flood model is used to simulate the hydrological response. The results show that climate change will exacerbate urban waterlogging in DHC Basin. The maximum inundation volume and number of inundation nodes were expected to increase in the five return periods under the RCP4.5 and RCP8.5 scenarios, respectively. The submerged area is increasing due to climate change. This study highlights the link between climate change and urban drainage systems, and suggests that the effect of climate change in extreme rainfall should be considered in urban waterlogging management and drainage system design.