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

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3545
Author(s):  
Soon-Ho Kwon ◽  
Joong-Hoon Kim
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Urban Drainage ◽  
Complex Data ◽  
New Paradigm ◽  
Neural Network Models ◽  
Drainage Systems ◽  
Time Operation ◽  
Urban Drainage Systems ◽  
Scientific Engineering

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.

scholarly journals Sustainable Urban Drainage Systems in Spain: A Diagnosis

2021 ◽  
Vol 13 (5) ◽  
pp. 2791
Author(s):  
Ignacio Andrés-Doménech ◽  
Jose Anta ◽  
Sara Perales-Momparler ◽  
Jorge Rodriguez-Hernandez
Keyword(s):  
National Standards ◽  
Three Dimensions ◽  
Urban Drainage ◽  
New Paradigm ◽  
National Scale ◽  
Drainage Systems ◽  
Global Context ◽  
Technical Performance ◽  
Urban Drainage Systems ◽  
The Right

Sustainable urban drainage systems (SUDS) were almost unknown in Spain two decades ago; today, urban drainage in the country is transitioning towards a more sustainable and regenerative management in a global context where green policies are gaining prominence. This research establishes a diagnosis of SUDS in Spain and examines the extent to which the country is moving towards the new paradigm in three dimensions: (a) the governance and social perception of the community, (b) the regulative background, and (c) the implementation and the technical performance of SUDS. The diagnosis identifies barriers that hinder the change. Then, we define the challenges that Spain has to face to overcome obstacles that delay the transition. Barriers to the governance sphere are related to the lack of involvement, knowledge, and organisational responsibilities. Within the regulative framework, the absence of national standards hinders the general implementation at the national scale, although few regional and local authorities are taking steps in the right direction with their own regulations. From the technical perspective, SUDS performance within the Spanish context was determined, although some shortcomings are still to be investigated. Despite the slowdown caused by the hard recession periods and the more recent political instability, SUDS implementation in Spain is today a fact, and the country is close to reaching the stabilisation stage.

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

2021 ◽  
Vol 25 (11) ◽  
pp. 5839-5858
Author(s):  
Yang Yang ◽  
Ting Fong May Chui
Keyword(s):  
Machine Learning ◽  
Management Practices ◽  
Hydrological Processes ◽  
Urban Drainage ◽  
Observation Data ◽  
Physical Processes ◽  
Large Uncertainty ◽  
Drainage Systems ◽  
Hydrological Responses ◽  
Urban Drainage Systems

Abstract. 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.

Assessment of urban flooding by dual drainage simulation model RisUrSim

2005 ◽  
Vol 52 (5) ◽  
pp. 257-264 ◽  
Author(s):  
T.G. Schmitt ◽  
M. Thomas ◽  
N. Ettrich
Keyword(s):  
Simulation Model ◽  
Surface Flow ◽  
Water Levels ◽  
Model Verification ◽  
Urban Drainage ◽  
Urban Flooding ◽  
Insurance Company ◽  
Drainage Systems ◽  
Engineering Research ◽  
Urban Drainage Systems

The European research project in the EUREKA framework, RisUrSim is presented with its overall objective to develop an integrated planning tool to allow cost effective management for urban drainage systems. The project consortium consisted of industrial mathematics and water engineering research institutes, municipal drainage works as well as an insurance company. The paper relates to the regulatory background of European Standard EN 752 and the need of a more detailed methodology to simulate urban flooding. The analysis of urban flooding caused by surcharged sewers in urban drainage systems leads to the necessity of a dual drainage modeling. A detailed dual drainage simulation model is described based upon hydraulic flow routing procedures for surface flow and pipe flow. Special consideration is given to the interaction between surface and sewer flow during surcharge conditions in order to most accurately compute water levels above ground as a basis for further assessments of possible damage costs. The model application is presented for a small case study in terms of data needs, model verification and first simulation results.

Long-term versus Real-time Optimal Operation for Gate Regulation during Flood in Urban Drainage Systems

2018 ◽  
Vol 15 (8) ◽  
pp. 750-759 ◽  
Author(s):  
Fatemeh Jafari ◽  
S. Jamshid Mousavi ◽  
Jafar Yazdi ◽  
Joong Hoon Kim
Keyword(s):  
Real Time ◽  
Optimal Operation ◽  
Urban Drainage ◽  
Drainage Systems ◽  
Urban Drainage Systems ◽  
Time Optimal
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