AIMC Topic: Drainage, Sanitary

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A knowledge-data fusion framework accelerates deep reinforcement learning for real-time control of urban drainage systems.

Water research Nov 4, 2025
Deep reinforcement learning (DRL) has been applied to real-time control (RTC) of urban drainage systems (UDSs), with impressive performance and efficiency in reducing urban flooding and combined sewer overflows (CSO). However, for complex UDSs, learn...
Deep Learning Cities Floods Reinforcement Machine Learning Drainage, Sanitary
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Deep reinforcement learning control as an innovative approach for urban drainage systems: review and prospects.

Water research Jun 4, 2025
Urban drainage systems (UDSs) are vital for managing stormwater and wastewater but face growing challenges due to urbanization, climate change and aging infrastructure. Real-time control (RTC) enhances UDSs' performance and circumvents the need for s...
Wastewater Deep Learning Cities Waste Disposal, Fluid Water Quality Reinforcement Machine Learning Drainage, Sanitary
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Enhancing the resilience of urban drainage system using deep reinforcement learning.

Water research Apr 19, 2025
Real-time control (RTC) is an effective method used in urban drainage systems (UDS) for reducing flooding and combined sewer overflows. Recently, RTC based on Deep Reinforcement Learning (DRL) has been proven to have various advantages compared to tr...
Deep Learning China Cities Models, Theoretical Floods Reinforcement Machine Learning Rain Drainage, Sanitary
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Prediction of the roughness coefficient for drainage pipelines with sediments using GA-BPNN.

Water science and technology : a journal of the International Association on Water Pollution Research Aug 1, 2023
Accurate prediction of the roughness coefficient of sediment-containing drainage pipes can help engineers optimize urban drainage systems. In this paper, the variation of the roughness coefficient of circular drainage pipes containing different thick...
Neural Networks, Computer Drainage, Sanitary
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Decision tree (DT), generalized regression neural network (GR) and multivariate adaptive regression splines (MARS) models for sediment transport in sewer pipes.

Water science and technology : a journal of the International Association on Water Pollution Research Mar 1, 2019
Sediment deposition in sewers and urban drainage systems has great effect on the hydraulic capacity of the channel. In this respect, the self-cleansing concept has been widely used for sewers and urban drainage systems design. This study investigates...
Neural Networks, Computer Waste Disposal, Fluid Models, Statistical Decision Trees Water Pollution Multivariate Analysis Geologic Sediments Drainage, Sanitary
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Flood forecasting within urban drainage systems using NARX neural network.

Water science and technology : a journal of the International Association on Water Pollution Research Nov 1, 2017
Urbanization activity and climate change increase the runoff volumes, and consequently the surcharge of the urban drainage systems (UDS). In addition, age and structural failures of these utilities limit their capacities, and thus generate hydraulic ...
Neural Networks, Computer Cities Models, Theoretical Climate Change Floods Rain Urbanization Drainage, Sanitary
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Application of a soft computing technique in predicting the percentage of shear force carried by walls in a rectangular channel with non-homogeneous roughness.

Water science and technology : a journal of the International Association on Water Pollution Research Jan 1, 2016
Two new soft computing models, namely genetic programming (GP) and genetic artificial algorithm (GAA) neural network (a combination of modified genetic algorithm and artificial neural network methods) were developed in order to predict the percentage...
Neural Networks, Computer Algorithms Models, Theoretical Drainage, Sanitary
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