AIMC Topic: Floods

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

Water research
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...

A physically informed domain-independent data-driven inundation forecast model.

Water research
Inundation maps with spatial and temporal distribution of the water depths are essential for protecting the population in case of pluvial flood events. Creating these maps in operational forecasting is currently not possible with traditional physical...

Investigating the use of physics informed neural networks for dam-break scenarios.

PloS one
The real-time forecasting of flood dynamics is a long-standing challenge traditionally addressed through numerical solutions of the Shallow Water Equations (SWEs). Numerical solutions of realistic flow problems using numerical schemes are often hinde...

Quantifying urban land cover imperviousness as input for flood simulation using machine learning: South African case study.

Water science and technology : a journal of the International Association on Water Pollution Research
The imperviousness of urban surfaces is an important parameter in simulating urban hydrological responses, but quantifying imperviousness can be challenging and time-consuming. In response, this study presents a new framework to efficiently estimate ...

Improved integrated framework for flooded crop damage and recovery assessment: A multi-source earth observation and participatory mapping in Hadejia, Nigeria.

Journal of environmental management
Flooding has increasingly significant adverse effects on global food security, and there is a lack of a framework to effectively integrate remote sensing with survey data for accurate damage and recovery assessment. Also, optical satellite images for...

Efficient urban flood control and drainage management framework based on digital twin technology and optimization scheduling algorithm.

Water research
Urban flood control and drainage systems often face significant challenges in coordinating municipal drainage with river-lake flood prevention during flood seasons. Rising river levels can create backwater effects, which substantially increase urban ...

Confidence interval forecasting model of small watershed flood based on compound recurrent neural networks and Bayesian.

PloS one
Flood forecasting exhibits rapid fluctuations, water level forecasting shows great uncertainty and inaccuracy in small watersheds, and the reliability and accuracy performance of traditional probability forecasting is often unbalanced. This study com...

Enhancing the resilience of urban drainage system using deep reinforcement learning.

Water research
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...

Advancing flood risk assessment: Multitemporal SAR-based flood inventory generation using transfer learning and hybrid fuzzy-AHP-machine learning for flood susceptibility mapping in the Mahananda River Basin.

Journal of environmental management
The Mahananda River basin, located in Eastern India, faces escalating flood risks due to its complex hydrology and geomorphology, threatening socioeconomic and environmental stability. This study presents a novel approach to flood susceptibility (FS)...

Flood resilience through hybrid deep learning: Advanced forecasting for Taipei's urban drainage system.

Journal of environmental management
The escalating impacts of climate change have intensified extreme rainfall events, placing urban drainage systems under unprecedented pressure and increasing flood risks. Addressing these challenges requires advanced flood mitigation strategies, opti...