Numerical modelling and quantification of coastal urban compound flooding

Low-lying coastal cities are vulnerable to compound floods caused by many factors including river flows, tides and local rainfall. Many previous studies focus on the impacts of rainfall and tidal levels (two driving factors) on estuaries or regions near the main single river, while research about the three influencing factors on the floods...

Toward Improved Real-Time Rainfall Intensity Estimation Using Video Surveillance Cameras

Under global climate change, urban flooding occurs frequently, leading to huge economic losses and human casualties. Extreme rainfall is one of the direct and key causes of urban flooding, and accurate rainfall estimates at high spatiotemporal resolution are of great significance for real-time urban flood forecasting. Using existing rainfall...

Event-based decision support algorithm for real-time flood forecasting in urban drainage systems using machine learning modelling

Urban flooding is a major problem for cities around the world, with significant socio-economic consequences. Conventional real-time flood forecasting models rely on continuous time-series data and often have limited accuracy, especially for longer lead times than 2 hrs. This study proposes a novel event-based decision support algorithm for...

Enhancing urban flood forecasting in drainage systems using dynamic ensemble-based data mining

This study presents a novel approach for urban flood forecasting in drainage systems using a dynamic ensemble-based data mining model which has yet to be utilised properly in this context. The proposed method incorporates an event identification technique and rainfall feature extraction to develop weak learner data mining models. These models...

Estimating Rainfall Intensity Using an Image-Based Deep Learning Model

Urban flooding is a major issue worldwide, causing huge economic losses and serious threats to public safety. One promising way to mitigate its impacts is to develop a real-time flood risk management system; however, building such a system is often challenging due to the lack of high spatiotemporal rainfall data. While some approaches (i.e.,...

A multi-criteria risk-based approach for optimal planning of SuDS solutions in urban flood management

This paper presents a multi-criteria risk-based approach for managing urban flood hazards by using a combination of conventional measures and contemporary Sustainable Drainage Systems (SuDS). A multi-objective optimisation model coupled with a simulation model of UDS in the SWMM software is developed with the three objectives of minimising...

Exploring the Performance of Ensemble Smoothers to Calibrate Urban Drainage Models

Urban drainage models (UDMs) are often used to manage urban flooding. However, these models generally involve many parameters to represent the underlying complex hydrodynamic processes. This results in significant challenges to achieving effective and robust model calibration especially with frequently limited observations, leading to...

Novel Bayesian Additive Regression Tree Methodology for Flood Susceptibility Modeling

Identifying areas prone to flooding is a key step in flood risk management. The purpose of this study is to develop and present a novel flood susceptibility model based on Bayesian Additive Regression Tree (BART) methodology. The predictive performance of the new model is assessed via comparison with the Naïve Bayes (NB) and Random Forest (RF...

Community Mapping Supports Comprehensive Urban Flood Modeling for Flood Risk Management in a Data-Scarce Environment

In this paper we demonstrate a framework for urban flood modeling with community mapped data, particularly suited for flood risk management in data-scarce environments. The framework comprises three principal stages: data acquisition with survey design and quality assurance, model development and model implementation for flood prediction. We...

Exploring trade-offs among the multiple benefits of green-blue-grey infrastructure for urban flood mitigation

Climate change is presenting one of the main challenges to our planet. In parallel, all regions of the world are projected to urbanise further. Consequently, sustainable development challenges will be increasingly concentrated in cities. A resulting impact is the increment of expected urban flood risk in many areas around the globe....

Assessing the Co-Benefits of green-blue-grey infrastructure for sustainable urban flood risk management

Green-blue infrastructures in urban spaces offer several co-benefits besides flood risk reduction, such as water savings, energy savings due to less cooling usage, air quality improvement and carbon sequestration. Traditionally, these co-benefits were not included in decision making processes for flood risk management. In this work we present...

Performance of LEMMO with artificial neural networks for water systems optimisation

Optimisation algorithms could potentially provide extremely valuable guidance towards improved intervention strategies and/or designs for water systems. The application of these algorithms in this domain has historically been hindered by the extreme computational cost of performing hydraulic modelling of water systems. This is because running...