Urban Flood Hazard Research Articles

Urban Flood Hazard Assessment: Harnessing Ensemble Machine Learning for Next-Generation Risk Analytics

Urban Flood Hazard Assessment: Harnessing Ensemble Machine Learning for Next-Generation Risk Analytics

Urban subsidence zones prone to flooding: mitigated deformation trends post-2024 Guilin megaflood

Climate change intensifies urban flood hazards, yet existing research often overlooks the complex dynamic relationships between surface deformation, soil properties, and flooding. This study uses the 2024 Guilin flood event as a case study, integrating SBAS-InSAR, DInSAR techniques, and various machine learning methods to explore the complex interactions between surface deformation, soil characteristics, and flooding. The results show that the flood caused significant water expansion, with ground subsidence mainly concentrated in the southern and eastern parts of Guilin, highly coinciding with the severely flooded areas. The flood-inundated areas exhibited opposite deformation trends before and after the flood, shifting from subsidence to uplift, while road subsidence also showed a dynamic process. Different machine learning methods showed varying performance in predicting surface deformation, with the ERT model performing relatively well. Soil thickness was positively correlated with surface subsidence within a certain range, and this relationship exhibited noticeable nonlinear characteristics post-flood. The findings of this study have important practical implications for urban flood mitigation, aiding urban planners in more accurately identifying flood-prone areas, especially those experiencing subsidence.

Urban flood hazard assessment using FLA-optimized boost algorithms in Ankara, Türkiye

This study presents a comprehensive analysis of flood hazard mapping in Ankara, the capital of Türkiye, highlighting the critical vulnerability of this major urban center to climate-related disasters. By applying advanced boosting algorithms—specifically, XGBoost, GradientBoost, and CatBoost—along with hyperparameter optimization through the Fick’s law algorithm (FLA), this research introduces an innovative methodology aimed at improving the reliability and accuracy of flood hazard assessments in Ankara’s urban landscape. The analysis utilizes an extensive dataset that integrates topographic, meteorological, hydrological, and anthropogenic variables to provide critical insights into the dynamics of urban flooding with a focus on Ankara’s vulnerability. This approach is novel in that it incorporates FLA for hyperparameter optimization, marking a significant advancement in flood hazard modeling and achieving higher model accuracy and generalizability. Notably, among the various determinants of flood hazard identified, elevation emerges as the most influential factor affecting flood risk in Ankara. This finding underscores the complex relationship between urban geography and flood hazards, and highlights the need for targeted urban planning and infrastructure development strategies to effectively mitigate flood risk. The implications of this research extend beyond the local setting, contributing valuable insights to the global discourse on climate change adaptation and urban resilience. By combining cutting-edge machine learning techniques with in-depth geographic analysis, this study offers a scalable and innovative model for flood hazard assessment and management, providing a critical tool for cities around the world facing similar challenges.

Enhancing urban flood hazard assessment: a comparative analysis of frequency ratio and xgboost models for precision risk mapping

Enhancing urban flood hazard assessment: a comparative analysis of frequency ratio and xgboost models for precision risk mapping

Analysis of urban flood hazards and adaptation strategies in the Tamale Metropolis of Ghana

The study examines urban flood risk and adaptation strategies in the Tamale Metropolitan area of Ghana. Geospatial techniques were employed to map flood-prone communities, while questionnaires were used to gather data from residents on flood adaptation strategies. The Kendall coefficient of concordance was applied to rank the effectiveness of the various adaptation measures. The results revealed that communities with a topographic wetness index (TWI) between −2.0 and −7.7 were at higher risk of flooding. Factors contributing to flooding included the presence of Ferric Luvisols soil with high clay content, low-lying terrain, significant water flow accumulation, and rapid urbanization, which has increased impervious surfaces. Flood barriers and sandbags were ranked the most effective among the adaptation strategies, followed by early warning systems and community evacuation plans. The findings underscore the need for proactive measures to mitigate the growing vulnerability of residents to flooding. The study argues for incorporating flood risk assessments into zoning regulations to prevent development in high-risk areas and promote a flood-resilient city.

Urban Flood Hazard Assessment Based on Machine Learning Model

Urban Flood Hazard Assessment Based on Machine Learning Model

Impact assessment of green infrastructure and urban growth on stormwater runoff through geospatial modeling

Kochi city in southern India periodically experiences waterlogging or urban floods due to unabated urban growth and extreme rainfall events. This study aims to mitigate urban flood hazards through green infrastructure (GI) and its effective management. Assessment of storm water runoff (SWR) modeling is carried out in four scenarios, viz., baseline, past, severe, and green, using urban growth and GI driven simulations. Urban growth modeling and GI suitability analysis are carried out using Cellular Automata Markov (CA-Markov) and urban planning guidelines, respectively. The study provides insights into how GI influences SWR reduction and urban environment conservation, with 16% SWR reduction as compared to the baseline scenario and 18% when compared to the severe scenario.

Hybrid Spatial Modelling on Urban Flood Hazard Using Remote Sensing, GIS, and Analytic Hierarchy Process: A Study of Delhi NCR, India

Hybrid Spatial Modelling on Urban Flood Hazard Using Remote Sensing, GIS, and Analytic Hierarchy Process: A Study of Delhi NCR, India

Stormwater digital twin with online quality control detects urban flood hazards under uncertainty

Stormwater digital twin with online quality control detects urban flood hazards under uncertainty

Block-level spatial integration of population density, social vulnerability, and heavy precipitation reveals intensified urban flooding risk

Block-level spatial integration of population density, social vulnerability, and heavy precipitation reveals intensified urban flooding risk

Urban flood hazard insights from multiple perspectives based on internet of things sensor data

Urban flood hazard insights from multiple perspectives based on internet of things sensor data

Enhancing flood risk assessment in urban areas by integrating hydrodynamic models and machine learning techniques

Enhancing flood risk assessment in urban areas by integrating hydrodynamic models and machine learning techniques

How Plans Prepare for Future Uncertainty: Integrating Land Change Modeling and the Plan Integration for Resilience Scorecard™

This study integrates Land Change Modeling with the Plan Integration for Resilience Scorecard™ methodology to assess coastal communities’ preparedness for uncertain future urban growth and flood hazards. Findings indicate that, under static climate conditions, the network of plans in Tampa is well prepared across all urban growth scenarios, but less so in the face of a changing climate. Specifically, scenario outputs that consider climate change suggest the need for more resilient growth to reduce flood vulnerability compared with the current land use plan. Notably, some existing policies are likely to lead to counterproductive outcomes in a future with more extensive flooding.

A Systematic Literature Review on Classification Machine Learning for Urban Flood Hazard Mapping

A Systematic Literature Review on Classification Machine Learning for Urban Flood Hazard Mapping

Mitigating urban flood Hazards: Hybrid strategy of structural measures

Mitigating urban flood Hazards: Hybrid strategy of structural measures

Urban Flood Hazard Zonation in Bengaluru Urban District, India

Abstract Flooding in urban areas is increasingly becoming a global challenge, driven by extreme rainfall events and the vulnerability or resilience of affected regions. This urban flood disaster not only threatens societal security but also hampers economic development in cities. Satellite remote sensing technology has played a crucial role in all aspects of flood disaster management, including preparedness, prevention, and relief efforts. Space systems, with their advantageous perspective, have proven their ability to provide essential information and services for effective flood management. This study focuses on creating flood hazard maps for Bengaluru’s urban district using an Analytical Hierarchy Process (AHP)-based Multi-Criterion Decision Analysis (MCDA) and Geographic Information System (GIS) techniques. Factors such as rainfall, drainage networks, land use, groundwater levels, terrain elevation, slope, and soil type are considered. The AHP method assigns weights and ranks to each factor, and a weighted linear combination approach is used to merge basic maps into the final flood vulnerability map.

Impact of Soil Moisture Dynamics and Precipitation Pattern on UK Urban Pluvial Flood Hazards Under Climate Change

AbstractThe diversity of flood‐generating mechanisms superimposed on catchment physiographic features with non‐stationary meteorological drivers makes future flood hazard assessment a grand challenge. To date, many studies have examined patterns in rainfall and streamflow, but far fewer have investigated trends in the other drivers of flooding. The complex transfer function between precipitation and flooding makes it potentially misleading to simply look at the change in rainfall to express the hazard. Furthermore, there are very few studies that have directly used output from km‐scale climate models in flood modeling. Coarse resolution climate data sets may not credibly resolve local climate and weather extremes. Changes in rainfall distribution and antecedent moisture over extended time periods due to climate change have so far been ignored when assessing urban pluvial flood risk. In this paper, an urban flood hazard assessment framework using the latest 2.2 km resolution UK Climate Projections Local is proposed. Global warming induced changes in pluvial flood risks under RCP8.5 are projected, focusing on the impact of changing precipitation patterns and soil moisture dynamics on flood generation. Results indicate a strong increase in the frequency of occurrence of extreme floods, and the resultant future (2060–2080) annual flood volume is expected to increase up to 52.6% relative to 1980–2000 over a major UK urban region, and these patterns are likely to hold more generally elsewhere in the UK. Shifts to a later occurrence of extreme flooding is identified under global warming. Previous studies that have neglected soil moisture dynamics are unlikely to give accurate flood estimates.

Classification machine learning models for urban flood hazard mapping: case study of Zaio, NE Morocco

Classification machine learning models for urban flood hazard mapping: case study of Zaio, NE Morocco

The influence of microtopography to road inundation caused by extreme flood

The influence of microtopography to road inundation caused by extreme flood

Efficiency evaluation of low impact development practices on urban flood risk

Efficiency evaluation of low impact development practices on urban flood risk