Local-scale flood hazard projections in historically vulnerable communities - Bista, Sunil; Bhattarai, Saurav; Sharma, Sanjib; Talchabhadel, Rocky (Modeling Earth Systems and Environment, 2026)
Keywords: Scopus_Alert_31_Octobre_2025

A physically informed domain-independent data-driven inundation forecast model - Schmid, Felix; Müller, Leonie; Leandro, Jorge (Water Research, 2026)
Keywords: Scopus_Alert_31_Octobre_2025

Urban Floodplain Development, Building Damage Exposure, and the Economic Feasibility of Flood Mitigation - Shultz, Steven (Natural Hazards Review, 2026)
Keywords: Scopus_Alert_31_Octobre_2025

Flood Forecasting of the Tajan Watershed Using the Output of the Numerical Weather Prediction Model (GFS) and the HEC-HMS Hydrological Model - Mehrpourbernety, Fatemeh; Fazloula, Ramin; Emadi, Alireza; Javaheri, Nasrollah; Gholami Sefidkouh, Mohammad Ali (Journal of Watershed Management Research, 2026)
Keywords: Scopus_Alert_26_Octobre_2025

Flood Impacts on Civil Infrastructure Systems: A Comprehensive Review of Impacts, Modeling Tools, Emerging Technologies, and Mitigation Strategies - Abegaz, Ruth; Xu, Jun; Wang, Fei (Natural Hazards Review, 2026)
Keywords: Scopus_Alert_26_Octobre_2025

Flood insurance purchasing behavior in two Gulf Coast cities - Cass, Evan; Shao, Wanyun; Hao, Feng (Cities, 2026)
Keywords: Scopus_Alert_17_Octobre_2025

Assessing subway station flood risks with a resilience Framework: Combining weighting methods and two-dimensional cloud model - Guo, De-Sai; Yang, Xin-Xin; Yang, Jin-Chen; Wu, Huai-Na; Meng, Fan-Yan; Chen, Ren-Peng (Tunnelling and Underground Space Technology, 2026)
Keywords: Scopus_Alert_17_Octobre_2025

GIS-driven hybrid multicriteria model for flood susceptibility assessment in a coastal metropolis of Ghana - Danso, Samuel Yaw (Geosystems and Geoenvironment, 2026)
Keywords: Scopus_Alert_17_Octobre_2025

Multi-model verification and evaluation of precipitation forecasts and Southwest China Vortex Rainstorms in Eastern Tibetan Plateau - Wu, Zhipeng; Zhang, Yan; Hu, Chunmei; Deng, Chengzhi; Luo, Juan; Wu, Shenggang; Zhou, Yingying; Zhang, Hong; Liao, Zhiyi; Wang, Huan; Pang, Yue (Atmospheric Research, 2026)
Keywords: Scopus_Alert_17_Octobre_2025

Serious Game Design for Resilience Enhancement of Transportation Infrastructure to Cyber Threats and/or Flood Hazards - Gordan, Meisam; Ko, Ili; Soroudi, Mona; McCrum, Daniel; König, Sandra; Schauer, Stefan; Connolly, Lorcan; Carroll, Páraic (Transport Transitions: Advancing Sustainable and Inclusive Mobility, 2026)
Abstract It is challenging to predict the precise location and severity of flood events, which can cause major disturbance to different public services, such as disruption to and the closure of critical transportation infrastructure. Therefore, approaches to enhance the resilience of these infrastructures are required. Serious games are new computing tools that have been applied in various fields with a combination of gameplay, learning and training. However, the application of serious games in intelligent transportation systems remains underutilized. As a result, the concept of a serious game is developed in this study to significantly improve transportation infrastructure resilience in an example of a flooding event. The serious game concept presented in this paper is being developed as part of the H2020-funded PRECINCT project ( www.precinct.info ).
Series Title: Lecture Notes in Mobility
Keywords: Scopus_Alert_17_Octobre_2025

Risks to the future health and productivity of tropical estuaries under climate change and increasing human development - Burford, M.A.; Brooks, A.; Cartwright, P.; Faggotter, S.J.; Irvine, D.J.; Waltham, N.J. (Marine Pollution Bulletin, 2026)
Keywords: Scopus_Alert_17_Octobre_2025

Evaluation of climate change impact on future flood in the Bagmati river basin, Nepal using CMIP6 climate projections and HEC-RAS modeling - Malla, Sushmita; Ohgushi, Koichiro (Water Cycle, 2026)
Keywords: Scopus_Alert_10_Octobre_2025

Integrating Hydrodynamic Mechanisms and Deep Learning for Real-Time Flood Inundation Forecasting - Fang, Xin; Li, Heng; Zhang, Sherong; Liu, Kang; Wang, Xiaohua; Wang, Chao (Journal of Computing in Civil Engineering, 2026)
Keywords: Scopus_Alert_10_Octobre_2025

Dynamic flood risk modeling in urban metro systems considering station configuration - Liang, Chen; Guan, Mingfu; Guo, Kaihua; Yu, Dapeng; Yin, Jie (Reliability Engineering & System Safety, 2026)
Keywords: Scopus_Alert_3_Octobre_2025

Real-time coastal flood risk assessment using IoT-integrated satellite data and machine learning models for predicting flooding events and informing resilient coastal planning for Durban coastal region, South Africa - Vadivel, M.; Vijaya Saraswathi, R.; Sree Lakshmi, P.; Merugu, R Rajaramesh; T., Subbulakshmi; Vivek, S. (Journal of African Earth Sciences, 2026)
Keywords: Scopus_Alert_3_Octobre_2025

Does flood risk matter in shaping climate gentrification? Evidence from Brisbane (2006–2021) - Gao, Xiaoyong; Yi, Xianjian; Kwan, Mei-Po; Li, Huan (Cities, 2026)
Keywords: Scopus_Alert_3_Octobre_2025

A data-driven and expert flood knowledge model based on the development of the Huber loss function for flood forecasting - Malik, Haider; Feng, Jun; Shao, Pingping; Abduljabbar, Zaid Ameen (Water Research, 2026)
Keywords: Scopus_Alert_3_Octobre_2025

JAX-WSPM: A GPU-accelerated parallel framework based on the JAX library for modeling water flow and solute transport in unsaturated porous media using an implicit finite element method - Toutlini, Nour-Eddine; Soulaïmani, Azzeddine; Beljadid, Abdelaziz (Computer Physics Communications, 2026)
Keywords: Infolettre_Octobre_2025

An integrated Bayesian hydrodynamic framework for quantifying rating curve uncertainty in design flood estimation and flood hazard mapping in a data-scarce urban area - Das, Tanmoy; Das, Subhasish (Water Research, 2026)
Keywords: Scopus_Alert_26_Septembre_2025

Short-Term Streamflow and Flood Forecasting Based on Graph Convolutional Recurrent Neural Network and Residual Error Learning - Pan, Xiyu; Mohammadi, Neda; Taylor, John E. (Journal of Computing in Civil Engineering, 2026)
Keywords: Scopus_Alert_26_Septembre_2025

Assessment of flood risk using integrated GIS and analytic hierarchy process in the Mekerra basin, Northwestern Algeria - Berrezel, Yacine Abdelbaset; Abdelbaki, Cherifa; Megnounif, Abdesselam; Saber, Mohamed; Benabdelkrim, Mohammed El Amin; Kumar, Navneet (Journal of African Earth Sciences, 2026)
Keywords: Scopus_Alert_19_Septembre_2025

Predictive Modeling and Strategic Planning for Urban Flood Risk Mitigation - Mitali, Patel; Patel, Nishit; Modi, Kshitish; Patel, Samir (Commun. Comput. Info. Sci., 2026)
Urban flooding threatens Indian cities and is made worse by rapid urbanization, climate change and poor infrastructure. Severe flooding occurred in cities such as Mumbai, Chennai and Ahmedabad. This has caused huge economic losses and displacement. This study addresses the limitations of traditional flood forecasting methods. It has to contend with the complex dynamics of urban flooding. We offer a deep learning approach which uses the network Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to improve flood risk prediction. Our CNN-LSTM model combines spatial data (water table, topography) and temporal data (historical model) to classify flood risk as low or high. This method includes collecting data pre-processing (MinMaxScaler, LabelEncoder) Modeling, Training and Evaluation. The results demonstrate the accuracy of flood risk predictions and provide insights into flexible strategies for urban flood management. This research highlights the role of data-driven approaches in improving urban planning to reduce flood risk in high-risk areas. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
Journal Abbreviation: Commun. Comput. Info. Sci.
Keywords: Scopus_Alert_5_Septembre_2025

Effects of long-term wetland variations on flood risk assessments in the Yangtze River Basin - Guo, Ziying; Shi, Xiaogang; Zhang, Dingfan; Zhao, Qunshan (Environmental Impact Assessment Review, 2026)
Flooding is the most frequent natural disaster in the Yangtze River Basin (YRB), causing significant socio-economic damages. In recent decades, abundant wetland resources in the YRB have experienced substantial changes and played a significant role in strengthening the hydrological resilience to flood risks. However, wetland-related approaches remain underdeveloped for mitigating flood risks in the YRB due to the lack of considering long-term wetland effects in the flood risk assessment. Therefore, this study develops an wetland-related GIS-based spatial multi-index flood risk assessment model by incorporating the effects of wetland variations, to investigate the long-term implications of wetland variations on flood risks, to identify dominant flood risk indicators under wetland effects, and to provide wetland-related flood risk management suggestions. These findings indicate that wetlands in the Taihu Lake Basin, Wanjiang Plain, Poyang Lake Basin, and Dongting and Honghu Lake Basin could enhance flood control capacity and reduce flood risks in most years between 1985 and 2021 except years with extreme flood disasters. Wetlands in the Sichuan Basin have aggravated but limited impacts on flood risks. Precipitation in the Taihu Lake Basin and Poyang Lake Basin, runoff and vegetation cover in the Wanjiang Plain, GDP in the Taihu Lake Basin, population density in the Taihu lake Basin, Dongting and Honghu Lake Basin, and the Sichuan Basin are dominant flood risk indicators under wetland effects. Reasonably managing wetlands, maximizing stormwater storage capacity, increasing vegetation coverage in urbanized and precipitated regions are feasible suggestions for developing wetland-related flood resilience strategies in the YRB. © 2025 The Authors
Publisher: Elsevier Inc.

Intrinsic drivers of urban flood disasters from the resilience perspective in China - Li, Xiaojuan; Chen, Rixin; Ren, Yifei; Jim, C.Y. (Reliability Engineering and System Safety, 2026)
Urban flood disasters pose substantial threats to public safety and urban development, with climate change exacerbating the intensity, frequency, and consequences of such events. While existing research has predominantly concentrated on flood control and disaster response, limited attention has been paid to the underlying drivers and evolutionary mechanisms of urban flood resilience. This study applies the resilience framework to develop an integrated methodology for assessing urban flood resilience. Focusing on three coastal provinces in China that frequently experience severe flooding, the study identifies fifteen key resilience drivers to construct a compound driver system. The evolution of flood resilience is examined through the lens of the Pressure-State-Response (PSR) model, which categorizes the drivers into three distinct dimensions. The Decision Making Trial and Evaluation Laboratory (DEMATEL) and Interpretative Structural Model (ISM) methods are employed to analyze the interrelationships and hierarchical structure among drivers. In parallel, a system dynamics (SD) modeling approach is used to construct causal-loop and stock-flow diagrams, revealing the complex interdependencies and critical pathways across resilience dimensions. The analysis identifies rainfall intensity as the most influential driver in shaping urban flood resilience. Scenario simulations based on the SD model explore variations in resilience performance under three developmental pathways. Findings suggest that enhancing response resilience is crucial under current flood control trajectories. This study contributes novel conceptual and methodological insights into the measurement and evolution of urban flood resilience. It offers actionable guidance for policymakers aiming to strengthen flood risk governance and urban safety. © 2025 Elsevier Ltd
Publisher: Elsevier Ltd

Enhancing flood prediction in the Lower Mekong River Basin by scale-independent interpretable deep learning model - Qiu, Yangzi; Shi, Xiaogang; He, Xiaogang (Environmental Impact Assessment Review, 2026)
Climate change has increased the frequency and intensity of extreme floods in the Lower Mekong River Basin (LMB). This study leverages the Long Short-Term Memory (LSTM) model to evaluate its performance in predicting river discharge across the LMB and to identify the key variables contributing to flood prediction through SHapley Additive exPlanation (SHAP) and Universal Multifractal (UM) analyses, in a scale-dependent and scale-independent manner, respectively. The performance of the LSTM model is satisfactory, with Nash–Sutcliffe Efficiency (NSE) values exceeding 0.9 for all subbasins when using all input features. The model tends to underestimate the largest peak flows in the midstream subbasins that experienced extreme rainfall events. According to SHAP, soil-related variables are important contributors to discharge prediction, with their impacts partially manifested through interactions with precipitation and runoff. Furthermore, the dominant contributing variables influencing flood prediction vary over time: soil-related variables and vegetation-related variables played a more significant role in earlier years, whereas hydrometeorological variables became more dominant after 2017. The UM analysis investigates the scaling behaviours of contributing variables, showing that hydrometeorological-related variables have a greater influence on predicting extreme discharge across the small temporal scales. Additionally, the UM analysis indicates that the model's performance improves as the temporal variability in extremes of the combined features decreases across 1 to 16 days. Overall, this study provides a comprehensive assessment of the LSTM model's performance in discharge prediction, emphasising the impact of the variability in the extremes of combined features through the scale-independent interpretation. These findings will offer valuable insights for stakeholders to improve flood risk management across the LMB. © 2025 The Authors
Publisher: Elsevier Inc.