Flood Prevention Research Articles

Analysis and risk prevention of flooding in high-risk gorges in the city of Arequipa, Peru

La ciudad de Arequipa, la segunda ciudad más importante del Perú, enfrenta eventos extremos, como lluvias de alta intensidad, pero de corta duración, que generan inundaciones y crecidas del río Chili (avenidas de lodo y huaycos). Esta situación agrava la vulnerabilidad de la población asentada en las márgenes de las quebradas, posteriormente torrenteras, debido a la poca o nula planificación territorial por parte de las entidades gubernamentales, que se refleja cada año en pérdidas humanas y de infraestructura. La frecuencia de estos eventos va en aumento con el tiempo y es por esta razón que se generan umbrales de precipitaciones extremas para su correspondiente caracterización, con un registro de 41 años (1981-2021), a fin de informar sobre la peligrosidad de un fenómeno meteorológico adverso, previsto o en desarrollo. Se utilizaron datos de precipitación máxima de 24 horas de las estaciones meteorológicas del Servicio Nacional de Meteorología e Hidrología del Perú (SENAMHI) para obtener el hidrograma líquido para diferentes periodos de retorno con el modelo hidrológico HEC-HMS. También se realizaron estudios de mecánica de suelos para determinar los parámetros reológicos del flujo no newtoniano y luego calibrar a través de eventos históricos con el modelo hidráulico HEC-RAS. Finalmente se elaboraron mapas cartográficos en QGIS para evaluar las zonas de peligro por inundaciones en las torrenteras Del Pato, San Lázaro, Venezuela y Los Incas.

Mitigating flood risk at Shah Alam, Malaysia for sustainable development

The natural environment and infrastructure are severely damaged by floods, which are becoming frequent occurrences. It may not be able to stop floods given the impending climate change and variability of the weather. Yet mapping flood risk can help with both mitigation and prevention of flooding. The Shah Alam experiences flash floods yearly, which is why it was chosen. An interactive approach to decision-making under multi-criteria decision analysis (MCDA), the analytical hierarchy process (AHP), is used. The flood risk zone is divided into five categories very low-risk (32.38%), low-risk (22.25%), moderate-risk (23.17%), high-risk (18.87%), and very high-risk (3.33%) respectively. Based on the data gathered for this study, the economic losses are identified, low-risk areas (RM 2091), moderate-risk areas (RM 2343), and high-risk areas (RM 2728). The historical flood events occur in the Seksyen 36, 18, 17, and most of the southern part of Shah Alam, Malaysia. Rainfall and the Klang River have the biggest effects on these regions, with water drainage systems being critical in the Shah Alam area to reduce the risk of floods. When analysing flood risks in the Shah Alam region and throughout the nation, important stakeholders both state and non-state actors can benefit greatly from the flood susceptibility map provided by this study.

Peri-Urban Floodscapes: Identifying and Analyzing Flood Risk Areas in North Bhubaneswar in Eastern India

Peri-urban catchment areas are increasingly susceptible to floods due to rapid land use transformations and unplanned urban expansion. This study comprehensively examines flood vulnerability in the rapidly developing peri-urban areas of North Bhubaneswar, focusing on significant changes in Land Use/Land Cover (LULC) and hydrological dynamics from 2004 to 2024, utilizing Geographic Information System (GIS) tools. The analysis reveals substantial shifts in land use patterns, with the urban footprint expanding by 71.8%, cropland decreasing by 21.7%, and forest areas by 13.6%. These changes have led to increased impervious surfaces, resulting in higher surface runoff and decreased groundwater recharge, thereby exacerbating flood risks in the region. The GRID-based vulnerability analysis classifies 90 villages within the catchment area based on their vulnerability levels, identifying 20 villages as high-risk areas requiring urgent attention, 44 villages as medium vulnerable, and 26 villages as low vulnerable. These classifications are based on factors such as proximity to drainage networks, slope, geomorphology, and LULC characteristics, with areas near drainage channels and low-lying regions being prone to flooding. The analysis integrates multiple factors to provide a comprehensive assessment of flood risk, highlighting the need for sustainable land use planning, conservation of vegetated areas, and the implementation of advanced flood prevention strategies in the peri-urban areas. Extending this research to other fringe regions could offer further valuable insights, guiding flood prevention and sustainable development strategies for areas undergoing significant land use transformations to effectively mitigate future flood risks.

Assessing Watershed Flood Resilience Based on a Grid-Scale System Performance Curve That Considers Double Thresholds

Enhancing flood resilience has become crucial for watershed flood prevention. However, current methods for quantifying resilience often exhibit coarse spatiotemporal granularity, leading to insufficient precision in watershed resilience assessments and hindering the accurate implementation of resilience enhancement measures. This study proposes a watershed flood resilience assessment method based on a system performance curve that considers thresholds of inundation depth and duration. A nested one- and two-dimensional coupled hydrodynamic model, spanning two spatial scales, was utilized to simulate flood processes in plain river network areas with detailed and complex hydraulic connections. The proposed framework was applied to the Hangjiahu area (Taihu Basin, China). The results indicated that the overall trend of resilience curves across different underlying surfaces initially decreased and then increase, with a significant decline observed within 20–50 h. The resilience of paddy fields and forests was the highest, while that of drylands and grasslands was the lowest, but the former had less recovery ability than the latter. The resilience of urban systems sharply declined within the first 40 h and showed no signs of recovery, with the curve remaining at a low level. In some regions, the flood tolerance depth and duration for all land use types exceeded the upper threshold. The resilience of the western part of the Hangjiahu area was higher than that of other regions, whereas the resilience of the southern region was lower compared to the northern region. The terrain and tolerance thresholds of inundation depth were the main factors affecting watershed flood resilience. The findings of this study provide a basis for a deeper understanding of the spatiotemporal evolution patterns of flood resilience and for precisely guiding the implementation and management of flood resilience enhancement projects in the watershed.

The Effect of Dam Break Speed on Flood Evolution in a Downstream Reservoir of a Cascade Reservoir System

The dam break flood is one of the potential causes of catastrophic events in cascade hydropower hub groups. Investigating the movement patterns of dam break flooding among reservoir groups under different dam break speeds is crucial for flood prevention and emergency response. In this study, the evolution characteristics of dam break floods were investigated in a cascading reservoir system, focusing on different break speeds of the upstream dam. The results indicate that the dam break speed determines the concavity or convexity of the water level curve changes in the upstream reservoir. Accordingly, dam breaks are classified into three modes: instant dam break, fast dam break, and slow dam break. An approximate critical speed has been identified to differentiate between the fast dam break and slow dam break. Further investigation into the evolution patterns of dam break floods in downstream reservoirs under different break modes was conducted. Correspondingly, the flood peak discharge and peak arrival time of the dam break floods vary differently with break speed under different break modes. Finally, a theoretical analysis for the flood peak discharge at the dam site during gradual dam break at a certain speed was established, which is able to predict the over-dam flood peak discharge in fast and slow dam break modes. This study is based on a combination of laboratory flume experiments and three-dimensional numerical simulations. This study has theoretical significance for the reinforcement of public infrastructure safety and the prevention of natural disasters.

Assessing critical flood-prone districts and optimal shelter zones in the Brahmaputra Valley: Strategies for effective flood risk management

Frequent flooding has become a persistent issue in floodplain regions, causing significant disasters during each rainy season due to insufficient disaster management planning. This study proposes a methodology to prioritize flood susceptibility areas at the district level and identify suitable sites for flood shelters using a combination of machine learning algorithms and multi-criteria analysis, supported by geospatial technology. Flood shelter suitability mapping was conducted using the Analytical Hierarchy Process (AHP), while flood susceptibility zones were assessed using four different machine learning models: Support Vector Machine (SVM), Random Forest, Decision Tree, and Naive Bayes. The integration of machine learning models with the AHP technique is vital in situations where conventional numerical models face challenges due to limited data, such as river discharge and water levels. The methodology includes a multicollinearity assessment to ensure the independence of selected flood-causing factors, information gain ratio to identify the most influential factors, Spearman's rho test to verify correlations between the machine learning models, and ROC-AUC along with statistical regression for validating the accuracy of the flood susceptibility maps. The findings indicate that the SVM algorithm, given its strong performance and effective training datasets, is recommended for areas with similar physical characteristics. The district-wise priority map generated from the weighted results of flood susceptibility assessments will be useful for flood management and mitigation strategies. Additionally, the study found that applying the AHP technique to determine flood shelter suitability, after assessing flood-prone areas, enhanced the efficiency of the flood management process. This research offers valuable insights for authorities to better address flooding and improve flood prevention and management efforts in floodplain regions, contributing to broader climate change adaptation strategies.

Tropical forest cover, oil palm plantations, and precipitation drive flooding events in Aceh, Indonesia, and hit the poorest people hardest.

Tropical forest loss and degradation in watersheds disrupt essential ecosystem services that regulate water flow, often causing devastating floods that impact agricultural productivity and impoverish downstream communities. Despite its importance, evaluations of the interconnectedness between the depletion of hydrological services and flooding lack an evidence-base in the Global South and, therefore, have little influence on policy dialogue. In this study, we focus on the forest-rich province of Aceh, Indonesia, using local and national newspaper articles to compile information on flood events between 2011 and 2018. We explored spatio-temporal flood patterns with a combination of climatic, topographic, and environmental factors. We compiled 2,029 reported flood events in mainland Aceh located in 20 of the 21 districts/cities, with a disproportionately high occurrence (71%) in four districts. The trend of flood events exhibited an increasing pattern between 2011 and 2018. Over this period, floods displaced ~158,000 people and damaged ~24,500 houses and ~11,500 ha of agricultural land. Our generalized linear mixed-effect model found that reported flood events were more likely to occur in areas with lower tree cover, more oil palm plantations, and higher precipitation. Areas with a lower human population density and higher poverty rates were found to be most susceptible to flooding events. Our findings highlight the critical link between forest preservation and flood prevention, and the irreplaceable role that forests play in ensuring the well-being of local communities, especially those affected by poverty. Our study underscores the importance of considering these interconnected factors in future land use and economic development plans and policies.

Exploring residents’ willingness-to-pay for benefits provided by Sustainable Drainage Systems

ABSTRACT Sustainable Drainage Systems (SuDS) have increasingly been used in the recent years since they help adapt cities to climate change and make them more sustainable and resilient. Since they are multifunctional, these stormwater control measures provide numerous benefits. However, the question of acceptability by inhabitants remains partially open. This article seeks to assess preferences of the inhabitants living nearby with regards to SuDS implemented on public land. It relies on a discrete choice experiments whose attributes are the different benefits provided by SuDS. The study was conducted in the Eurometropolis of Strasbourg (France). Results show a strong preference for a reduction of urban heat island effects as well as flood prevention and water protection. Based on a latent class model, we also highlight the heterogeneous nature of the preferences. These results are important for the acceptance of environmental programs and a better guarantee of desirable and effective results.

Flood prevention benefits provided by Canadian natural ecosystems

The escalating impacts of climate change have heightened concerns about the frequency and severity of natural disasters, particularly extreme flooding events. Future projections underscore the necessity for innovative flood prevention strategies, including broad-scale nature-based solutions. Here, we present the first comprehensive assessment of the flood prevention benefits provided by Canadian natural ecosystems and identify key areas crucial for human well-being. Using spatially explicit modeling, we (1) evaluated the potential runoff retention by natural ecosystems and (2) identified downstream urban and agricultural areas critically dependent on these natural benefits, particularly those in floodplains and close proximity to upstream natural ecosystems. The natural ecosystems within the top 5 % of sub-basins, representing regions with a high priority for conservation practices aimed at flood prevention, play a crucial role in safeguarding approximately 54 % (∼6,000 km2) of the total built-up area and 74 % (∼16,900 km2) of the total cropland situated within floodplains. Additionally, they are positioned upstream of floodplain-based urban zones belonging to 358 population centers, directly benefiting 3.7 million people (∼10 % of the Canadian population) and indirectly benefiting almost 20.1 million people (∼56 % of the Canadian population). Moreover, among Canada’s 5.2 million km2 of flood-preventing natural ecosystems, we identified a small fraction (10 %) whose loss or degradation would result in a significant (>50 %) increase in runoff. Several of these crucial ecosystems are situated in less populated northern regions, where local governments might want to incentivize conservation initiatives to support flood prevention. Our research underscores the imperative to integrate nature-based solutions into national strategies that consider the results of spatial planning analyses. Establishing other effective area-based conservation measures in the priority regions highlighted in this study can contribute towards reaching current ambitious environmental goals and provide critical flood prevention benefits. Additionally, our methods are transferable to other regions worldwide, leveraging globally available datasets and ensuring computational feasibility.

A novel framework for evidence-based assessment of flood resilience integrating multi-source evidence: A case study of the Yangtze River Economic Belt, China

Floods are extremely destructive to the society, economy and nature, and severely restrict sustainable urban development. There is an urgent need to assess the level of flood resilience, analyze its evolutionary characteristics and improve the flood resilience of the region. However, few previous studies have integrated multi-source evidence into a framework for assessing flood resilience over a longer time span. This study integrated multi-source evidence to analyze the Yangtze River Economic Belt, including literature-based evidence, policy texts and social media data. Subsequently, the Kruskal algorithm and Jaccard coefficient were used to construct collinear networks and identify the clustering dimensions of flood resilience. After evidence judgment, a flood resilience indicator system was constructed. The flood resilience index was calculated using the improved Critic method. The dynamic evolution characteristics and key influencing factors were illustrated by the Kernel density and Grey correlation methods, respectively. The principal contribution is to integrate multi-source evidence to assess flood resilience, with a newly constructed indicator system that systematically considers local public demand, policy priorities and common indicators from previous literature. The results show that: the flood resilience index of the Yangtze River Economic Belt is in the range of [0.16, 0.75] from 2000 to 2022, with higher values in the downstream compared to those in the midstream and upstream. The social dimension dominating the overall flood resilience. The kernel density curve of the YREB is characterized by a right trailing. The inter-regional differences tend to widen and have a polarization effect. The special funds for water conservancy finances and the length of flood prevention levees are key factors influencing the level of flood resilience. Finally, strategies for enhancing flood resilience were proposed. This study provides a feasible framework for constructing an indicator system and assessing flood resilience by integrating multi-source data. It is important for flood risk management and helps to the development of urban flood prevention and resilience strategies.

Study on flood level forecasting of tidal reach in Puyang River basin

Abstract The downstream section of the Puyang River is a tidal river reach subject to both upstream floods and downstream tides. This combined impact results in an unstable relationship between water level and discharge at the forecast station, making flood level forecasting challenging. This paper took the Linpu station on the Puyang River in Zhejiang Province as the research object. Based on the historical flood data, the cause of high water levels at Linpu station was analyzed. The flood levels at Linpu station were decomposed into flood increments and basic tidal levels. Using the multiple regression method, the prediction formula for upstream flood-induced water level increments was obtained. A flood level forecasting method based on the flood increment was developed. The result of the scheme accuracy evaluation indicates that this method has good forecasting performance. The flood level forecasting method for the tidal channel proposed in this paper has a simple structure and high accuracy, which provides a scientific basis for the prevention and control of flood and drought hazards and the decision-making of project scheduling in the tidal river.

Using a multiphysics coupling-oriented flood modelling approach to assess urban flooding under various regulation scenarios combined with rainstorms and tidal effects

Climate change and rapid urbanisation have intensified urban flooding. Flood models constructed using hydrological and hydrodynamic approaches are crucial tools for simulating and regulating urban flooding processes. Urban flooding arises from the interplay of various physical processes in urban water systems, influenced by heterogeneous urban surfaces, underground structures, and intricate river networks influencing surface flow, which must be considered in flood models. For coastal cities with intricate drainage systems, the disasters of combined rainstorms and tidal flooding can be mitigated through river scheduling and real-time control of hydraulic structures. However, a lack of research on the effectiveness of flood regulations limits the application of flood models. This study proposes a flood modelling approach oriented towards the coupling of multiphysics processes, achieving a comprehensive simulation and regulation of the flood process. A surface flood control model was created by coupling hydrological and hydrodynamic processes using the Storm Water Management Model and the orthogonal storage cell model, enabled by data exchange synchronisation technology. Subsequently, the model’s accuracy was validated using observed rainstorm events, identifying drainage system overflows and land surface inundation. Finally, the developed model was applied to analyse the enhancing effect of tides on rainstorm-induced flooding and the impact of various control scenarios. This study offers theoretical direction for large-scale urban flood modelling and model development, offering significant references for revisiting urban planning and formulating flood prevention strategies under the joint impact of rainstorms and tides.

A novel urban flood risk assessment framework based on refined numerical simulation technology

Due to urbanization and climate change, urban areas are becoming more susceptible to flooding, posing significant risks to human life and property. Numerical simulation techniques can provide valuable support in mitigating urban flood risks. The objective of this study is to establish a novel framework for flood risk assessment based on refined urban flood simulations. To achieve refined urban flood simulations, the Torricelli’s law is incorporated into a two-dimensional fluid dynamics model, and a numerical analysis model is developed to estimate the inundation within buildings and accurately simulate the entire inundation process. The model was used to reproduce a real flood event in Omihachiman City, Japan, with the Nash-Sutcliffe Efficiency (NSE) values of 0.6488 and 0.7182, demonstrating the credibility of the model. Based on the simulation results, the hydrological causes of the flood event were analyzed, and flood prevention measures were proposed. After implementing these measures, the maximum water depths at the study area were reduced to 0.14 m, 0.62 m, and 0.12 m, respectively, proving the effectiveness of the flood prevention measures. To address the issue of inundation depth not fully reflecting flood risk, the instability of human bodies in floodwaters is conducted to evaluate flood risk for adults and children in the target area. In response to increasingly frequent extreme rainfall events, the extreme precipitation events of three different return periods (10-year, 50-year, and 100-year) are used to assess the flood risk and the effectiveness of flood prevention measures. Based on the assessment results, the flood prevention measures not only reduced the flood risk levels but also decreased the flood risk area, further confirming their effectiveness.

Spatiotemporal evolution and influencing factors of flood resilience in Beibu Gulf Urban Agglomeration

In the context of global climate change and rapid urbanization, enhancing flood resilience is essential for mitigating urban flood risk. However, few studies have conducted long-term, cross-scale dynamic evaluations of flood resilience and analyzed its influencing factors and mechanisms. Taking the Beibu Gulf urban agglomeration as the study area, a long-term, cross-scale dynamic evaluation index system based on the “Robustness-Resistance-Recovery” (3Rs) framework were adopted to assess the spatiotemporal evolution of flood resilience from 2000 to 2020. Furthermore, the optimal parameters-based geographical detector model is employed to identify the key influencing factors and mechanisms. The results reveal that pre-flood robustness is lower in coastal areas and higher in inland regions. In the during-flood stage, cities with greater comprehensive power exhibit stronger resistance. Post-flood recovery is higher in city centers and marginal mountainous areas, while coastal and inland low-lying regions show lower recovery. The flood resilience of urban agglomerations has improved in recent years, largely due to the enhancement of urban flood control infrastructure and healthcare capacity. However, disparities between cities persist. From 2000 to 2020, economic factors have been the primary drivers of improved flood resilience, while ecological factors have gained increasing importance over the past decade. These findings provide valuable insights for flood prevention, mitigation, and resilience management in urban agglomerations. The proposed dynamic evaluation index system offers a reference framework for evaluating flood resilience in other regions.

Analyzing the spatial scale effects of urban elements on urban flooding based on multiscale geographically weighted regression

In the context of rapid urbanization and the frequent occurrence of extreme rainfall in cities, the risk of flooding in the future will further increase, and the problem of urban flooding cannot be ignored. Urban elements exhibit significant spatial heterogeneity, which largely determines the spatial distribution differences in urban flooding. Therefore, it is important to clarify the scale of influence of different urban elements and explore their scale effects on urban flooding to accurately assess the risk of urban flooding. Taking Zhengzhou City, China, as the study area, this study analyzed the urban elements associated with urban flooding, quantified the scale of the influence of urban elements on flooding using multiscale geographically weighted regression (MGWR), and further explored the spatial scale effects of urban elements on urban flooding. The results showed that MGWR can better fit the spatially non-uniform distribution of urban flooding and that the scale of the influence of urban elements on urban flooding can be reflected by the bandwidth of MGWR. The results of MGWR indicated that the bandwidths of elevation, number of drainage outfalls (NDO1Abbreviations: RRP, Rainfall return period; DDR, Distance to river; NDO, Number of drainage outfalls; ILMFCP, Input level of materials for flood control projects; PRL, Proportion of residential land; PCL, Proportion of commercial land; PIL, Proportion of industrial land; PPSL, Proportion of public service land; PRA, Proportion of road area; PGL, Proportion of green land; PD, Population density; MI, Manpower input; GDP, Gross domestic product; ILER, Investment level in education and research.1), distance to river (DR), Gross domestic product (GDP), proportion of residential land (PRL), and proportion of commercial land (PCL) were small, and their influence scales were localized. In contrast, the influence of rainfall return period (RRP), slope, proportion of industrial land (PIL), proportion of public service land (PPSL), proportion of road area (PRA), proportion of green land (PGL), input level of materials for flood control projects (ILMFCP), population density (PD), manpower input (MI), and investment level in education and research (ILER) were at global scales. The most influential factors for urban flooding were RRP, PD, and MI. Slope, DR, and PRA had less influence on urban flooding. This study helps improve the effectiveness of urban flood prevention and mitigation efforts.

Analysis of changes in river connectivity during the urbanization process: a case study of the southeastern plain of Yinzhou, China.

Affected by human activities, the naturally occurring river network in the southeastern plain of Yinzhou has gradually evolved into a natural-artificial composite water system, and changes in river connectivity due to changes in river network systems have caused water security problems, including urban flooding. To clarify the river connectivity change and its relationship with the urbanization process, this paper discusses an evaluation method for river connectivity based on complex networks and cellular automata (CA) from the perspective of complex systems, quantitatively analyzes the spatial-temporal characteristics of the structural and functional river connectivity in the study area during the 1990s-2020s, and reveals the impact of river nodes and chains on the connectivity level under the disturbance of natural or human factors. The results contained the following revelations: ① River connectivity showed a decreasing trend in the initial and rapid development stages of urbanization from the 1990s to the 2010s and a limited increasing trend in the optimization and upgrading stages from the 2010s to the 2020s. ② River network degradation and ongoing connectivity decline are found in the northeastern part of the study area. The highest river connectivity exists in Dongqianhukaifaqu. ③ The number of river nodes and chains should be maintained at approximately 80% for normal river connectivity. The nodes of high degree in the inflow area are listed in the key protection areas. ④ Changes in river connectivity are significantly correlated with the urbanization process. Changes in the functional connectivity level affect the magnitude of a flood. This study provides a theoretical basis for river network connectivity improvement and flood prevention in plain areas.

Adaptation and Adversity Acceptance as Resilience to Flooding at World Heritage Sites: A Case Study of Hoi An Ancient Town, Vietnam

ABSTRACT This article investigates long-term adaptation and resilience to floods at World Heritage sites through a case study of Hoi An Ancient Town, Quang Nam, Vietnam. Drawing on fieldwork observation, document review, and unstructured interviews conducted in Hoi An, we examine the enduring processes and experiences of co-living with floods. The article considers residents’ ability to comprehend, acknowledge, and address the challenges of flooding using the conceptual lens of adaptation and adversity acceptance as resilience. Our findings reveal an inseparable bond between residents and their built environment, encompassing invaluable lessons and experiences, local beliefs, resource availability, and adaptive strategies aimed at harmonious cohabitation with flooding described as ‘leaning into nature’. Strikingly, residents have developed their own practices and identities shaped by the town’s inundation by flood waters over a few centuries. Various modern flood prevention measures trace their origins to historical efforts, underscoring the vital role of traditional knowledge and practices in coping with the impacts of climate change. Those efforts are also framed within the local policy context such as Heritage Management Plan, Disaster Risk Management Plan, and Regulation for the Protection of the UNESCO-listed Hoi An Ancient Town.

The Impact of Urbanization on Surface Runoff and Flood Prevention Strategies: A Case Study of a Traditional Village

Increasingly severe flooding disasters have caused heavy casualties and property losses worldwide. Traditional Chinese villages that rarely experienced flooding disasters in the past have begun to frequently suffer from floods due to unreasonable reconstruction activities such as ground hardening and pond filling caused by urbanization. However, previous studies on hydrological changes and flood disasters caused by reconstruction activities in rural areas are scarce, especially lacking in quantitative analysis and research. In this study, the Storm Water Management Model (SWMM) is used to construct two hydrological models before and after the reconstruction of Hezhai Village, a traditional Chinese village. By simulating and comparing the changes in hydrological indicators of the two models, this study quantitatively analyzes how reconstruction activities caused changes in surface runoff and flooding disasters in Hezhai Village. The results show that the increase in the impervious ratio in the village has obvious effects on the total runoff, peak runoff, and runoff coefficient. And the reconstruction of ponds and canals has a notable impact on flooding. This study further delves into the logic of flooding at ponds and ordinary nodes and analyzes the specific reasons for flooding in Hezhai Village. Based on this, the paper provides recommendations for the optimization of the reconstruction of Hezhai Village.

The effect of goal frame and risk perception on digital flood prevention tool acceptability.

To cope with catastrophic floods, people need to be better prepared. In this context, a self-assessment digital tool for habitat vulnerability was developed. To improve its take-up rate, we are looking at the motivations associated with the social acceptability of this tool. The motivations (hedonic-gain-normative), derived from goal-frame theory, as well as elements relating to risk perception, are tested. One, 688 participants (aged between 18 and 87) first read a scenario presenting the application (reflecting either one of the motivations of the Goal Framing Theory or a control scenario with no motivation). After reading one of the scenarios, they completed an online questionnaire, measuring the acceptability of the tool using three measures: a direct one (items from the Technological Acceptability Model: ease of use, perceived usefulness and social influence), an indirect measure (by asking the percentage of neighbours interested in the tool) and a social measure (judgement of a person using the tool). The last part of the questionnaire was about subjects' risk perception. The analyses show that, of all the scenarios, the one involving hedonic motivation leads to the lowest social acceptance of the tool. We also observe that a better risk perception predicts better tool acceptability. Finally, we observe interaction effects between risk perception and motivations, showing that normative motivation is better when risk perception increases and that the control condition is better when risk perception decreases. Goal framing theory is usually used for ecological behaviors. It also appears here as relevant in the field of risk prevention. Although risk perception remains the best predictor of acceptability, these results lead us to conclude that hedonic motivation is not appropriate for the acceptability of a flood risk prevention tool. It is preferable to focus on normative and gain motivations.

Study on long short-term memory based on vector direction of flood process for flood forecasting

Accurate flood forecasting is crucial for flood prevention and mitigation, safeguarding the lives and properties of residents, as well as the rational use of water resources. The study proposes a model of long and short-term memory (LSTM) combined with the vector direction (VD) of the flood process. The Jingle and Lushi basins were selected as the research objects, and the model was trained and validated using 50 and 49 measured flood rainfall-runoff data in a 7:3 division ratio, respectively. The results indicate that the VD-LSTM model has more advantages than the LSTM model, with increased NSE, and reduced RMSE and bias to varying degrees. The flow simulation results of VD-LSTM better match the observed flow hydrographs, improving the underestimation of peak flows and the lag issue of the model. Under the same task and dataset, with the same hyperparameter settings, VD-LSTM can more quickly reduce the loss function value and achieve a better fit compared to LSTM. The proposed VD-LSTM model couples the vectorization process of flood runoff with the LSTM neural network, which contributes to the model better exploring the change characteristics of rising and receding water in flood runoff processes, reducing the training gradient error of input–output data for the LSTM model, and more effectively simulating flood process.