Flood Damage Mitigation Research Articles

Geotechnical characteristics of levees subjected to sand boil due to high-water levels

The investigation of river levees holds significant implications for mitigating flood damage. Sand boiling, backward erosion piping, and phenomena manifesting along the riverside of levees directly imperil the integrity of these structures. It is imperative to address these phenomena comprehensively to safeguard both lives and property amidst flood events. The principal aim of this research is to delineate the variances in geotechnical conditions between sand boils observed at slope toes on the landside and those occurring at a distance from this region along the levee. Therefore, this study conducted extensive boring investigations at sites where sand boils occurred. The soil samples sampled from the boring investigations were analysed for grain size. The results of a series of geotechnical investigations showed that in the cases where sand boils occurred near the toe of the slope, a series of sandy soils with grain size characteristics similar to those of the sand boils were deposited in the foundation of the levee. On the other hand, in the case where the sand boil occurred far from the toe of the slope, sandy soil with grain size characteristics similar to that of the sand boil was deposited only on the landside.

An Analysis of Policy Frameworks on Business Formulization and Disaster Management to Mitigate Flood Loss and Damage Among Informal Businesses in Sri Lanka

The 27th Conference of the Parties to the United Nations Framework Convention on Climate Change in Egypt made a historical decision on disaster loss and damage for vulnerable countries. Even though parties agreed to salvage vulnerable nations with generous support, a fundamental question remains as to the extent to which these vulnerable countries can accurately identify the most vulnerable communities/areas. In Sri Lanka, informal businesses are particularly vulnerable to floods, but their informal status has limited the extent to which they could receive flood protection, including disaster-resilient infrastructure developments. The main objective of this paper, therefore, is to identify challenges that informal businesses have experienced in dealing with flood disaster risks. In doing so, we analyzed government policies and laws that are related to business formalization and disaster management. We also conducted interviews with key informants to verify our data. Our analysis found that the Sri Lankan government requires informal business owners to follow complicated rules to register their businesses. For these owners, who are not highly educated, these processes and fear of high tax rates discouraged registering their businesses. The central government tends to prioritize flood mitigation actions for formalized business areas. Informal businesses are not usually covered by flood insurance and compensation. In conclusion, we emphasize the need to establish widely available legal and administrative support for informal businesses to register. Adopting business continuity plans (BCPs) and keeping standardized business records also help businesses minimize flood loss and damage.

Reservoir-based flood forecasting and warning: deep learning versus machine learning

In response to increasing flood risks driven by the climate crisis, urban areas require advanced forecasting and informed decision-making to support sustainable development. This study seeks to improve the reliability of reservoir-based flood forecasting and ensure adequate lead time for effective response measures. The main objectives are to predict hourly downstream flood discharge at a reference point, compare discharge predictions from a single reservoir with a four-hour lead time against those from three reservoirs with a seven-hour lead time, and evaluate the accuracy of data-driven approaches. The study takes place in the Han River Basin, located in Seoul, South Korea. Approaches include two non-deep learning (NDL) (random forest (RF), support vector regression (SVR)) and two deep learning (DL) (long short-term memory (LSTM), gated recurrent unit (GRU)). Scenario 1 incorporates data from three reservoirs, while Scenario 2 focuses solely on Paldang reservoir. Results show that RF performed 4.03% (in R2) better than SVR, while GRU performed 4.69% (in R2) better than LSTM in Scenario 1. In Scenario 2, none of the models showed any outstanding performance. Based on these findings, we propose a two-step reservoir-based approach: Initial predictions should utilize models for three upstream reservoirs with long lead time, while closer to the event, the model should focus on a single reservoir with more accurate prediction. This work stands as a significant contribution, making accurate and well-timed predictions for the local administrations to issue flood warnings and execute evacuations to mitigate flood damage and casualties in urban areas.

Modeling the dynamics of social preparedness with regional interactions to explore efficient flood early warning systems

Abstract Flood early warning systems (FEWS) are essential in mitigating flood damage. To optimize their effectiveness, it is important to understand how people respond to warnings and prepare for flooding events. The key factors influencing social preparedness include (1) direct and (2) indirect experiences of floods and (3) trust in warnings. However, existing socio-hydrological models do not incorporate all these elements. To include these elements for social preparedness, we propose an idealized model that allows multiple regions to influence one another (i.e., regional interactions). We investigate the dynamics of social preparedness in a society composed of regions with varying infrastructure levels (e.g., levee heights) and explore strategies for developing a socially efficient FEWS. Numerical analyses reveal that in a society that has a region characterized by a low infrastructure level (i.e., a region with frequent floods), regional interactions lead to a pronounced cry wolf effect due to false alarms from other regions, diminishing social preparedness in the low-infrastructure region. These interactions also prevent a warning strategy that optimizes the natural science-based index (i.e., threat score) from maximizing social efficiency. Conversely, in a society that has a region characterized by a high infrastructure level (i.e., a region with infrequent floods), regional interactions enhance the efficiency of FEWS by improving social preparedness through indirect experiences with floods. These findings suggest that as regional heterogeneity increases, it becomes increasingly vital for forecasters to consider social aspects (e.g., people’s experiences, trust, and interactions) when establishing a socially efficient FEWS. The refined model will be valuable to forecasters in designing effective FEWS in real-world situations.

DEVELOPING A LOCALIZED OPTIMAL FLOOD BARRIER IN THE PHILIPPINES

In the Philippines, flood is a usual experience due to its geographical location near the equator. This calls for governmental units to establish flood control protocols that utilize equipment to effectively mitigate the impact of the floods. However, the existing protocols lack proper equipment integrated in their flood protocols that effectively handle flood occurrences. This research aims to improvise the current practices of the Philippine local government units in combating flood by proposing the optimal flood barrier design to be deployed for flood control and damage mitigation and investigate the optimal flood barrier for deployment. The methodology involves revisiting operational manuals of the target community of the Philippine governmental units and exploring existing flood barrier designs in literature and the market, whether in or out of the Philippines. Barriers designed in the literature and market were investigated, where there is a physical experiment conducted for the optimal design using physics concepts and past tests performed by studies. Results demonstrated the amount of force that can be resisted by the barriers, the amount of flood water until it floats, and the maximum velocity until barriers move. This is the first study to investigate the designs of flood barriers that are suitable for deployment and selects the optimal barrier for flood control for the Philippine setting while integrating it to the Philippine flood protocols. This research will be valuable to studies in regards with flood emergency disaster management planning.

Improving flood forecast accuracy based on explainable convolutional neural network by Grad-CAM method

The advent of deep learning techniques has shown promising improvement in flood forecasting accuracy which are crucial for operating reservoir and mitigating flood damages. However, the practical application of typical deep learning models, such as the Long Short-Term Memory network (LSTM), is hindered by their high complexity, computational cost, and lack of interpretability. This study introduces an Explainable Convolutional Neural Network (ECNN) model that can effectively capture inter-variable relationships and temporal dynamics to address these challenges. The proposed ECNN model was applied to the Lushui basin in China and compared with benchmark models. The interpretability of the ECNN model was analyzed using the Gradient-weighted Class Activation Mapping (Grad-CAM) method. Our findings demonstrated that the ECNN model exhibits exceptional performance in flood forecast tasks, particularly for long-term forecast horizons. Furthermore, precipitation and inflow play a decisive role in flood forecasting with the watershed-specific flood characteristics. The ECNN model is highly promising in real-time flood forecasting with ingenious parallel structure, remarkable accuracy, and interpretability.

Comparing 5D BIM costs: stilt housing against conventional housing for flood management

Purpose Sustainable and resilient infrastructure projects aim to reduce flooding impacts and improve community adaptability. For instance, flood-resistant stilts elevate structures, mitigating flood damage. Comprehensive consideration is crucial when adding elements to housing projects, incurring costs for all involved parties. This study aims to assess the viability of concrete stilts for cost-effective flood mitigation in Malaysian terrace housing. Design/methodology/approach The study evaluates cost implications through a comparative 5D building information modeling (BIM) cost analysis of stilted and conventional (standard) housing models. This assesses the percentage increase in total cost. Furthermore, a survey of construction professionals was undertaken. The study used online convenience and stratified sampling techniques. Out of the 222 emails that were sent, 27 construction professionals located in Johor, Sabah and Selangor within Malaysia participated in the research survey. Their perspectives on stilt housing prospects and factors for costing such structures were analyzed through a descriptive analysis using SPSS. Findings The case study models revealed that the incorporation of stilts could lead to a 21.64% increase in the overall cost per unit. This cost increase was primarily attributed to the additional reinforcement required. However, the survey findings highlighted that a majority of construction professionals perceived the cost increment to fall within the range of 10%–20%. Consequently, it becomes imperative to meticulously consider cost factors such as foundational requirements, staircases, and the extended construction duration to effectively curtail expenditures. The prospect of heightened costs potentially posing a threat to profit margins and discouraging developers necessitates careful financial management. Notwithstanding these challenges, the survey's insights underscored that professionals in the construction industry indeed recognize the potential of stilt technology in the realm of flood mitigation and management, particularly within housing projects. Practical implications This research has significant practical implications. It provides a precise financial contrast between housing categories using 5D BIM and incorporates construction experts’ viewpoints on raised housing. Enhanced design considerations for raised housing can make it economically viable, offering a cost-effective, nature-based approach to flood mitigation. This approach can bring substantial benefits to residents by reducing flood-related damages and enhancing community resilience. Originality/value One of the notable aspects of this research is its originality. It uses a dual quantitative methodology involving modeling and survey techniques to address its objectives effectively. This approach contributes significantly to the relatively limited body of research focused on stilt housing and the application of 5D BIM. By combining these methodologies, the study explores a relatively uncharted area, making a valuable contribution to the field.

Assessing future urban flood hazard: A comprehensive approach to estimating the implications of future rainfall scenarios

AbstractEstimating potential changes in future flood patterns based on anticipated changes in hydrological characteristics within the basin is crucial for mitigating flood damage and managing flood risk. In this study, nonparametric probability models are used to estimate future rainfall patterns in Seoul under the GCM‐based climate change scenarios (CCS), and the estimated future daily rainfall data was temporally downscaled to hourly units using the KNNR‐GA technique. Changes in flood hazard and runoff characteristics of the target area based on the estimated future rainfall data are quantitatively assessed. The results highlight that under CCS, flood runoff may increase further into the future, resulting in more significant changes in flood patterns and accelerating the increase in flood hazard. The delta change factor of flood risk indicators increased relatively significantly in more severe CCS. This study also proposed a process to estimate future flood runoff and mitigation effects according to CCS by reflecting various flood mitigation measures in the urban drainage system model. These findings can offer valuable insights for setting the direction of current and future mitigation measures.

DEVELOPMENT AND ANALYSIS OF A GIS-BASED CURVE NUMBER MAP OF THE PHILIPPINES

Stormwater management requires quantitative methods to determine objective flood risk by estimating how much rainfall becomes runoff. With the absence of a locally generated runoff coefficient database in the Philippines, the standard model for surface runoff estimation is yet to be implemented using homegrown datasets. Traditionally, the empirical method is adapted from the National Resources Conservation Service (NRCS). The method quantifies rainfall-runoff relationships underscoring the combined effects of ground cover, soil hydraulic conductivity, and antecedent runoff condition (ARC) on runoff potential via a hydrologic parameter called the Curve Number (CN). Using geographic information system (GIS) tools, the Philippine CN map is developed by preprocessing and intersecting land use and land cover (LULC) and hydrologic soil group (HSG) with the CN look-up table. It was revealed that Group C soils dominate the Philippines. New data products—three raster CN maps at 25m spatial resolution—indicated the prevalence of medium to high runoff potential. National curve numbers were 61, 78, and 89 for dry, average, and wet ARCs, respectively. The outputs of this study provide access to spatially varied local runoff potential data in GIS format thus allowing direct GIS use for the swift simulation of flood damage mitigation models, among others.

Unraveling the interplay of human decisions and flood risk: An agent-based modeling approach

Flooding is a frequent and highly destructive natural disaster that requires effective flood management policies. This paper addresses the limitations of traditional flood risk assessment methods, focusing on the interplay between human decisions and flood risk using an integrated approach. Agent-Based Modeling (ABM) is employed to capture the social behavior of government, households, and farmers in three flood management scenarios. The study is conducted in the Fall River watershed, Idaho, USA, over a 30-year period. The results highlight the important influence of crop selection decisions on flood damage outcomes, with alfalfa emerging as the preferred crop due to its profitability and lower damage potential (around 67 % crop loss compared to more than 81 % for crops like corn, barley, and wheat). The levee scenario is identified as the most effective strategy, significantly reducing flood damage by more than 37 % and generating the highest average annual profit for farmers. The study emphasizes the need for comprehensive flood risk management strategies that address both frequent and rare flood occurrences in mitigating flood damage and promoting community resilience. By integrating human-flood interactions into flood risk assessment, this study offers valuable insights for Policymakers and stakeholders can utilize the research implications to develop effective strategies that mitigate damage and enhance community resilience.

Development of a spatial framework for flash flood damage assessment and mitigation by coupling analytics of machine learning and household level survey data – A case study of rapid collaborative assessments and disbursement of public funds to the affectees of floods 2022, Punjab Pakistan

In August 2022, one of the most devastating floods in the history of Pakistan was triggered due to heavy and exceptionally high monsoon rainfall. It has affected thirty-six million people across the country and agriculture losses were recorded in the most productive Indus plains which aggravated the food insecurity in the country. This study focuses on the two districts of Punjab provinces includes DG Khan and Rajanpur, where huge flood damages were reported. As part of damage and loss assessment methodologies, GIS/RS based approach is developed in this study to estimate flood extent in DG Khan and Rajanpur. Field survey was conducted using android based devices with the help of military and government organizations such as PDMA, to verify and record flood damages. Information regarding damage includes property, livestock, people, crop and other damages with real-time images captured and stored via android app and flood data monitoring was visualized on a web-based dashboard. SAR data (Sentinel 1-A) was used to calculate and verify extent of the flood along with Sentinel 2-A dataset to extract Land cover classes. SAR flood maps were validated using the survey data retrieved during flood events. This data was compared with the Sentinel-1A dual polarised (VV & VH). The SAR data (Sentinel-1A) was also collected for the same day to extract the Flood Extent of August/September 2022 using Modified Normalized Difference Water Index (mNDWI). Hence, flood inundation map was prepared using polarisation. This examination is confirmed through the kappa coefficient (κ), which is 0.88 for VV polarisation and 0.86 for VH polarisation. In VV polarisation, PA and UA of ‘water’ are less than of all ‘other classes. Mauza level mapping was also conducted to identify the damaged mauzas in both floods affected districts to figure out damaged villages and find out damaged property with khasra or mauza name. GIS RS based and survey-based model was developed in this research, which replicated on two districts of Punjab to assess flood damage, it will help Pakistan government to always asses flood damages to crop, houses, livestock and other assets. This helped to communicate with the local governments and identify and rank high affected villages; so that priority shall be given as per intensity of the damages. An android application for flood survey was developed. The flood survey application provided facility to estimate the on-ground reality of each individual, property, crop and livestock damage due to flood. This application was mainly used for the assessment purpose that provides detail information with precise location and real visualization of the damage assets. Joint survey teams were constituted including members from the Provincial Disaster Management Authority, National Disaster Management Authority, Pakistan Army, Local Government and the Urban Unit. Each village classified as flood damages/affected was visited by the survey team through a proper survey plan. After all the flood data was recorded, now the next step was to build transparent system to distribute funds to the flood victims. For this purpose, Bank has developed dashboard to distribute funds to the affected by the Government of Punjab.

Enhancing resilience: Understanding the impact of flood hazard and vulnerability on business interruption and losses

Without taking additional measures, flooding is becoming more likely and intense in a changing climate, which causes large economic damage. Households and firms are directly impacted by physical flood damage, but further ripple effects on society occur through business disruptions. By using post-disaster survey data from the 2021 flood event in the Netherlands, this study adds to the literature on business interruption duration and losses after flooding. The current empirical literature on flood impacts on firms is often unable to distinguish separate effects for flooded and non-flooded firms and does not incorporate flood severity and the influence of risk reduction measures. Here, we use multivariate regression models to determine depth-duration functions that describe the relationship between flood hazard characteristics and business interruption duration. This relationship can be used to calibrate flood damage models that capture indirect firm impacts. The prediction of business interruption after flooding allows for differentiation in business interruption between firms within a flooded area, reducing the reliance of these macroeconomic models on restrictive assumptions. Our results indicate that a day of business interruption duration costs a firm on average 0.5 % of their annual revenue; an effect that is stronger for firms with a weaker connection to their region. Flood damage mitigation (FDM) measures taken at the building level do not significantly affect business interruption duration, although further research on this is required. Finally, quick damage compensation is found to reduce business interruption duration and thus revenue losses, calling for higher insurance uptake and rapid and streamlined post-disaster insurance and government compensation.

Efficiency Evaluation of Surface Water Collection Infrastructure during Floods: Information Analysis and Zoning Management

Objective: Flood events, occurring as natural and unexpected phenomena, have become increasingly prevalent in recent decades. Assessing the probability of flood risk and creating flood zoning maps in vulnerable areas, particularly urban regions, are crucial for mitigating flood damages and managing such events. This research aims to determine the flood-prone zones along the Darakeh River, located in Tehran, the capital city of Iran, by integrating the hydraulic model HEC-RAS with Geographic Information System (GIS). Methods: After establishing the physiographic characteristics of the watershed, including area, perimeter, elevation, slope, and time of concentration, influential factors on flood occurrences such as permeability, hydrological soil groups, runoff potential, curve number, land use, and vegetation cover were identified. This information, along with other hydraulic data and flow specifications such as boundary conditions, roughness coefficients, and design discharge, was incorporated into the HEC-RAS model. The model was executed for different return periods of 2, 5, 10, 25, 50, and 100 years. Subsequently, the hydraulic model outputs were transferred to the GIS model to obtain the flood-prone zones along the Darakeh River for the aforementioned return periods. Results: As observed, in the middle section of the Khoshke Channel, which has a lower gradient, the floodplain exhibits a wider extent. Furthermore, along the course of the river and after passing through a stretch of the Khoshke Channel with a reduced gradient, the floodplain's area increases up to the location of the western flood channel. Conclusion: It is concluded that areas with lower river slopes exhibited larger flood-prone zones, highlighting the necessity of increased attention to prevent significant damages in urban areas during flood events.

A Transfer Learning Approach Based on Radar Rainfall for River Water-Level Prediction

River water-level prediction is crucial for mitigating flood damage caused by torrential rainfall. In this paper, we attempt to predict river water levels using a deep learning model based on radar rainfall data instead of data from upstream hydrological stations. A prediction model incorporating a two-dimensional convolutional neural network (2D-CNN) and long short-term memory (LSTM) is constructed to exploit geographical and temporal features of radar rainfall data, and a transfer learning method using a newly defined flow–distance matrix is presented. The results of our evaluation of the Oyodo River basin in Japan show that the presented transfer learning model using radar rainfall instead of upstream measurements has a good prediction accuracy in the case of torrential rain, with a Nash–Sutcliffe efficiency (NSE) value of 0.86 and a Kling–Gupta efficiency (KGE) of 0.83 for 6-h-ahead forecast for the top-four peak water-level height cases, which is comparable to the conventional model using upstream measurements (NSE = 0.84 and KGE = 0.83). It is also confirmed that the transfer learning model maintains its performance even when the amount of training data for the prediction site is reduced; values of NSE = 0.82 and KGE = 0.82 were achieved when reducing the training torrential-rain-period data from 12 to 3 periods (with 105 periods of data from other rivers for transfer learning). The results demonstrate that radar rainfall data and a few torrential rain measurements at the prediction location potentially enable us to predict river water levels even if hydrological stations have not been installed at the prediction location.

Technical Note: Resolution enhancement of flood inundation grids

Abstract. High-resolution flood maps are needed for more effective flood risk assessment and management. Producing these directly with hydrodynamic models is slow and computationally prohibitive at large scales. Here we demonstrate a new algorithm for post-processing low-resolution inundation layers by using high-resolution terrain models to disaggregate or downscale. The new algorithm is roughly 8 times faster than state-of-the-art algorithms and shows a slight improvement in accuracy when evaluated against observations of a recent flood using standard performance metrics. Qualitatively, the algorithm generates more physically coherent flood maps in some hydraulically challenging regions compared to the state of the art. The algorithm developed here is open source and can be applied in conjunction with a low-resolution hydrodynamic model and a high-resolution DEM to rapidly produce high-resolution inundation maps. For example, in our case study with a river reach of 20 km, the proposed algorithm generated a 4 m resolution inundation map from 32 m hydrodynamic model outputs in 33 s compared to a 4 m hydrodynamic model runtime of 34 min. This 60-fold improvement in runtime is associated with a 25 % increase in RMSE when compared against the 4 m hydrodynamic model results and observations of a recent flood. Substituting downscaling into flood risk model chains for high-resolution modelling has the potential to drastically improve the efficiency of inundation map production and increase the lead time of impact-based forecasts, helping more at-risk communities prepare for and mitigate flood damages.

Estimation of river discharge using Monte Carlo simulations and a 1D hydraulic model based on the artificial multi-segmented rating curves at the confluence of two rivers

During extreme floods caused by climate change, reliable flow discharge data are essential for successful reservoir operation to mitigate downstream flood damage. Generally, the flow discharge is computed using the rating curve (RC) established from the relationship between the flow rate and water stage level. Determining the parameters of rating curves is subject to uncertainties related to the difficulties and limitations of flow monitoring in covering a wide range of flow variations. Especially at river confluences, the uncertainties are pronounced when floods occur owing to several factors such as roughness change, backwaters, and levee overflow. The Seomjin River Basin in Korea suffered from flood inundation that occurred at the tributary confluence during an extreme flood in 2020. To identify a reliable flow rate of the main stream and tributary, this study proposes an indirect flow assessment scheme using a 1D hydrodynamic simulation model to find the best simulated water level in an iterative manner based on Monte Carlo (MC) simulations. With a large amount of discharge data generated from random-number combinations, it is possible to obtain the best results automatically by specifying the reliability limitation considering the uncertainty of the predetermined RC parameters associated with the roughness coefficient. Nash Sutcliffe Efficiency (NSE) was incorporated to evaluate the reproduced water level to meet the threshold specified for NSE ≥ 0.75. The simulated flowrates computed from the revised RC and roughness coefficients revealed an error range of 8%–36.6% compared with the design flood. The approach proposed in this study is applicable for determining the valid parameters necessary to create a revised RC at an existing water level gauge station, where the uncertainties of the RC are pronounced, particularly in the vicinity of the channel confluence.

Urban Flood Modelling under Extreme Rainfall Conditions for Building-Level Flood Exposure Analysis

The expansion of urban areas and the increasing frequency and magnitude of intense rainfall events are anticipated to contribute to the widespread escalation of urban flood risk across the globe. To effectively mitigate future flood risks, it is crucial to combine a comprehensive examination of intense rainfall events in urban areas with the utilization of detailed hydrodynamic models. This study combines extreme value analysis techniques applied to rainfall data ranging from sub-hourly to daily durations with a high-resolution flood modelling analysis at the building level in the centre of Thessaloniki, Greece. A scaling procedure is employed to rainfall return levels assessed by applying the generalised extreme value (GEV) distribution to annual maximum fine-temporal-scale data, and these scaling laws are then applied to more reliable daily rainfall return levels estimated by means of the generalised Pareto distribution (GPD), in order to develop storm profiles with durations of 1 h and 2 h. The advanced flood model, CityCAT, is then used for the simulation of pluvial flooding, providing reliable assessments of building-level exposure to flooding hazards. The results of the analysis conducted provide insights into flood depths and water flowpaths in the city centre of Thessaloniki, identifying major flowpaths along certain main streets resulting in localised flooding, and identifying around 165 and 186 buildings highly exposed to inundation risk in the study area for 50-year storm events with durations of 1 h and 2 h, respectively. For the first time in this study area, a detailed analysis of extreme rainfall events is combined with a high-resolution Digital Terrain Model (DTM), used as an input into the advanced and fully featured CityCAT hydrodynamic model, to assess critical flowpaths and buildings at high flood risk. The results of this study can aid in the planning and design of resilient solutions to combat urban flash floods, as well as contribute to targeted flood damage mitigation and flood risk reduction.

Flood Susceptibility Mapping Using Machine Learning Algorithms: A Case Study in Huong Khe District, Ha Tinh Province, Vietnam

A flood is a natural catastrophe that causes heavy damage not only to people but also to properties. To prevent and mitigate flood damage, an accurate flood susceptibility map that reveals highly potential flood-prone areas is essential. This study aims to construct flood susceptibility maps in the Huong Khe district using three machine learning algorithms, namely the K - Nearest Neighbour (KNN), the Support Vector Machine (SVM) and Artificial Neural Network (ANN). Training and testing datasets were extracted from Sentinel-1 SAR images. Seven causative factors were selected as input for predictive models after removing high-correlation factors and unimportant factors through a rigorous screening process by analyzing the Pearson correlation coefficient (PCC) and calculating the information gain ratio (InGR). The model's hyperparameters were found by grid search algorithm integrated 5-fold cross-validation. The three optimal flood susceptibility models showed excellent performance, with very high accuracy indices in the training and testing phases, over 90% of overall accuracy and UAC values. High and very high susceptibility classes on flood susceptibility maps accounted for around 18% of the total study area and were mainly located in residential and agricultural areas. Thus, there is a need to make proper land use planning for these areas to reduce damage in flood seasons.

Flood Vulnerability Models and Household Flood Damage Mitigation Measures: An Econometric Analysis of Survey Data

AbstractFlood events are expected to increase in their frequency and severity, which results in higher flood risk without additional adaptation measures. The information gained from flood risk models is essential in effective disaster risk management. However, vulnerability estimations are often a large driver of uncertainty, and flood damage is rarely estimated due to a lack of empirical damage data from flood events. This study uses a unique data set with experienced damages and the implementation of flood damage mitigation (FDM) measures on the household level, collected after the flood event in the Netherlands in 2021. Flood damage models that control for several hazard, exposure, and vulnerability indicators are estimated and allow for additional input in flood risk models. Previous estimates of the effectiveness of FDM measures are prone to a selection bias, as households that do, and do not implement FDM measures systematically differ in their risk profiles. By using an instrumental variable‐estimation, this study overcomes this selection bias and finds significant reductions in flood damage due to FDM measures. These reductions can be incorporated in multivariate flood vulnerability estimations, which indicate that FDM measures significantly reduce flood damage. Providing information on flood hazard, as well as implementing early warning systems, is crucial for ensuring effective flood risk management.

Evaluation of an Adaptation Strategy for Flood Damage Mitigation Under Climate Change Through the Use of Irrigation Reservoirs in Japan

AbstractThis study performed flood analysis, considering the water storage of irrigation reservoirs, to realize flood control by reservoirs, which are traditional irrigation facilities. The effect on flood control was determined through flood analysis performed by varying the reservoir storage rates. Based on the flood analysis results, Japan’s flood damage costs were estimated. Using three Shared Socioeconomic Pathways (SSPs) (SSP1-2.6, SSP2-4.5, and SSP5-8.5), we evaluated the flood control potential of reservoirs under future climate conditions. When the reservoirs were empty, the damage cost reduction rate resulting from the use of reservoirs across Japan was small, ranging from 1.1% to 2.3%, but as the damage cost reduction rate did not change under all future scenarios, reductions in flood damage were not affected by changes in rainfall under future climate conditions. Moreover, some prefectures showed high damage cost reduction effects. In northern part of Japan and numerous prefectures in western Japan, the potential to reduce damage through flood control by irrigation reservoirs was high. Some prefectures experienced similar reduction levels as those under alternative adaptation strategies. In the Kanto region, flood control by irrigation reservoirs had low potential for reducing damages. Even among prefectures with low water storage capacities of irrigation reservoirs, the potential to mitigate the effect of damage was high.