Flood Damage Assessment Research Articles

A Review of Cutting-Edge Sensor Technologies for Improved Flood Monitoring and Damage Assessment

Floods are the most destructive, widespread, and frequent natural hazards. The extent of flood events is accelerating in the context of climate change, where flood management and disaster mitigation remain important long-term issues. Different studies have been utilizing data and images from various types of sensors for mapping, assessment, forecasting, early warning, rescue, and other disaster prevention and mitigation activities before, during, and after floods, including flash floods, coastal floods, and urban floods. These monitoring processes evolved from early ground-based observations relying on in situ sensors to high-precision, high-resolution, and high-coverage monitoring by airborne and remote sensing sensors. In this study, we have analyzed the different kinds of sensors from the literature review, case studies, and other methods to explore the development history of flood sensors and the driving role of floods in different countries. It is found that there is a trend towards the integration of flood sensors with artificial intelligence, and their state-of-the-art determines the effectiveness of local flood management to a large extent. This study helps to improve the efficiency of flood monitoring advancement and flood responses as it explores the different types of sensors and their effectiveness.

Chinese Satellite-Based Flood Mapping and Damage Assessment in Dongdian Flood Detention Basin, China

Chinese Satellite-Based Flood Mapping and Damage Assessment in Dongdian Flood Detention Basin, China

Increasing the available water diversion volume of water source project through flood resource utilization: A case study of the middle route of the South-to-North water diversion project in China

Water supply operating rules are critical for guaranteeing the safety of the water supply in the source project and receiving areas and overcoming the uneven distribution of water resources among different regions. Previous studies have mainly focused on optimizing the water supply operating rules to balance multiple benefits under the current engineering design parameters. In this study, a framework is proposed in which the water supply operating rules for the resource reservoir are adapted through flood resource utilization. The correlations among seasonal floods are first analyzed by considering the influence of water diversion projects. The seasonal flood-limited water levels (FLWLs) are then re-designed in terms of a flood damage assessment index under the deduced most-probable seasonal floods. Finally, the water supply operating curves of the resource reservoir are optimized by adopting the recommended seasonal FLWLs. The middle route of the South-to-North Water Diversion Project in China is taken as a case study. The results show that the recommended seasonal FLWLs are 160.2 m in summer and 164.2 m in autumn, and that the annual average available water diversion volume can be increased by 0.52 billion m3 without increasing the flood risk. Compared with the current water supply operating rules, the optimal operating curves make the annual distribution of the available water diversion volume more uniform in different typical-year scenarios and reduce the surplus reservoir water. These findings are helpful for exploring the potential of water resource utilization.

Flood damage models and flood damage factors in a data-scarce river basin, Nigeria

ABSTRACT To evaluate the effectiveness of various adaptation strategies, it is critical to develop a flood damage function that considers building characteristics for future risk evaluation. This study analysed the contributions of some important flood and household characteristics in flood damage processes and developed a new, more accurate, multi-variable flood loss model for flood damage assessment for residential buildings and contents, considering different house types in Ogun River Basin, Nigeria. A detailed household questionnaire survey was conducted to collect data (149 samples). Depth-damage functions were developed to estimate direct flood damage to building structures and contents using the nonlinear logistic and power (concave) models, and the model parameters were estimated using the Gauss–Newton algorithm. The performance of all flood damage regression analysis functions for building structures and building contents under different construction materials and basement floors was evaluated by comparing predicted damages and after-flood survey damages, which demonstrated acceptable comparativeness. In the event of a flood disaster in the area, the models can be used to forecast future damages and devise risk-mitigation strategies. The study’s findings will aid in developing flood protection adaptation measures.

Flood Vulnerability Index Based on Indicator Approach in Assessing Flood Risk in Selangor, Malaysia

Floods are one of the most common and damaging natural disasters, claiming lives, and causing significant economic damage. Urbanization can increase the risk of flooding due to increased water runoff and volume. This study focuses on a flood vulnerability assessment approach as a planning tool by combining the Flood Vulnerability Index (FVI) with variables available in urban development areas. This index was created by combining the variables of three components: exposure, susceptibility, and resilience. FVI is a critical element in risk management and flood damage assessment. Vulnerability is described in FVI research as the amount of damage that can be predicted under conditions of exposure, susceptibility, and resilience. Vulnerability studies frequently use an indicator-based vulnerability assessment to estimate the complexity and degree of risk. FVI reflects the vulnerability of urban areas. Secondary data were used to obtain variables for the social and economic components. A quantitative method was used to calculate the correlation of each variable with flooding via the Statistical Package for the Social Sciences (SPSS). The indicators are categorized as exposure, susceptibility, and resilience, and these indices were created using normalization, weighting, and aggregation of the indicators. To determine the flood vulnerability in Selangor, a small number of indicators were selected to quantitatively assess the flood risk in Selangor. Based on calculated FVI, the vulnerability of the districts in Selangor has been ranked and categorized from high to very low. The result shows Gombak and Klang are the most vulnerable districts in Selangor to flooding because of their high exposure to flooding. The results of this study could serve as a reference tool for future flood risk mitigation efforts in Selangor.

Developing an Ensemble Machine Learning Approach for Enhancing Flood Damage Assessment

Developing an Ensemble Machine Learning Approach for Enhancing Flood Damage Assessment

Flood assessment using machine learning and its implications for coastal spatial planning in Phu Yen Province, Vietnam

ABSTRACT The objective of this study was the development of a new machine learning model using a radial basis function neural network (RBFNN) to build flood susceptibility maps and damage assessment for the Phu Yen province of Vietnam. The built model will be optimized by five algorithms, namely Giant Trevally Optimization (GTO), Golden Jackal Optimization (GJO), Brown-Bear Optimization (BBO), Gray Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA) to find out the best model to establish the flood susceptibility map. These models were evaluated using the statistical indices such as root mean square error (RMSE), mean absolute error (MAE), receiver operating characteristic (ROC), area under the curve (AUC), and coefficient of determination (COD). The result showed that all five optimization algorithms were successfully improving the performance of the RBFNN model, among them the hybrid model RBFNN–BBO has the highest performance with AUC = 0.998 and R2 = 0.8 and the RBFNN–GTO model has the lowest performance with AUC = 0.755 and R2 = 0.65. The regions identified with a high- and very-high flood susceptibility area (1,075 km2) were concentrated on the plain and along three of the largest rivers in Phu Yen province.

Flood damage assessment using HEC-FDA software in shoor watershed, Esfahan, Iran

Flood damage assessment using HEC-FDA software in shoor watershed, Esfahan, Iran

Applications of satellite data for rapid inundation assessment - A case study in Thua Thien Hue province

Floods are one of the most significant devastating natural hazards around the globe. Recent development in remote sensing technology supports faster and low-cost analysis of flood hazards. This study used the Sentinel-1 SAR data for flood mapping and damage assessment. We selected the October 2022 flood event in Thua Thien Hue province, Vietnam. The Change Detection and Thresholding (CDAT) method was adopted to detect the inundation areas. The results showed that The flood event affected to several districts with Quang Dien being the worst hit, followed by Phu Loc and Huong Tra. Phu Vang had the smallest flooded area. The total inundated area was up to 33,384.08ha. The derived flood map was overlaid onto the current land cover map to conduct an initial evaluation of the potential flood damage corresponding to each land use category. The damage to the land cover was estimated that most of the affected areas were in the cropland and accounted for 77.07% of the total inundated area. The results can assist decision-makers in monitoring and assessing flood damage in Central Vietnam

Flood damage assessment for the Phan-Ca Lo River basin in Vinh Phuc Province, Vietnam

ABSTRACT In this research, the authors aim to evaluate the damage caused by floods in Vinh Phuc Province. The study incorporates the construction of hydro-hydraulic models and the development of damage functions for the main elements at risk. In order to ensure the accuracy of the calculated results, the hydro-hydraulic models and damage curves are verified for their reasonableness. The outcomes of this study give us an overview of flood damage in the research area. It indicates that the potential level of flood damage is a matter of significant concern, with an annual damage estimate of approximately 6.9 million USD. The research also reveals the distribution of damage by sector and by space. According to the research findings, the greatest damage was in Vinh Tuong District, where most of the damage was caused by flooding of agricultural land. In contrast, residential buildings in the area suffered relatively little damage compared to agricultural land. This information is crucial for decision-makers to implement effective and targeted mitigation measures.

The Construction of a Crop Flood Damage Assessment Index to Rapidly Assess the Extent of Postdisaster Impact

Floods are among the most serious natural disasters worldwide; they cause enormous crop losses every year and threaten world food security. Many studies have focused on flood impact assessments for administrative districts, but fewer have focused on postdisaster impact assessments for specific crops. Therefore, this study used remote sensing data, including the normalized difference vegetation index (NDVI), elevation data, slope data, and precipitation data, combined with crop growth period data to construct a crop flood damage assessment index (CFAI). First, the analytic hierarchy process (AHP) was used to assign weights to the impact parameters; then, the Weighted Composite Score Method was used to calculate the CFAI; and finally, the impact was classified as sub-slight, slight, moderate, sub-severe, or severe based on the magnitude of the CFAI. This method was used for the Missouri River floods of 2019 in the United States and the Henan flood of 2021 in China. Due to the lack of measured data, the disaster vegetation damage index (DVDI) was used to compare the results. Compared with the DVDI, the CFAI underestimated the evaluation results. The CFAI can respond well to the degree of crop impact after flooding, providing new ideas and reference standards for agriculture-related departments.

REMOTE SENSING AND GOOGLE EARTH ENGINE FOR RAPID FLOOD MAPPING AND DAMAGE ASSESSMENT: A CASE OF TYPHOON GONI (ROLLY) AND VAMCO (ULYSSES)

Abstract. Flooding is a devastating natural disaster with global repercussions, affecting human life, crop production, and infrastructure. The increasing frequency and unpredictability of flood events, driven by climate change, highlight the urgent need for rapid and accurate flood mapping and damage assessment. This study explores the use of Google Earth Engine, a web-based platform that utilizes satellite imagery and geospatial data, for flood mapping and damage assessment. By incorporating localized datasets, such as region-specific land cover maps and population density information, the developed workflow provides a robust tool for assessing flood extent, identifying high-risk areas, and estimating the impact on cropland and urban centers. Key findings showed that Cagayan province presented the highest potential flood area, covering approximately 55,063 hectares. This province also had the largest population at risk, with around 39,440 individuals potentially exposed to the effects of flooding. Moreover, the cropland in Cagayan province experienced the most substantial impact, with around 91.26% being affected due to the swelling of the Cagayan River. Camarines Sur experienced severe devastation in urban areas, with about 269 hectares affected by flooding from the Bicol River. This study's findings contribute to a comprehensive understanding of flood impacts beyond the extent of flooding, facilitating informed decision-making, emergency response planning, and targeted interventions to mitigate the effects of floods on communities, agriculture, and urban areas. The approach presented here can be applied in other regions, supporting improved flood management and mitigation strategies for national.

Assessment of flood damage to agricultural crops under climate change scenarios using MRI-AGCM outputs in the Solo River basin of Indonesia

Assessment of flood damage to agricultural crops under climate change scenarios using MRI-AGCM outputs in the Solo River basin of Indonesia

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.

Improved flood depth estimation with SAR image, digital elevation model, and machine learning schemes

Study regionNhat Le river basin, the floodplains of Central coast of Vietnam. Study focusFlood disasters have a significant impact on population and economies worldwide. To accurately assess flood damage, it is crucial to estimate the water depth and predict the potential spread of damage. However, conventional methods of estimating flood depth can be time-consuming and costly due to the large amount of data required from hydraulic and rain-runoff models. To overcome these limitations, we propose a new approach that integrates a machine learning (ML) algorithm with a floodwater depth estimation tool (FwDET) based on SAR imagery and digital elevation models (DEMs). To evaluate our approach, we trained and tested six ML regression algorithms using 804 sets of inventory flood depth data from the historic flood on the Nhat Le River in Vietnam in October 2020 as the dependent variable. This approach significantly improves flood depth prediction accuracy, enabling a faster and more cost-effective estimation of flood damage. New hydrological insights for the regionOur research shows that by combining ML with FwDET, we were able to significantly enhance water depth prediction accuracy. We observed a 25.05% increase in the R2 coefficient and a 13.46% increase in the R coefficient. We recommend integrating satellite imagery, DEM, and ML for flood depth predictions, especially for floodplains with similar topographical conditions as the Nhat Le basin. This approach offers a practical, cost-effective, and near real-time solution for flood response. Implementing this approach can help improve flood damage assessment and mitigate the potential impacts of flood disasters on communities and economies.

A novel framework for assessment of human impact of floods: Demonstrated for the Indian subcontinent

Flood damages are a result of mutual interaction between flood hazard and socio-economic factors. The increase in flood damages over time can be attributed to various enhancements in the magnitude and frequency of floods as well as socio-economic development in the floodplains. The quantification and analysis of the human dimensions of flood damages is challenged due to irreversibility and lack of understanding of complex-human flood interactions, especially in less developed regions. Since flood damage assessments play a critical role in designing effective flood preparedness plans, we suggest including the human dimension in evaluating damages. We present a framework for the assessment of the human impact of floods by incorporating statistics on the number of people exposed, displaced, and fatalities recorded for each flood event. The framework involves categorizing human impact, identifying spatial clusters of different categories, and exploring the role of various hazard and socio-economic characteristics in determining impact. While categorizing and clustering of impact helps to distinguish between regions prone to different impact, understanding the role of socio-economic and demographic factors is expected to help in developing tailor-made strategies to mitigate flood disasters. The methodology broadly consists of categorization using unsupervised learning, spatial clustering using Local Indicators of Spatial Autocorrelation (LISA), and spatial regression. The results indicate that the impact varies from one region to another and so does the role of hazard and socio-demographic variables in determining impact. Moreover, the framework developed may be used to study the relevance of various socio-hydrological theories and their role in determining the human impact of floods.

Fuzzy machine learning model for class-based flood damage assessment from planetscope temporal data

Fuzzy machine learning model for class-based flood damage assessment from planetscope temporal data

Flood Mapping and Damage Assessment using Ensemble Model Approach

Flood Mapping and Damage Assessment using Ensemble Model Approach

Linking direct rainfall hydrodynamic and fuzzy loss models for generating flood damage map

ABSTRACT This research work proposes a combined method for mapping flood loss in catchment scale in which direct rainfall modelling and fuzzy approach are linked. The direct rainfall modelling was carried out using HEC-RAS 2D in which rainfall event hyetograph was defined as the boundary condition, and infiltration layer and roughness layer were other main inputs of the model. The fuzzy loss model was developed to assess direct-tangible damages of the flood in which expert opinions were applied to generate verbal fuzzy rules of flood loss. In this model, depth and velocity are inputs and normalized flood loss (between 0 and 1) is output. The results of the direct rainfall model and the fuzzy loss model were combined to generate loss map using python scripting in geographical information system. The output of direct rainfall model was verified based on recorded depths at downstream hydrometric station in which the Nash–Sutcliffe efficiency (NSE) and root mean square error (RMSE) were applied as the evaluation indices. Due to acceptability of indices (NSE = 0.75, RMSE = 0.83 m), the direct rainfall model was reliable. Maximum flood loss was 0.91 in the case study. Using the proposed approach is recommendable for to improve flood damage assessment in the catchments.

Decadal mapping of flood inundation and damage assessment in the confluence region of Rivers Niger and Benue using multi-sensor data and Google Earth Engine

Abstract Climate change has made weather patterns more extreme, causing floods in Nigeria. Flooding is the most frequent and serious natural hazard in the confluence region of Rivers Niger and Benue, impacting lives, agriculture, and socio-economic activities significantly. Advancements in satellite technology and computational capabilities have enhanced rapid information about flood extent for monitoring, mitigation, and planning. However, there is a dearth of information based on time series analysis of flood inundation and monitoring in the confluence region. In this study, Sentinel-1 Synthetic Aperture Radar, Sentinel-2, and Landsat-7 and Landsat-8 data were used to extract flood inundation for 10 years (2012–2022) in the confluence region of Rivers Niger and Benue. Flood extent/surface waterbodies were extracted using the Google Earth Engine platform, modified normalized difference water index, and normalized difference water index methods. The findings revealed that within 10 years, four significant flooding incidents occurred in 2012, 2018, 2020, and 2022, inundating 60.57, 48.24, 39.98, and 84.39 km2 of the area, respectively. The study underscores the need for the establishment of a decision support system for monitoring flood inundation and providing decision-makers necessary information for flood disaster preparedness, mitigation, and adaptation.