Flood Disaster Risk Assessment Research Articles

Flood Disaster Risk Assessment in Wuhan City Based on GIS Analysis and Indicator Ranking Using Random Forest

In recent years, with the acceleration of urbanization and the frequent occurrence of extreme weather globally, the risk of urban flood disasters has gradually increased, and its potential consequences are immeasurable. Therefore, conducting risk assessment of urban flood disasters is of great significance, as it is one of the foundations and decision-making means for Disaster Prevention and Mitigation, and has become a hot topic and trend in current research. This paper starts by exploring the concept and formation mechanism of urban flood disasters, taking Hazard Factors, Disaster-prone Environment sensitivity, Vulnerability of Exposed Bodies, and Disaster Prevention and Mitigation Capabilities as primary indicators. Based on this, a risk assessment index system is established with 14 secondary indicators, such as annual average rainfall, distance to water systems, elevation, and terrain undulation. The spatialization of each indicator data point is processed through ArcGIS10.7, and the importance of hazard and sensitivity indicators is ranked using the Random Forest algorithm. The indicators are then weighted using a combination of the Analytic Hierarchy Process (AHP) and the entropy method, and the combined weights of each assessment indicator are calculated. Taking Wuhan City as the research area, the weights of each indicator are input into the established risk assessment model. ArcGIS spatial analysis techniques and raster calculation functions are utilized to solve the fuzzy comprehensive evaluation of the assessment model, obtaining zoning maps of risk levels for hazard, sensitivity, vulnerability, disaster prevention, and mitigation capabilities, as well as the distribution of comprehensive risk levels. The validity and rationality of the model results are verified by actual disaster data, providing important reference for urban flood disaster prevention in the future.

Development and application of a comprehensive assessment method of regional flood disaster risk based on a refined random forest model using beluga whale optimization

To scientifically characterize regional flood disaster risks, a comprehensive regional flood disaster risk assessment index system was constructed based on the R cluster-factor analysis method. To improve the regional flood disaster risk identification ability, a random forest model refined by the beluga whale optimization (BWO-RF) was proposed. It was applied to the flood disaster risk assessment of the Jiansanjiang Branch of the Beidahuang Agricultural Reclamation Group in Heilongjiang Province, China. The research results show that the constructed BWO-RF model has strong generalization ability and optimization efficiency. The evaluation results have high rationality and stability, which fully demonstrates the applicability of the BWO-RF model in flood disaster risk assessment. On a time scale, the flood risk of the Jiansanjiang Branch shows a clear downward trend. However, due to weak economic development and insufficient water conservancy and flood control planning and construction, the risk of flood disasters fluctuates greatly in the later period, and the prevention and control situation is still not optimistic. On a spatial scale, the risk of flood disasters on farms in the northeast of Jiansanjiang Branch is low, while the risk on farms in the southwest is higher. Future flood control and drainage should focus on this area. The research results provide a technical method system for reference in flood disaster risk assessment and provide valuable information for reducing regional flood disaster risks.

Flood disaster industry-linked economic impact and risk assessment: a case study of Yangtze River Economic Zone

Flood disaster industry-linked economic impact and risk assessment: a case study of Yangtze River Economic Zone

Spatio-temporal variation and dynamic risk assessment of drought and flood disaster (DFD) in China

It is important to understand the variations of Drought-Flood-Disaster (DFD) from the past to future for disaster management decision-making under the backdrop of global warming. This study explored the spatio-temporal variations of DFD risks in China from the past (2000–2020) to future (2020–2100) based on the natural and socio-economic datasets. The main findings include: (1) The spatial pattern of disaster vulnerability decreased from 2000 to 2100. The overall vulnerability distribution showed a spatial pattern of high east and low west, and its eastern distribution presented high north and low south. (2) The drought hazard danger degree showed a trend of decreasing first and then increasing with obvious spatial heterogeneity. The flood hazard danger degree had a decreasing spatial distribution from southeast to northwest, with an overall gradually increasing trend. (3) The DFD risks showed a spatial pattern of high east and low west, and were more closely related to disaster vulnerability. Overall, the flood and drought risks had good consistency at the highest level (VI), mainly distributed in the North China Plain, Northeast Plain, eastern Sichuan, and southern Guangdong. This level means that the most severe damage to life and property will occur in these regions. (4) The DFD showed the direction of “north(east)-south(west)”, and the main axis of the ellipse moved north eventually. The DFD hot spots were mainly distributed in North China and the middle and lower reaches of the Yangtze River. The DFD cold spots were mainly distributed in the southwest, Qinghai-Tibet region, and Gansu province.

Retracted: Risk Assessment and Prediction of Rainstorm and Flood Disaster Based on Henan Province, China

Retracted: Risk Assessment and Prediction of Rainstorm and Flood Disaster Based on Henan Province, China

Flood Risk Assessment Based on a Cloud Model in Sichuan Province, China

Floods are serious threats to the safety of people’s lives and property. This paper systematically introduces the basic theories and methods of flood risk assessment, takes Sichuan Province as the study area, and establishes a flood risk assessment index system with 14 indicators in four aspects—disaster-causing factors, disaster-forming environment, disaster-bearing body, and regional disaster resilience capacity—from the causes of disaster losses and flood formation mechanisms. Furthermore, this paper constructs a flood disaster risk assessment model for Sichuan Province based on a cloud model, entropy value, and GIS technology. The model is validated by taking the July–August 2023 flood disaster as an example, and the results show that the distribution of the disaster was consistent with the flood risk assessment results of this paper, which verifies that the selected indicators are appropriate and the model is accurate and valid.

Detection and Classification of Buildings by Height from Single Urban High-Resolution Remote Sensing Images

Recent improvements in remote sensing technologies have boosted building detection techniques from rough classifications using moderate resolution imagery to precise extraction from high-resolution imagery. Shadows frequently emerge in high-resolution urban images. To exploit shadow information, we developed a novel building detection and classification algorithm for images of urban areas with large-size shadows, employing only the visible spectral bands to determine the height levels of buildings. The proposed method, building general-classified by height (BGCH), calculates shadow orientation, detects buildings using seed-blocks, and classifies the buildings into different height groups. Our proposed approach was tested on complex urban scenes from Toronto and Beijing. The experimental results illustrate that our proposed method accurately and efficiently detects and classifies buildings by their height levels; the building detection rate exceeded 95%. The precision of classification by height levels was over 90%. This novel building-height-level detection method provides rich information at low cost and is suitable for further city scene analysis, flood disaster risk assessment, population estimation, and building change detection applications.

Urban Flood Dynamic Risk Assessment Based on Typhoon Rainfall Process: A Case Study of Typhoon “Lupit” (2109) in Fuzhou, China

Flood disasters caused by typhoon rainfall seriously threaten regional social and economic development. Accurately assessing the risk of typhoons and their secondary disasters is a great challenge in disaster prevention and reduction. To address this, the city of Fuzhou, Fujian Province, which was severely affected by Typhoon “Lupit” (2109), was selected as a case study. A typhoon rainfall flood disaster system including four components (the disaster-causing factor, disaster-pregnant environment, disaster-bearing body, and disaster prevention and reduction capacity) was constructed. A typhoon-rainfall process comprehensive intensity index (TPCI) based on different time scales within the typhoon process was developed to accurately evaluate the flood risk. The TPCI represented the disaster-causing factors of rainfall intensity, duration, and concentration features. Geographical similarity and random forest (RF) were applied to screen 23 typical indices for an urban flood disaster risk assessment model. The results indicated that the TPCI based on a 6 h precipitation simulation at a 24 h time scale was highly effective in highlighting the role of short-term precipitation in the typhoon process. A total of 66.5% of the floodplain area had a medium-grade or higher TPCI value, while 32.5% of the area had a low-grade TPCI. Only 1% of the flooded areas were not identified, which indicated that the TPCI could accurately capture the risk of typhoon rainfall. The urban flood disaster risk assessment model comprehensively considered socioeconomic and natural environment conditions. High-risk areas were identified as regions with extreme precipitation and dense populations. The dynamic evaluation results accurately described the spatiotemporal differences in the flood disaster risk. A period of extreme precipitation lagged the landfall time of Typhoon “Lupit”, causing the proportion of areas above the medium–high-risk threshold of flood disasters to rapidly increase from 8.29% before the landfall of the typhoon to 23.57% before its demise. The high-risk areas of flood disasters were mainly distributed in the towns of Shangjie, Nanyu, and Gaishan, which was consistent with the observed disasters. These study findings could contribute to the development of effective measures for disaster prevention and reduction, and improve the resilience of urban areas to typhoon disasters.

Risk assessment and zoning of flood disaster in Wuchengxiyu Region, China

Based on the data of physical geography and social economy, an index system of Wuchengxiyu Region was constructed with the disaster-inducing factors, disaster-pregnant environment, and disaster-bearing body as indicators by analyzing the formation and impact factors of flood disasters. The weight of each index was given using the analytic hierarchy process, and the comprehensive evaluation index was constructed by the weighted comprehensive method, and the flood disaster evaluation model was established. The flood risk zoning of Wuchengxiyu Region was implemented by means of Geographic Information System spatial analysis methods. The results showed that the high-risk areas in Wuchengxiyu Region are mainly distributed in Liangxi District, Binhu District, Xishan District of Wuxi City, Zhonglou District, Tianning District, and Xinbei District of Changzhou City, and Jiangyin City. The high-risk area in Wuxi City is highly exposed due to its low terrain. The high-risk area in Changzhou City is highly vulnerable due to the high density of population, buildings and roads. And the high-risk area in Jiangyin City is located in the high-risk area of disaster-causing factors due to its abundant rainfall.

GIS-based risk assessment of flood disaster in the Lijiang River Basin

This study is designed to provide a scientific reference for the establishment of rainstorm and flood disaster prevention system in Guilin region and improve the risk assessment of rainstorm and flood disasters. To realize the goal, a flood risk evaluation model is established by weight analysis methods including the entropy weight method and the analytic hierarchy process from 3 aspects, i.e., risk of disaster causing factors, sensitivity of disaster-pregnant environment and vulnerability of disaster bearing body. For the model, the daily precipitation 1980–2020 of 6 representative national meteorological stations in the Lijiang River Basin was used as reference data of disaster causing factors; six indicators, i.e., NDVI, river network density, geological hazard, slope, slope aspect and terrain undulation were selected as the sensitivity of disaster-pregnant environment; NPP, potential of farmland production, and population density were taken as the criteria for determining the vulnerability of disaster bearing capacity. Meanwhile, ArcGIS was used for analysis and calculation to complete the risk assessment of flood disaster in Lijiang River Basin, Guangxi. The results indicate that: (1) the hazard level of flood disaster causing factors in Lijiang River Basin shows a decreasing distribution pattern from north to south, and high-risk areas cover 3108.47 km2, accounting for 21.29%; (2) the stability grade of disaster-pregnant environment shows a decreasing trend from the surrounding mountains to the plains, and the low-stability and lower-stability areas are mostly found in the low-lying areas around Lijiang River, with an area of 4218.63 km2, accounting for 28.69%; (3) the vulnerability of the disaster bearing body is generally at a low level, and the areas with high level cover 246.96 km2, accounting for only 1.69%; (4) under the combined effect of the above factors, the northern part of Guilin City in the Lijiang River Basin has a high risk of flood disaster.

Multiscale Flood Disaster Risk Assessment in the Lancang-Mekong River Basin: A Focus on Watershed and Community Levels

Floods are one of the most frequent and costly natural hazards worldwide, causing significant damage to infrastructure, agriculture, and livelihoods. The Lancang-Mekong River is a major river in Southeast Asia, but the basin is prone to flood disasters that may be exacerbated by climate change. Therefore, to better understand disaster risk and tailor disaster risk reduction measures, this study conducted multiscale flood disaster risk assessments at the watershed and community levels using indicator-based and hydrodynamic model-based methods. Both methods adopted open data with the supplement of local survey data. The results of the study showed that the flood risk is generally higher in the lower reach of the river due to high levels of both hazard and vulnerability. However, the community-scale risk assessment revealed that high flood-risk communities exist in low-risk zones, and vice versa, when the flood risk was assessed at the watershed scale. Such phenomena can lead to inadequate community preparedness for flooding or unnecessary allocation of resources for flood mitigation measures. These findings provide valuable insights for the development of disaster risk reduction strategies, policies, and plans based on an understanding of the risks. Furthermore, they offer a basis for prioritizing and targeting resources, particularly in areas with high population density or vulnerable communities.

Urban Flooding Risk Assessment in the Rural-Urban Fringe Based on a Bayesian Classifier

Urban flooding disasters have become increasingly frequent in rural-urban fringes due to rapid urbanization, posing a serious threat to the aquatic environment, life security, and social economy. To address this issue, this study proposes a flood disaster risk assessment framework that integrates a Weighted Naive Bayesian (WNB) classifier and a Complex Network Model (CNM). The WNB is employed to predict risk distribution according to the risk factors and flooding events data, while the CNM is used to analyze the composition and correlation of the risk attributes according to its network topology. The rural-urban fringe in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is used as a case study. The results indicate that approximately half of the rural-urban fringe is at medium flooding risk, while 25.7% of the investigated areas are at high flooding risk. Through driving-factor analysis, the rural-urban fringe of GBA is divided into 12 clusters driven by multiple factors and 3 clusters driven by a single factor. Two types of cluster influenced by multiple factors were identified: one caused by artificial factors such as road density, fractional vegetation cover, and impervious surface percentage, and the other driven by topographic factors, such as elevation, slope, and distance to waterways. Single factor clusters were mainly based on slope and road density. The proposed flood disaster risk assessment framework integrating WNB and CNM provides a valuable tool to identify high-risk areas and driving factors, facilitating better decision-making and planning for disaster prevention and mitigation in rural-urban fringes.

Risk Assessment of Flood Disaster in Sichuan Province Based on GIS

In the context of global climate change, flood disaster has become one of the significant disasters endangering human life. Sichuan Province has more annual precipitation and complex landforms, which suffer from yearly floods. To reveal the spatial distribution and spatiotemporal dynamic change characteristics, we developed a risk assessment model based on geographic information systems and natural disaster risk assessment theory. Considering both natural and human factors, precipitation, terrain, climate type, population, and GDP are selected as evaluation indexes. We Used the expert score-AHP method to obtain the annual flood disaster risk regionalization from 2017 to 2021. In the end, the results were compared with the entropy weight method in the objective weighting method. The results showed that the comprehensive flood disaster risk in Sichuan Province is higher in the east and lower in the west. The flood disaster risk in the eastern hill area, Panxi area, and northeast Sichuan Basin is generally higher. In contrast, that in the west Sichuan Plateau is lower. Based on the evaluation results, it is found that the risk assessment of flood disasters based on expert scoring-AHP method is more in line with the actual situation.

Risk Assessment and Spatial Pattern Analysis of Flood Disaster in Xiantao City in Hubei Province

Risk Assessment and Spatial Pattern Analysis of Flood Disaster in Xiantao City in Hubei Province

Review on Urban Flood Risk Assessment

Under the background of rapid urban development and continuous climate change, frequent floods around the world have caused serious economic losses and social problems, which has become the main reason for the sustainable development of cities. Flood disaster risk assessment is an important non-engineering measure in urban disaster prevention and mitigation, and scientific flood disaster risk assessment is the premise and foundation of flood disaster risk management. This paper summarizes the current situation of flood risk assessment by analyzing the international literature in recent 20 years. The mechanism of flood disaster is mainly discussed. The flood disaster assessment methods are summarized, including historical disaster statistics method, multi-criteria index system method, remote sensing and GIS (Geographic Information System) coupling method, scenario simulation evaluation method and machine learning method. Furthermore, the development status of flood risk analysis and forecasting is summarized. Finally, the development trend and direction of flood risk assessment are put forward.

Risk assessment of urban rainstorm flood disaster based on land use/land cover simulation

AbstractUnder the urbanization context, land use/land cover (LULC) changes rapidly which affects both the probability of urban flood disaster and its consequence. In order to explore the impact of LULC changes on rainstorm flood disaster risk in the future, a framework of rainstorm flood disaster risk assessment is constructed from natural‐social perspective through LULC simulation based on the patch‐generating land use simulation (PLUS) model in this study. Specifically, the spatial LULC under three future development scenarios (inertia development scenario, economic priority scenario and sustainable development scenario) for the year 2030 is simulated, and then assessment model of rainstorm flood disaster risk is constructed through obtaining inundation depth and social vulnerability based on simulated LULC. Jingdezhen City in China is selected as an example. The results show that proposed risk assessment framework based on PLUS model can be used to detect the risk changes in different development scenarios. The risk of urban rainstorm flood disaster under economic priority scenario is most severe and risk under the sustainable development scenario is the lowest under the same return period rainstorm. Under the sustainable development scenario, green land including forest land, grassland and open water increases, which reduces the surface runoff and decrease the risk of rainstorm flood disaster effectively, however the reduction is limited with the greater rainstorm return period. Therefore, adaptive measures should be implemented together with land use planning to prevent and control urban flood risk. This study provides a framework to predict the future risk of urban rainstorm flood under different development scenarios, which can support for flood disaster risk prevention and urban sustainable development.

Flood Disaster Risk Assessment Based on DEA Model in Southeast Asia along “The Belt and Road”

The “Belt and Road” initiative proposed by China has received much attention from the international community. Natural disasters along the route have posed considerable challenges to the “Belt and Road” economic construction. Southeast Asia, as the main thoroughfare of the Maritime Silk Road, always suffers from floods. It is necessary to evaluate flood risk to enhance disaster emergency management. Based on the Data Envelopment Analysis (DEA) model, inputs consist of four factors: the number of deaths, victims, frequency of occurrence, and economic losses caused by meteorological disasters. To study the vulnerability to flood disasters in Southeast Asian countries, the four factors caused by flood disasters were taken as outputs, respectively. The relative efficiency values of Laos, Malaysia and Cambodia exceed 0.8. They are most vulnerable to floods. The following four countries, Thailand, Myanmar, Indonesia, and the Philippines, are also vulnerable to flood disasters. The vulnerability of Vietnam is relatively lower than the others. In brief, the risk of flood disasters in Southeast Asia is high. Risk assessment for Southeast Asia is essential to ensure the implementation of the “Belt and Road” initiative.

A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks.

As one of the most critical elements in the hydrological cycle, real-time and accurate rainfall measurement is of great significance to flood and drought disaster risk assessment and early warning. Using commercial microwave links (CMLs) to conduct rainfall measure is a promising solution due to the advantages of high spatial resolution, low implementation cost, near-surface measurement, and so on. However, because of the temporal and spatial dynamics of rainfall and the atmospheric influence, it is necessary to go through complicated signal processing steps from signal attenuation analysis of a CML to rainfall map. This article first introduces the basic principle and the revolution of CML-based rainfall measurement. Then, the article illustrates different steps of signal process in CML-based rainfall measurement, reviewing the state of the art solutions in each step. In addition, uncertainties and errors involved in each step of signal process as well as their impacts on the accuracy of rainfall measurement are analyzed. Moreover, the article also discusses how machine learning technologies facilitate CML-based rainfall measurement. Additionally, the applications of CML in monitoring phenomena other than rain and the hydrological simulation are summarized. Finally, the challenges and future directions are discussed.

Establishing a risk dynamic evolution model to predict and solve the problem of urban flood disaster.

Under the influence of global climate change and rapid urbanization, urban flood disaster becomes more and more serious. Urban flood disaster risk assessment and risk response strategy have become a hot issue in sponge city research. This study introduce the basic concept of sponge urban construction risk and the basic theory, using the grounded theory to determine the sponge risk factors for urban construction, building based on system dynamics dynamic evolution model of the urban construction risk, sponge city construction in Jiangxi province as the research object, the application of Vensim PLE software carries on the simulation results. This paper analyzes the dynamic development situation and key risk factors of each sub-risk system in the whole life cycle, and discusses what countermeasures should be taken to reduce the risk of sponge city construction.

Risk Assessment and Prediction of Rainstorm and Flood Disaster Based on Henan Province, China

To reasonably evaluate and predict the loss of rainstorm and flood disaster, this study is based on the rainfall data and rainstorm and flood disaster data of 18 cities in Henan Province from 2010 to 2020, using GIS technology and weighted comprehensive evaluation method to analyze the risk of rainstorm and flood disaster factors in various regions. The four risk factors of hazard risk, hazard-pregnant environment sensitivity, hazard-bearing body vulnerability, and disaster resilience were analyzed in compartment analysis. At the same time, a new rainstorm and flood disaster prediction model was constructed in combination with the hybrid PSO-SVR algorithm. The research results show that there are many rivers in Henan Province, the terrain tends to be higher in the west and lower in the east, and most areas are low plains, making most cities in Henan Province at a moderate risk level. For the more developed cities such as Zhengzhou, Luoyang, and Nanyang, the hazard risk, sensitivity, vulnerability, and disaster resistance are high, and they are prone to heavy rains and floods. For the economically underdeveloped, the terrain is high or hills, such as Sanmenxia City; Xinyang City and other places have low hazard risk and are not prone to rainstorms and floods. By constructing a hybrid PSO-SVR model, selecting two representative cities of Zhengzhou and Luoyang, and predicting the daily rainfall, the number of disasters, and the direct economic loss, the calculated RMSE and MAPE values are both less than GA-SVR, the traditional SVR, and BPNN models, which have verified the superiority of the model proposed in this study and the practical value it brings. To further verify the prediction accuracy of the hybrid model, the average value of RMSE and MAPE of other 16 cities are calculated, and the result is still smaller than other three models, and the study can provide some decision-making references for the urban rainstorm and flood management.