Flood Disaster Risk Research Articles

Assessing Flood Risks in Coastal Plain Cities of Zhejiang Province, Southeastern China

Constructing a precise and effective evaluation index system is crucial to flood disaster prevention and management in coastal areas. This study takes Lucheng District, Wenzhou City, Zhejiang Province, southeastern China, as a case study and constructs an evaluation index system comprising three criterion levels: disaster-causing factors, disaster-gestation environments, and disaster-bearing bodies. The weights of each evaluation index are determined by combining the Analytic Hierarchy Process (AHP) and the entropy method. The fuzzy matter-element model is utilized to assess the flood disaster risk in Lucheng District quantitatively. By calculating the correlation degree of each evaluation index, the comprehensive index of flood disaster risk for each street area is obtained, and the flood disaster risk of each street area is classified according to the risk level classification criteria. Furthermore, the distribution of flood disaster risks in Lucheng District under different daily precipitation conditions is analyzed. The results indicate that: (1) the study area falls into the medium-risk category, with relatively low flood risks; (2) varying precipitation conditions will affect the flood resilience of each street in Lucheng District, Wenzhou City. The flood disaster evaluation index system and calculation framework constructed in this study provide significant guidance for flood risk assessment in coastal plain cities.

Hydrological streamflow modeling at the inlet of Gilgel Gibe III dam of Ethiopia using soil and water assessment tool

AbstractComprehensive approach is a pre‐requisite to improve the estimation of variability in streamflow data for designing water supply systems, generating hydropower, determining environmental flows, allocating water, and conducting pollution studies that need a comprehensive approach. There is a need to model streamflow data to develop effective water policy plans when sufficient data are unavailable. This study aims at assessing the accuracy and applicability of the Soil and Water Assessment Tool (SWAT) model in predicting streamflow at the inlet of the Gilgel Gibe III dam watershed in Ethiopia. Historical meteorological data from nine available stations between 1987 and 2017 were used to predict the streamflow. The SWAT model was calibrated and validated for two different time periods (1997–1999 and 2003–2004) using SWATCUP program and SUFI‐2 technique. Calibration and validation were based on monthly observed discharges at the Abelti and Gojeb stations. The results showed acceptable values of R2, Nash–Sutcliffe modeling efficiency (NSE), and percent bias (PBIAS). At the Abelti station and Gojeb station, the calibration and validation results were 0.82, 0.79, and −10.1%; 0.88, 0.84, and −14.4%; and 0.86, 0.79, and 14.1% for calibration and 0.88, 0.8, and 11.1% for validation, respectively. The prediction uncertainty of 95% was consistent with the observed discharge. The mean annual runoff was 440.08 m3/s, demonstrating the model's effectiveness in simulating streamflow at the dam's inlet. The calibrated and validated model can be used to study the effects of climate and land use changes; analyze water quality and sediment yield; and inform future water policy plans, dam construction planning, and flood disaster risk management.

Geospatial analysis of alarmingly increasing flood vulnerability and disaster risk within the northeast himalaya region of India

Geoenvironmetally the eastern part of the Himalaya region is highly vulnerable to flood and other natural disasters as it consists of fragmented and tectonically active geology and geomorphology, very high monsoon rainfall (>360 cm) and subsequent runoff, rugged hilly terrain with high ranges of elevation and slope, dense drainage density etc. Other hand, the unplanned developmental activities keep going and scaling up this vulnerability and risk to floods and other disasters. Addressing this burning issue, a geospatial technology-based case study of the Kohima district, Nagaland state (India), is presented here. The geospatial "technology-based" analyses employed in the study are thematic GIS mapping of flood hazard, vulnerability and risk controlling factors; and performing overlay operation using the AHP model in GIS software to generate spatio-temporal map layers of flood vulnerability and disaster risk. Results reveal that the region is under a high rate of monsoon climate change (increasing temperature, rainfall, rainy days, rainfall events and flood events with an annual rate of 0.35 %, 1.12 %, 0.36 %, 2.67 % and 4 % respectively), land use degradation (increasing built-up area with 0.60 %, annual rate decreasing forest, shrubs and water bodies with 0.80 % accumulated annual rates respectively) and demographic changes (increasing urban as well and rural population density with 0.53–2.10 % and 0.55–2.14 % respectively). Accumulated impacts of climate change, land use degradation and demographic changes causing an increase in flood hazard, vulnerability and disaster risk. Flood hazard zones and vulnerability zones extending with 0.50 % (4.89 km2) and 0.84 % (8.16 km2) annual rates respectively, subsequently the flood risk zones categorized as a moderate, high and very high potential risk, have been spreading out with a yearly rate of 0.07 % (0.65 km2), 0.13 % (1.31 km2) and 0.03 % (0.33 km2) respectively. It decreases the area under low and very low-risk zones by 0.17 % (1.63 km2) and 0.07 % (0.65 km2) annual rates respectively. Following up on these annual rates, the spatial distribution of flood hazard, vulnerability and risk zones for the next decade (2031–2040) have also been projected, revealing alarming situations, if flood disaster risk reduction (F-DRR) measures were not implemented in timely. It is strongly believed that the proposed study will be very useful for district-level planners and administrators to implement sustainable development planning, for the scientific fraternity to enhance their research work in the field of flood disaster management and for individuals for their safety in terms of life and property.

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.

Analysis of Short-Term Heavy Rainfall-Based Urban Flood Disaster Risk Assessment Using Integrated Learning Approach

Accurate and timely risk assessment of short-term rainstorm-type flood disasters is very important for ecological environment protection and sustainable socio-economic development. Given the complexity and variability of different geographical environments and climate conditions, a single machine learning model may lead to overfitting issues in flood disaster assessment, limiting the generalization ability of such models. In order to overcome this challenge, this study proposed a short-term rainstorm flood disaster risk assessment framework under the integrated learning model, which is divided into two stages: The first stage uses microwave remote sensing images to extract flood coverage and establish disaster samples, and integrates multi-source heterogeneous data to build a flood disaster risk assessment index system. The second stage, under the constraints of Whale Optimization Algorithm (WOA), optimizes the integration of random forest (RF), support vector machine (SVM), and logistic regression (LR) base models, and then the WRSL-Short-Term Flood Risk Assessment Model is established. The experimental results show that the Area Under Curve (AUC) accuracy of the WRSL-Short-Term Flood Risk Assessment Model is 89.27%, which is 0.95%, 1.77%, 2.07%, 1.86%, and 0.47% higher than RF, SVM, LR, XGBoost, and average weight RF-SVM-LR, respectively. The accuracy evaluation metrics for accuracy, Recall, and F1 Score have improved by 5.84%, 21.50%, and 11.06%, respectively. In this paper, WRSL-Short-Term Flood Risk Assessment Model is used to carry out the risk assessment of flood and waterlogging disasters in Henan Province, and ArcGIS is used to complete the short-term rainstorm city flood and waterlogging risk map. The research results will provide a scientific assessment basis for short-term rainstorm city flood disaster risk assessment and provide technical support for regional flood control and risk management.

Comprehensive Zoning Strategies for Flood Disasters in China

The frequency of global floods has increased, posing significant threats to economic development and human safety. Existing flood risk zoning studies in disaster prevention lack integration of the natural–economic–social chain and urban resilience factors. This study addresses this gap by constructing flood disaster risk and intensity indices using data from 31 provinces and 295 prefectural-level cities in China from 2011 to 2022. These indices incorporate natural (rainfall), economic (GDP), and social (population, built-up area) indicators to assess the flood likelihood and loss degree, providing comprehensive risk and intensity ratings. The study also examines the impact of resilience factors—environmental (green space), infrastructural (rainwater pipeline density), and natural resource (watershed areas)—on flood intensity. Findings reveal that high-risk regions are mainly in the Yangtze River Basin and southern regions, while high-intensity regions are primarily in the middle and lower Yangtze River and certain northwestern cities. Increasing rainwater pipeline density mitigates flood impacts in high-risk, high-intensity areas, while expanding green spaces and pipelines are effective in high-risk, low-intensity regions. This paper proposes a comprehensive flood hazard zoning mechanism integrating natural, economic, and social factors with urban resilience, offering insights and a scientific basis for urban flood management.

The role of large reservoirs in drought and flood disaster risk mitigation: A case of the Yellow River Basin

The acceleration of water cycle processes in the context of global warming will exacerbate the frequency and intensity of extreme events and predispose to drought and flood disasters (DFD). The Yellow River Basin (YRB) is one of the basins with significant and sensitive impacts of climate change, comprehensive assessment and prediction of its DFD risk are of great significance for ecological protection and high-quality development. This study first constructed an evaluation index system for drought disaster risk and flood disaster risk based on hazard, vulnerability, exposure and the role of large reservoirs. Secondly, the weights of each evaluation index are established by the analytic hierarchy process. Finally, based on the four-factor theory of disasters, an evaluation model of DFD risk indicators is established. The impact of large reservoirs on DFD risk in the YRB is analyzed with emphasis. The results show that from 1990 to 2020, the drought disaster risk in the YRB is mainly distributed in the source area of the Yellow River and the northwest region (11.26–15.79 %), and the flood disaster risk is mainly distributed in the middle and lower reaches (30.04–31.29 %). Compared to scenarios without considering large reservoirs, the area at risk of high drought and high flood is reduced by 45.45 %, 44.22 % and 31.29 % in 2000, 2010 and 2020, respectively. Large reservoirs in the YRB play an important role in mitigating DFD risk, but their role is weakened with the enhancement of the emission scenario. Under the influence of different scenario models, the DFD risk in the YRB in 2030 and 2060 will increase, and the area of high drought and high flood risk in the middle and upper reaches of the basin will increase by 0.26–25.15 %. Therefore, the YRB should play the role of large reservoirs in DFD risk defense in its actions to cope with future climate change, while improving non-engineering measures such as early warning and emergency management systems to mitigate the impacts of disasters.

Increasing the Preparedness through Participatory Action Research in the Implementation of the Disaster Resilient Village Program in Madegondo Village

Climate change is increasing the frequency of floods in Indonesia, thereby triggering the need for effective disaster management down to detailed levels such as the Destana Program. Madegondo Village, Sukoharjo Regency experiences floods every year. It has become the focus of research to improve community preparedness using the Participatory Action Research (PAR) method with Focus Group Discussion (FGD) as the main technique. This research revealed that Madegondo Village is vulnerable to tornadoes, fires, and dengue fever. Risk analysis indicates a moderate level of danger in affecting human, economic, infrastructure, environmental, and socio-political assets. Furthermore, the creation of flood disaster risk maps, the Disaster Risk Reduction Forum, and disaster management plans were also carried out based on community participation. An early warning system was also developed via telephone and WhatsApp based on data from Kaliwingko and Bengawan Solo River.

FLOOD SUSCEPTIBILITY MAPPING USING MACHINE LEARNING IN KENING RIVER, SUB WATERSHED OF BENGAWAN SOLO, TUBAN

Floods are natural occurrences with the potential to cause damage to ecosystems and pose significant threats to human life, resulting in the destruction of property, infrastructure, and socioeconomic challenges. In recent times, flooding in the Sub-Watershed of Bengawan Solo has been linked to the overflowing Kening River in Tuban County. Aim: This study aims to produce a flood susceptibility map to mitigate the frequency of flood occurrences as well as facilitate effective planning for flood disaster risk management. Methodology and results: Flood data is collected from 2016 to 2023 through field surveys, Sentinel-1 satellite imagery, and data from the Development Planning Agency, Tuban County. Integrating remote sensing data from satellite imagery (PlanetScope, Sentinel-2), geographic information systems (GIS), and spatial modeling techniques, a flood susceptibility map is developed for the Kening River catchment. The occurrence of floods in the Kening River area is associated with various factors (11 variables) assessed through the frequency ratio approach, including profil curvature, LS factor, aspect, rainfall, river distance, road distance, building density 100 m, road density 100 m, vegetation type, normalized difference water index (NDWI), and soil adjusted vegetation index (SAVI). The results show flood susceptibility maps utilizing frequency ratio (FR) and convolutional neural network (CNN) techniques. The flood susceptibility map obtained through the CNN method demonstrates a notably high AUC value. The model development generated a validation AUC value of 0.857 for training and 0.856 for testing. Conclusion, significance and impact study: This research provides an understanding into the factors that influence the occurrence of floods in the Kening River catchment area. It also emphasizes the benefit of advanced machine learning approaches in mapping the susceptibility of floods. Furthermore, this study has the potential to be helpful in guiding regional policy decisions and result in enhanced flood risk management measures in Tuban County.

SARIMA Model-Based Monte Carlo Simulation of Option Contract Design for Maize Seasonal Heavy Precipitation in Shenyang

Agricultural production is highly dependent on weather conditions such as temperature, light, water and heat. The huge loss caused by extreme disaster weather makes the global insurance and reinsurance market overwhelmed, and seriously affects the enthusiasm of agricultural investment and the development of agricultural economy. With unique topography and vast territory, China is vulnerable to complex and diverse climate disasters. In recent years, the annual average economic loss caused by extreme weather disasters has reached about 200-300 billion yuan, among which floods caused by extreme heavy rainfall are the main agricultural disasters in China. In order to enhance the "farmer - insurance company - government" interest association to resist the risk of flood disaster caused by extreme heavy rainfall, financial hedging derivatives of weather disaster risk have gradually become a new hedging tool besides traditional insurance and reinsurance. This paper takes Shenyang, the main grain producing area in northeast China, as the sample of the study area, and uses the SARIMA model to obtain the distribution characteristics of seasonal rainfall time series. Different seasonal rainfall index option products (call option and put option contracts) are designed respectively, and the final option pricing is obtained by Monte Carlo stochastic simulation. The seasonal rainfall call option and put option contracts have opened up a new hedging model of agricultural extreme weather catastrophe outside the insurance and reinsurance market, which has enriched the varieties of weather financial derivatives market in China, reduced the severe impact of extreme weather disasters on agriculture, and enhanced the ability of agricultural stakeholders to resist the risk of extreme weather disasters.

Community Participation in Reducing Flood Disaster Risk in Boyantongo Village, South Parigi Sub-district, Parigi Moutong District

Boyantongo Village is one of the villages in the South Parigi Sub-district, Parigi Moutong Regency, which has experienced flooding several times during the rainy season. Flash floods that hit Boyantongo Village occurred on August 25, 2012, causing 142 residents of Boyantongo Village to flee Flash floods in Boyantongo Village occurred again on July 11-13, 2020 flash floods occurred at 21.00 WITA causing 18 Boyantongo Village residents to flee. The purpose of this study was to determine the level of community participation in reducing the risk of flood disasters in Boyantongo Village. The study used a sample survey method, survey research is one research approach that is generally used for extensive data collection. At the same time, the data collection techniques used in this study used instruments in the form of questionnaires. The research was conducted by distributing questionnaires to 67 households. The results showed that the potential for flooding is in Hamlet I, Hamlet III, and Hamlet IV. Aspects of community understanding of basic disaster knowledge, from Hamlet I is categorized as quite good, Hamlet III is categorized as good and Hamlet is categorized as quite good. Community knowledge in disaster mitigation during floods is categorized as low. It shows that the community's knowledge of disasters is not good, as seen from the parameters of knowledge and attitudes towards disaster risk. This is because the community has not participated in socialization and training on community participation in reducing the risk of flooding in the village.

Investigating the influence of residents' attitudes, perceptions of risk, and subjective norms on their willingness to engage in flood prevention efforts in Freetown, Sierra Leone

This study investigates the intricate relationships between flood disaster risk perception (RP), attitudes(ATT), subjective norms(SN), and participation in flood prevention activities(IPF), emphasizing the mediating roles of self-efficacy(SE) and bonding social capital(BSC). The research in Freetown, Sierra Leone, involved 702 participants, providing a diverse socio-demographic snapshot crucial for understanding community-based flood risk management. Data analysis revealed that demographic factors like age, gender, education, and income significantly influence flood risk perceptions and mitigation behaviors. Additionally, subjective norms were found to substantially impact both self-efficacy and bonding social capital, affecting participation in flood prevention activities. The study also explored the role of community dynamics and social norms in shaping risk perceptions and intentions to engage in flood risk prevention. It was observed that higher self-efficacy and more robust community bonds lead to increased participation in flood mitigation efforts. The findings offer valuable insights into flood prevention behavior's psychological and social drivers and highlight the importance of community-focused strategies in enhancing flood resilience. The study contributes to the broader understanding of flood risk management in urban settings, particularly in developing countries, and underscores the need for policies and practices that foster individual empowerment and collective community action.

Flood Disaster Risk Management In Manado City

Geological and hydrometeorological disasters are disasters that are often experienced by every region in Indonesia. Hydrometeorological disasters such as floods are influenced by strong westerly winds and world climate change. Apart from that, inappropriate land conversion is also one of the factors causing this disaster. One of the regions in Indonesia that is highly vulnerable to flood disasters is the city of Manado. Geographically, Manado City is surrounded by mountains and 5 large rivers that pass-through Manado City, namely the Tikala river and the Tondano river which merge in the Paal 2 area, the Malalayang river, the Sario River and the Bailang River. To overcome this disaster, it is necessary to carry out appropriate mitigation efforts to minimize the impact caused by the flood disaster in Manado City. The aim of this research is to analyze the risk level of flood disasters in Manado City and provide appropriate recommendations related to flood disaster mitigation in Manado City. This research uses analysis with quantitative descriptive research methods. The results of this research are that there are 3 classes of flood disaster risk levels in Manado City. In the high class there are 53 subdistricts, medium 2 subdistricts and low 32 subdistricts. Policy recommendations are made based on the level of risk by taking into account the main causal factors. Furthermore, it is recommended in every sub-district.

Jayapura Regency Government's Strategy in Flood Disaster Management in Sentani District

Sentani District in Jayapura Regency often faces floods that have a significant impact on people's lives, infrastructure, and the environment. High rainfall intensity, geographical conditions, and suboptimal spatial management are the main factors causing flooding in this region. The floods that occurred not only damaged people's homes, but also disrupted economic and social activities, and caused huge losses to the community and local government. The aim of this research is to analyze and formulate appropriate strategies to be used by the Jayapura Regency Government in managing flood disasters, especially in the Sentani District, in order to find out appropriate steps that can be implemented in resolving the problems of flood disasters that frequently occur. The research method used is descriptive qualitative through an inductive approach. Apart from that, the author's data sources are people, places, and documents, with data collection techniques using interviews, observation, and documentation. The theory used by researchers is strategic theory using SOAR analysis. This research produced 5 (five) strategies for flood management. By implementing the right strategy and good collaboration between stakeholders, it is hoped that it can reduce the risk and impact of flood disasters and increase community resilience in facing these disasters.

UJI VALIDITAS IBFWS (IMPACT BASE FORECAST AND WARNING SERVICES) DALAM MEMPREDIKSIKAN WILAYAH YANG BERPOTENSI TERDAMPAK BANJIR DI KALIMANTAN SELATAN

South Kalimantan is one of the provinces in Indonesia which has a high risk of flood disasters seen from its physical condition, which geographically is mostly located below sea level. In 2021, 11 out of a total of 13 districts and cities in South Kalimantan were affected by flooding on a large scale, which caused tens of thousands of residents to suffer losses and even lost family members. IBFWS (Impact Base Forecast and Warning Services) is present as the latest innovation from the Meteorology, Climatology and Geophysics Agency as an impact-based weather forecast information service. To determine the accuracy of the IBFWS service information, it is necessary to validate the prediction results issued by IBFWS with the actual situation. By using spatial analysis methods, it is hoped that it can describe which areas are potentially affected by flooding and continued with the calculation of the contingency table so that the accuracy value of IBFWS is obtained in predicting areas potentially affected by flooding in South Kalimantan. IBFWS validation results in predicting areas potentially affected by flooding in South Kalimantan during the rainy season range between 0.85 – 1.00 which mean 85% - 100% the IBFWS prediction results are correct, during the transition season in general is 0.77 which mean 77% the IBFWS prediction results are correct, and during the dry season it ranges between 0.92 – 1.00 which mean 92% - 100% the IBFWS prediction results are correct.

Institutional vulnerability to urban flood risk management: A case study of the 7·20 flooding disaster in Zhengzhou, China

AbstractThe ‘vulnerability’ approach highlights institutional factors as the primary drivers of flooding risk. However, the intricate connections between institutional vulnerability and urban flood disaster risk have not been extensively explored until now. This study aims to address this gap by examining the case of the 7·20 heavy rainfall disaster in Zhengzhou. Employing a hybrid analysis methodology that integrates text coding, fault tree analysis and analytic hierarchy process methods, the study seeks to identify and assess the distinct contributions of various institutional factors and their interplay, culminating in ineffective urban flood risk management. In contrast to previous research findings that emphasize the determinant role of policy‐making and organizational coordination, this study demonstrates that inadequate legislative compliance constitutes the root cause determining the ineffectiveness of urban flood risk management. It is further exacerbated by insufficient policy attention, resulting in inadequate allocation of resources that ensure the legislative quality, risk coping skills and knowledge and stakeholders' coordination and collaboration. By emphasizing the significance of legislative compliance and policy attention, the study offers a fresh conceptual perspective to understand the factors influencing the efficiency of urban flood risk management. It provides valuable insights to develop targeted countermeasures for mitigating similar urban flood risks in the future.

Eco-friendly dredging methods of changing fluvial landforms for enhancing hydraulic habitat quality and river corridor continuum

Hydraulic habitat connectivity, including the longitudinal continuum respect and lateral flood pulse, is critical for fish survival and organism dispersal. Inappropriate and excessive dredging for prevent flooding may harm river ecosystems. The main objective of this study is to evaluate whether eco-friendly dredging presented by changing local river landforms incorporating the concept of nature-based solutions could grow fish habitat quality for improving river continuity and achieving flood control effects. By combining various mathematical models and empirical formulas and verifying them with the data obtained through field surveys, we explore the interconnections of hydrology, river morphology, and the habitat dynamics of four endemic fishes in an alluvial river. The relationship between habitat structure, flood risk, and river topography, flow discharge was presented as the reference for developing the proper river dredging approaches. The results reveal that the primary habitat defects were lack of high-quality habitat, unsatisfied habitat diversity, deficiency in refugia, and disconnectivity. Longitudinal disconnectivity was induced due to shallow water depth, while lateral disconnectivity is primarily caused by fast flow velocity, suggesting different and specific dredging methods were instructed. We recommend that the corresponding eco-friendly dredging schemes for longitudinal and lateral suitable habitat linkages increase fish habitat quality and river corridor continuity. The win-win strategy for enhancing the connection between suitable habitats sustains a more beneficial aquatic corridor and simultaneously achieves alluvial flood disaster risk reduction.

Pemanfaatan Sig Untuk Untuk Mengurangi Resiko Bencana Banjir Di Kota Demak

The application of Geographic Information Systems (SIG) has become an important tool in combating the risk of flood disasters in many areas, including Demak City. This research aims to explore and evaluate the possible benefits of implementing SIG to reduce the danger of flooding in Demak City. The study approaches used include stakeholder interviews, field surveys, and geographic data analysis. The research results show how useful SIG is for creating early warning systems, identifying air flow patterns, and mapping areas vulnerable to flooding. The use of SIG also enables improved coordination of flood mitigation and management among various interconnected entities. This study makes a significant contribution to our understanding of the function of geospatial information technology in disaster management and provides useful insights for stakeholders to create more effective methods for mitigating flood hazards in Demak City.

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.

Forecasting of Flood in the Non-Tidal River of Northern Regions of Bangladesh Using Machine Learning-Based Approach

Forecasting of Flood in the Non-Tidal River of Northern Regions of Bangladesh Using Machine Learning-Based Approach