Flood Risk Research Articles

Analisis Tingkat Kerawanan Banjir di Sub Daerah Aliran Sungai (DAS) Kampar Kiri di Kecamatan Kampar Kiri

This study aims to describe and analyze the flood vulnerability level in the Kampar Kiri Sub-Watershed, Kampar Kiri District, as well as the extent of flood-prone areas impacting settlements in the region. The primary focus of this research is to understand the flood risk of the area and its effects on the existing settlements. The method applied in this study is Multi-Criteria Evaluation (MCE) using a Geographic Information System (GIS) approach. MCE is used to determine the weights of parameters affecting flood vulnerability and to select appropriate methods for analysis. This approach has proven effective and efficient in flood risk analysis, as demonstrated in previous studies. The parameters analyzed include slope, elevation, rainfall, soil type, land use, and distance from rivers, which are calculated using scoring methods and overlaid to produce a flood vulnerability map. The results indicate that the flood vulnerability level in the Kampar Kiri Sub-Watershed is categorized into three classes: not vulnerable, vulnerable, and highly vulnerable. The areas for each class are: not vulnerable with 16,854 hectares, vulnerable with 58,543 hectares, and highly vulnerable with 11,506 hectares. The extent of settlements in the vulnerability zones includes: vulnerable zone with an area of 277 hectares (approximately 20.83%), and highly vulnerable zone with an area of 1,054 hectares (approximately 79.17%). Based on these findings, it is recommended that the Kampar Kiri District government enhance land use planning to reduce flood risks, collaborate with the Kampar Public Works and Spatial Planning Office, and improve infrastructure such as drainage channels and flood protection embankments.

SAR-driven flood inventory and multi-factor ensemble susceptibility modelling using machine learning frameworks

Climate change has substantially increased both the occurrence and intensity of flood events, particularly in the Indian subcontinent, exacerbating threats to human populations and economic infrastructure. The present research employed novel ML models—LR, SVM, RF, XGBoost, DNN, and Stacking Ensemble—developed in the Python environment and leveraged 18 flood-influencing factors to delineate flood-prone areas with precision. A comprehensive flood inventory, obtained from Sentinel-1 Synthetic Aperture Radar (SAR) data using the Google Earth Engine (GEE) platform, provided empirical data for entire model training and validation. Model performance was assessed using precision, recall, F1-score, accuracy, and ROC-AUC metrics. The results highlighted Stacking Ensemble’s superior predictive ability (0.965), followed closely by, XGBoost (0.934), DNN (0.929), RF (0.925), LR (0.921), and SVM (0.920) respectively, establishing the feasibility of ML applications in disaster management. The maps depicting susceptibility to flooding generated by the current research provide actionable insights for decision-makers, city planners, and authorities responsible for disaster management, guiding infrastructural and community resilience enhancements against flood risks.

Cataloging and Testing Flood Risk Management Measures to Increase the Resilience of Critical Infrastructure Networks

Cataloging and Testing Flood Risk Management Measures to Increase the Resilience of Critical Infrastructure Networks

Multi-Source Remote Sensing Data for Wetland Information Extraction: A Case Study of the Nanweng River National Wetland Reserve

Wetlands play a vital role in regulating the global carbon cycle, providing biodiversity, and reducing flood risks. These functions maintain ecological balance and ensure human well-being. Timely, accurate monitoring of wetlands is essential, not only for conservation efforts, but also for achieving Sustainable Development Goals (SDGs). In this study, we combined Sentinel-1/2 images, terrain data, and field observation data collected in 2020 to better understand wetland distribution. A total of 22 feature variables were extracted from multi-source data, including spectral bands, spectral indices (especially red edge indices), terrain features, and radar features. To avoid high correlations between variables and reduce data redundancy, we selected a subset of features based on recursive feature elimination (RFE) and Pearson correlation analysis methods. We adopted the random forest (RF) method to construct six wetland delineation schemes and incorporated multiple types of characteristic variables. These variables were based on remote sensing image pixels and objects. Combining red-edge features, terrain data, and radar data significantly improved the accuracy of land cover information extracted in low-mountain and hilly areas. Moreover, the accuracy of object-oriented schemes surpassed that of pixel-level methods when applied to wetland classification. Among the three pixel-based schemes, the addition of terrain and radar data increased the overall classification accuracy by 7.26%. In the object-based schemes, the inclusion of radar and terrain data improved classification accuracy by 4.34%. The object-based classification method achieved the best results for swamps, water bodies, and built-up land, with relative accuracies of 96.00%, 90.91%, and 96.67%, respectively. Even higher accuracies were observed in the pixel-based schemes for marshes, forests, and bare land, with relative accuracies of 98.67%, 97.53%, and 80.00%, respectively. This study’s methodology can provide valuable reference information for wetland data extraction research and can be applied to a wide range of future research studies.

Exploring a GIS-based analytic hierarchy process for spatial flood risk assessment in Egypt: a case study of the Damietta branch

Floods are the most common and costly disasters worldwide, while spatial flood risk assessment is still challenging due to fewer observations and method limitations. In this study, the flood risk zonation in the Nile districts of the Damietta branch, Egypt, is delineated and assessed by integrating remote sensing with a geographic information system, and an analytical hierarchy process (AHP). Twelve thematic layers (elevation, slope, normalized difference vegetation index, topographic wetness index, modified normalized difference water index, topographic positioning index, stream power index, modified Fournier index, drainage density, distance to the river, sediment transport index, and lithology) are used for producing flood susceptibility zonation (FSZ) and six parameters (total population, distance to hospital, land use/land cover, population density, road density, and distance to road) are utilized for producing flood vulnerability zonation. Multicollinearity analysis is applied to identify highly correlated independent variables. Sensitivity studies have been used to assess the effectiveness of the AHP model. The results indicate that the high and very high flood risk classes cover 21.40% and 8.26% of the area, respectively. In 14.07%, 27.01%, and 29.26% of the research area, respectively, flood risk zones classified as very low, low, and moderate are found. Finally, FSZ is validated using the receiver operating characteristics curve and area under curve (AUC) analysis. A higher AUC value (0.741) in the validation findings demonstrated the validity of this AHP approach. The results of this study will help planners, hydrologists, and managers of water resources manage areas that are susceptible to flooding and reduce potential harm.

Selecting facility location of Gendarmerie Search and Rescue (GSR) Units; An analysis of efficiency in disaster response

Disasters, referred to as events that result in physical, economic, and social losses for individuals and disrupt the daily activities of human communities, necessitate ongoing preparedness due to their unpredictable nature. Swift response during and after a disaster is crucial for preserving human life. Hence, it is imperative to initiate planning immediately following a disaster to ensure readiness for various tasks. Given these factors, search and rescue units must carefully select a base location that enables them to promptly reach affected areas.Disasters exhibit unique characteristics across different regions of Türkiye. While some regions are prone to earthquakes, others face the risks of landslides, avalanches, or floods. Consequently, the required measures for disaster management vary from region to region. Nevertheless, when the term “disaster” is mentioned in Türkiye, earthquakes often come to mind due to their frequent occurrence and significant impact. The Gendarmerie Search and Rescue (GSR) units have been actively responding to these earthquakes, renowned for their exemplary institutional discipline and working methods. This study aims to examine the operations and deployment locations of GSR units, which play a crucial role in mitigating the impact of frequent earthquakes in Türkiye, utilizing a SWOT analysis. Additionally, a Multi-Criteria Decision Making-based mathematical model will be employed to optimize task activities and to select the most suitable facility locations for GSR units. The use of mathematical modeling in this context ensures that GSR units are strategically positioned to maximize their operational effectiveness and minimize response times. The results will be evaluated through sensitivity analysis.

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.

Flood risk assessment using modern models integrated with GIS for susceptibility mapping of the M'zab Valley watershed (Algeria)

ABSTRACT Comprehensive flood control is needed because flooding can be catastrophic for people's lives and ways of living. This management must provide information on the hydrologic, geotechnical, environmental, social, and economic elements of floods. This work created maps for the flood susceptibility of the M'zab Valley using logistic regression (LR) and frequency ratio (FR). The main objective of the research was to strengthen the bivariate probability capabilities of FR and LR. A flood inventory map was created by extracting flood sites from multiple sources. The flood inventory was divided randomly into two parts: 30% was used for validation and the remaining 70% was used to train the models. Independent variable datasets included the distance from the river, rainfall, soil type, land use/land cover (LULC), elevation, drainage density, flow accumulation, and slope. The effect of each variable on flooding was assessed by comparing each independent variable with the dependent flood layer. The flood susceptibility map was assessed using the prediction rate approach on the validation dataset, which was not used for model construction. The accuracy assessment's findings revealed a prediction rate of FR 0.86 and LR 0.881, as well as a success rate of FR 0.924 and LR 0.924.

Disaster risk reduction measures and farmers choices: a discrete choice experiment in Uganda

ABSTRACT Climate change induces high and erratic rainfall which triggers landslides and floods. With the increasing population and food needs, households in mountainous, densely populated areas turn fragile ecosystems into farms. This exacerbates landslide and flood risks requiring Disaster Risk Reduction (DRR) measures. Tree planting and diversion channels are among the recommended measures for farmers but their adoption remains low. Current studies assessing barriers to adoption ignore farmers’ opinions regarding the kind of trees or diversion channels preferred. We apply a Discrete Choice Experiment to evaluate how information delivered through videos impacts preferences for the DRR measures. Plot-level data were collected from 319 farmers from Kasese, Bundibugyo, Bushenyi and Buhweju in Uganda – districts prone to landslides and floods. The mixed logit model reveals a general preference for risk-reducing attributes of DRR measures. Using the conditional logit model to analyze split samples reveals that information influences preferences for tree planting, while preferences for diversion channels were hardly changed. Plot characteristics did not strongly explain the differences in preferences. Our study indicates that information specific to DRR measures in extension programmes would increase the adoption of such measures.

Community Participation in Flood Management Systems in Aceh Utara Regency

Flooding remains a persistent challenge in Aceh Utara Regency, representing a complex, cross-sectoral issue. If not addressed through collaboration among various stakeholders, including community groups, it has the potential to lead to new societal problems. This research seeks to assess the effectiveness and impact of using a participatory model in flood management efforts in the region. A qualitative approach was employed, gathering data through in-depth interviews with stakeholders such as local communities, government authorities, and non-governmental organizations involved in flood mitigation programs. The findings emphasize the urgency of adopting a participatory model to enhance the effectiveness of flood management systems. Actively engaging the community in planning, execution, and evaluation processes leads to a deeper understanding of local conditions, needs, and suitable solutions. Key aspects of the participatory model include raising public awareness of flood risks and mitigation measures, improving flood management infrastructure based on community input, creating more accessible and effective evacuation plans, and strengthening the community’s capacity for emergency response and simple mitigation efforts. In conclusion, this study aims to further explore the relevance and effectiveness of the participatory model in flood management in Aceh Utara Regency, ensuring the long-term sustainability of the region's disaster management system.

Evaluation of Potential Flood Areas in the Basin of Lake Ladik through AHP and GIS Integration, (Samsun, Türkiye)

Floods are a problem of many countries on a global scale. In Türkiye, especially in the summer months, large floods occur in the Black Sea Region coastal belt. The number and the destructive power of experienced floods are increasing day by day. It is not possible to prevent the occurrence of floods. But it is possible to take the necessary measures to prevent it from turning into a disaster. Recently, susceptibility analyses have been carried out for floods and similar natural disasters and the results help decision-makers. Within the scope of this study, flood susceptibility analysis was carried out by using multiple geographical factors together in the Basin of Lake Ladik of Samsun Province in the Black Sea Region. In the study, Analytical Hierarchy Process (AHP), Geographic Information Systems (GIS) and Remote Sensing (RS) techniques were used, which are multi-criteria decision-making methods. In this context, flood susceptibility analysis of the basin was carried out by using nine (9) different geographical factors (slope, aspect, lithology, soil, basin size, land use, landforms, precipitation, and drainage density). As a result of the study, for floods, there were identified four (4) different levels as low, medium, high, and very high; and 36.77% of the basin was found to be low, 30.03% was medium, 11.43% was high and 21.77% was found to be sensitive to possible floods at a very high level. The results of the study are also important for decision-makers to make flood risk planning.

Geospatial analysis and AHP for flood risk mapping in Quetta, Pakistan: a tool for disaster management and mitigation

The 2022 flood events in Quetta, Pakistan, caused severe damage to the economy, properties, and lives. Therefore, flood risk mapping to identify flood-prone areas is essential for planners and decision-makers to take critical protective measures to control the effects of flooding. This study focuses on mapping flood-prone regions in the Quetta district of Pakistan using an analytical hierarchy process (AHP) and a geographic information system (GIS). The factors influencing flood used in the present study were topographic witness index (TWI), elevation, slope, land use, land cover, precipitation, stream distance, drainage density, and soil type. Weights and ranks were allocated separately to all factors through AHP and were interpreted in a GIS environment. The produced flood hazard model of the study area depicted four zones. These zones ranged from low (19.49%), moderate (43.34%), high (28.30%), to very high (8.87%). The model was further validated through previous flood events in the study area. Around 90% of flood hazard events in the past took place mainly in the produced model's very high and high zones, which is why the current model is reliable. Finally, integrating geospatial approaches with AHP in flood hazard mapping is a quick, reliable, and affordable method that may be utilized in the area.

Flood risk projection in Iran using CMIP6 models and frequency analysis of precipitation

Flood risk projection in Iran using CMIP6 models and frequency analysis of precipitation

Understanding bi-modal seas, shingle beach response and flood risk at Hayling island, UK

Beach management through nourishment and annual recycling has been applied for over 37 years to manage flood and coastal erosion risk to 1700 properties at Eastoke, Hayling Island, UK. This form of natural flood risk management has proved successful in avoiding the annual flooding that blighted this area previously. However there have been unexpected storm events which have caused beach erosion and localised flooding. Using long-term nearshore wave datasets and state-of-the-art statistical methods, new multivariate extreme wave conditions were derived. The conditions were applied to simulate beach profile response, estimated bimodal overtopping rates and resulting flood inundation, then validated against recent storm events. The Eastoke beach was found to meet its original design criteria of a 0.5% AEP standard of protection for unimodal wave conditions. However, it experiences greater roll back erosion, wave overwash and therefore flood inundation under certain bimodal wave conditions, causing uncertainty around the future standard of protection.It remains unclear if the unexpected beach response at Eastoke resulted from increased storminess due to climate change, additional loading by mixed sea/swell events, increasing bimodal events, or the assumptions within the original bivariate analysis, leading to incorrect AEP attribution.Communicating these issues to practitioners and a non-specialist audience is challenging as we tend to oversimplify, despite every beach and storm being unique. Better understanding of mixed beaches both in situ and through parametric and numerical models would reduce uncertainty, to ensure communities are resilient to climate change.

FACTORS AFFECTING INDONESIAN PADDY HARVEST FAILURE: A COMPARISON OF BETA REGRESSION, QUASI-BINOMIAL REGRESSION, AND BETA MIXED MODELS

The Paddy harvest failure rate is one of the key aspects in determining the total number of claims in a crop insurance policy. It is also an important factor indicating the fulfillment of targeted total production. Therefore, we proposed Beta Regression, Quasi Binomial Regression, and Beta Mixed Models which can be used to analyze significant variables affecting paddy harvest failure rates. Model selection and evaluations indicated that the Nested Beta Mixed Model is the best. Previous research has shown four significant fixed effect variables: drought, flood, pests, and disease risks. Pests and other types of risks also affect the variability of loss rate. All variables have positive effects, indicating higher values cause a higher possibility of a higher average harvest failure rate. High variability was shown for province, municipality, and farmers' random effects. Hence, to prevent a more significant loss rate, MoA should consider more intensive and innovative participatory activities in farmer groups to enhance good farming practices, especially for farmers who suffer from certain risks. These activities should also consider the local characteristics of each province or municipality. As for AUTP development and improvement, farmers with lower failure risks could be given a discounted premium to make it more appealing.

The Coupled Application of the DB-IWHR Model and the MIKE 21 Model for the Assessment of Dam Failure Risk

The phenomenon of global climate change has led to an increase in the frequency of extreme precipitation events, an acceleration in the melting of glaciers and snow cover, and an elevation of the risk of flooding. In this study, the DB-IWHR model was employed in conjunction with the MIKE 21 hydrodynamic model to develop a simulation system for the dam failure flow process of an earth and rock dam. The study concentrated on the KET reservoir, and 12 dam failure scenarios were devised based on varying design flood criteria. The impact of reservoir failures on flood-risk areas was subjected to detailed analysis, with consideration given to a range of potential failure scenarios and flood sizes. It was determined that under identical inflow frequency conditions, the higher the water level, the more rapid the breakout process and the corresponding increase in flood peak discharge. Conversely, for a given frequency of incoming water, an elevated water level results in a transient breach process, accompanied by a reduction in flood peak flow. Moreover, for a given water level, an increase in water frequency results in a reduction in breaching time, an extension of flood duration, and an increase in flood peak flow. The observed trend of flood spreading is generally north-south, and this process is highly compatible with the topographic and geomorphological features, demonstrating good adaptability.

Assessment of Climate Change Impact on Flood Hazard Zones

AbstractThere have been many destructive pluvial and fluvial floods in Poland and the projection of increasing flood hazards in the future is a reason of considerable concern. The maps of river hazard zones are changing over time, and understanding these changes is of primary importance for flood risk reduction and climate change adaptation. This article aims to assess the impact of climate change on the spatial extent and depth classes of flood hazard zones for a selected reach of the River Warta in the western part of Poland. To this end, we integrated the Soil & Water Assessment Tool (SWAT) hydrological model of the Warta River Basin with the 1D hydraulic model HEC-RAS of the selected reach. The climate change effect was quantified based on the coupled model simulations forced with bias-corrected projections from the EURO-CORDEX project. Flood hazard maps were developed for two townships along the River Warta (Oborniki and Wronki), three greenhouse gas concentration scenarios (one for the baseline scenario in the reference period, 1971–2000; one for RCP 4.5 and one for RCP 8.5, for the time horizon 2021–2050) and for three return periods (10-, 100- and 500-year floods). Based on the ensemble mean, the increase in the flooded area projected in the future is more pronounced for RCP8.5 than for RCP4.5. This unique combination of software and data enabled the transformation of climate change impact into the land surface part of the hydrological cycle and assessment of changes in flood hazard and opens the way to assess the potential increases in the economic losses in the future.

Geospatial Mapping and Meteorological Flood Risk Assessment: A Global Research Trend Analysis.

Flooding is a global threat causing significant economic and environmental damage, necessitating a policy response and collaborative strategy. This study assessed global research trends and advances in geospatial and meteorological flood risk assessment (G_MFRA), considering the ongoing debate on flood risk management and adaptation strategies. A total of 1872 original articles were downloaded in BibTex format using the Web of Science (WOS) and Scopus databases to retrieve G_MFRA studies published from 1985 to 2023. The annual growth rate of 15.48% implies that the field of G_MFRA has been increasing over time during the study period. The analysis of global trends in flood risk research and practice highlights the key themes, methodologies, and emerging directions. There exists a notable gap in data and methodologies for flood risk assessment studies between developed and developing countries, particularly in Africa and South America, highlighting the urgency of coordinated research efforts and cohesive policy actions. The challenges identified in the body of extant literature include technical expertise, complex communication networks, and resource constraints associated with the application gaps of the study methodologies. This study advocates for a holistic research approach to flood disaster management through ecosystem-based adaptation that underpins the Sustainable Development Goals to develop innovative flood techniques and models with the potential to influence global decision-making in the G_MFRA domain. Addressing these global challenges requires a networked partnership between the research community, institutions, and countries.

Influence of Rainfall Patterns on Rainfall–Runoff Processes: Indices for the Quantification of Temporal Distribution of Rainfall

To understand the influence of rainfall patterns on rainfall–runoff processes, we propose two indices: skewnessPEAK (skewp), representing the relative timing of peak rainfall, and the normalized root-mean-square error peak (NRMSEp), which quantifies the concentration of rainfall near the peak. By analyzing approximately 25,000 rainfall scenarios, we examined the relationship between these indices and peak flood discharge in the rainfall–runoff process. The analysis revealed that peak flood discharge positively correlates with the NRMSEp, indicating that concentrated rainfall near the peak substantially increases discharge. Conversely, a negative correlation with skewp suggests that earlier peak rainfall reduces discharge. These insights were synthesized into a three-dimensional solution space providing a comprehensive framework for predicting how variations in rainfall distribution affect flood discharge. The findings underscore the importance of incorporating these indices into real-time flood forecasting models and urban flood risk management strategies.

Wastewater flooding risk assessment for coastal Communities: Compound impacts of climate change and population growth

The study introduces a wastewater modeling framework that evaluates the compound impacts of intense rainfall, groundwater infiltration, sewer aging based roughness and population growth on wastewater systems. It integrates property and city-level flooding risk assessment and wastewater treatment plant (WWTP) capacity analysis into a single methodological approach. The framework applied to the coastal city of Charlottetown, whose population increased from 30,887 in 1981 to 42,440 in 2023, but the wastewater system did not expand accordingly, resulting in frequent sewer backups, street and basement flooding, as witnessed during the extreme wet weather event of September 2, 2021. Using PCSWMM (Personal Computer Storm Water Management Model), the study assessed that the city-wide wastewater flooding risk in Charlottetown, based on 2023 population data and historical IDF curves, affects 13.31% of the network during a 2-year storm and 18.38% during a 100-year storm. These risks increase to 14.5% and 22.6% under future IDF scenarios, reaching 17.89% and 26.4% by 2060 with projected population growth. The WWTP capacity is exceeded by 27.8% during peak wet weather flows from a 2-year storm and by 86.3% during a 100-year storm, based on 2023 population and historic IDFs. Under future IDF scenarios for 2060 population, exceedances rise to 103.6% and 169.1% respectively, for a 2-year and 100-year storm. Basement flooding risk affects 13.35% of basements during a 2-year storm and 18.31% during a 100-year storm, for 2023 population and historic IDFs. Future IDF scenarios indicate risk increasing to 17.77% and 25.80% by 2060 for a 2-year and 100-year storm respectively. The hydraulic modeling results indicate that GWI is not currently impacting the study area, nor is it expected to in near future, because the groundwater table is over 10 m deep, while wastewater pipes are no deeper than 6 m. The framework and study have significant social implications and benefits, including protecting public health, enhancing the resilience of urban infrastructure, and safeguarding the environment, ultimately improving the quality of life for residents in coastal communities like Charlottetown.