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Mapping areas affected by flooding requires an efficient and an effective mapping methods because the flood detection methods provided diverse results and bias. A reliable comparison technique between one method and other methods is needed in this case. Furthermore, in-depth analysis regarding the use of data in detecting flood-affected areas also needs to be carried out to improve the performance of flood detection methods. This research aims to compare the application of the machine learning methods, namely random forest (RF), classification and regression tree (CART), and support vector machine (SVM), to detect flood-affected areas. Sentinel-1 Synthetic Aperture Radar (SAR) Ground Range Detected (GRD) data are used in this research through monotemporal and multitemporal approaches. Furthermore, this research also accommodates the use of band combinations as input data. Therefore, this research can provide a highly comparable comparison between machine learning methods. Data from Official Statistics, FloodScan flood-affected area maps, and online news are used as validation data for the detection results of flood-affected areas. The research results show that RF has higher performance than CART and SVM, with an F1-score of 91.54%. Compared to monotemporal data, the use of multitemporal data in flood-affected area detection is proven to increase the performance of the RF, CART, and SVM models, respectively, by 5.20%, 6.34%, and 5.96% on average. The utilization of Sentinel-1 band combinations for machine learning offers an alternative for developing flood detection models. This research is useful for the government to formulate policies related to flood disasters, especially in disaster risk assessment strategies, the distribution of aid, and food security. [ABSTRACT FROM AUTHOR]

Floods can cause extensive damage proportional to their magnitude, depending on the watershed hydrology and terrain characteristics. Flood studies generally assume bathymetry as steady, while in reality it is constantly changing due to sediment transport. This study seeks to quantify the impact of different lake bathymetry conditions on flood dynamics. The Hydrotel and Telemac2D models are used to simulate floods for a lake with bathymetries from multiple year surveys. The bathymetries differ in bed elevation due to sediment accumulation and/or remobilisation. Results show that bathymetric differences produce a more noticeable effect for moderate flows than for maximum flows. During moderate flows, shallower bathymetries induce higher water levels and larger water extents. For peak flows, differences in water levels and extent are practically negligible for the different bathymetries tested. Higher water levels during moderate flows could produce longer flooding times and affect the community's perception of flood impacts. [ABSTRACT FROM AUTHOR]

Introduction: Global coastal flooding maps are now achieving a level of detail suitable for local applications. The resolution of these maps, derived from widely available open data sources, is approaching that of local flooding maps (0.5–100 m), increasing the need for a standardized approach to evaluate underlying assumptions and indicators for local applications. Methods: This study introduces the Waterlevel, Elevation, Protection, Flood, Impact, Future (WEPFIF) notation, a structured notation for documenting and comparing key methodological choices and data variations across global coastal flooding studies. This approach enhances the understanding and explanation of the fitness-for- purpose of flood maps. This notation builds on commonly used methodological choices, dataset variations, and model approaches in global flooding risk research. Analysis of these workflows identifies common elements and highlights the need for a more structured reporting approach to improve comparability. Results: Applying the WEPFIF notation to a case study in the Netherlands reveals significant variations in flood risk assessments originating from differences in Digital Elevation Model (DEM) and water level selection, and inclusion of protective infrastructure. Discussion: WEPFIF, by annotating these methodological variations, enables more informed comparisons between local and global flood studies. This allows researchers and practitioners to select appropriate data and models, based on their specific research objectives. The study proposes tailored approaches for three common types of flood studies: raising concern, optimizing flood protection investments, and representing the state of coastal risk. [ABSTRACT FROM AUTHOR]

The current study focuses on the devastating floods along the Kosi River in Bihar in July 2020, which were recorded by Sentinel-1A satellite images. Three flood-affected districts in Saharsa, Supaul, and Madhepura have been selected for this study. Pre- and post-flood images from the Sentinel-1A satellite were utilized to generate the flood-inundated map, and pre-flood Landsat-8 datasets were used to generate the land use and land cover maps. Finally, damage assessment was done by superimposing land use and land cover on a flood-inundated map. The results showed that Madhepura was the most affected district with 57.6% of its barren land experiencing inundation. Furthermore, a comparison of cloud cover percentages during the flood was undertaken using the Sentinel-1A, Sentinel-2B, and Landsat-8 datasets. The findings indicate Sentinel-1A has less than 1% cloud cover, making it suitable for flood monitoring. Due to urbanization and industrialization, land use/cover (LULC) varies rapidly, and the effect on the flood plain must be determined. TerrSet's Land Change Modeler uses cellular automata–Markov modeling to project LULC maps for 2030 and their implications for subsequent flooding. The findings indicate that Saharsa will see an increase in the built-up area of 2.7%, Supaul with a gain of 13.4%, and Madhepura will see a growth of 10.3%. The growth of built-up regions in the near future will encourage greater impermeable layers and more discharge. Therefore, land use planners, environmentalists, and lawmakers should consider these LULC changes in water resource planning. [ABSTRACT FROM AUTHOR]

Flooding is one of the most severe natural hazards, causing widespread environmental, economic, and social disruption. If not managed properly, it can lead to human losses, property damage, and the destruction of livelihoods. The ability to rapidly assess such damages is crucial for emergency management. Near Real-Time (NRT) spatial information on flood-affected areas, obtained via remote sensing, is essential for disaster response, relief, urban and industrial reconstruction, insurance services, and damage assessment. Numerous flood mapping methods have been proposed, each with distinct strengths and limitations. Among the most widely used are machine learning algorithms and spectral indices, though these methods often face challenges, particularly in threshold selection for spectral indices and the sampling process for supervised classification. This study aims to develop an NRT flood mapping approach using supervised classification based on spectral features. The method automatically generates training samples through masks derived from spectral indices. More specifically, this study uses FWEI, NDVI, NDBI, and BSI indices to extract training samples for water/flood, vegetation, built-up areas, and soil, respectively. The Otsu thresholding technique is applied to create the spectral masks. Land cover classification is then performed using the Random Forest algorithm with the automatically generated training samples. The final flood map is obtained by subtracting the pre-flood water class from the post-flood image. The proposed method is implemented using optical satellite images from Sentinel-2, Landsat-8, and Landsat-9. The proposed method's accuracy is rigorously evaluated and compared with those obtained from spectral indices and machine learning techniques. The suggested approach achieves the highest overall accuracy (OA) of 90.57% and a Kappa Coefficient (KC) of 0.89, surpassing SVM (OA: 90.04%, KC: 0.88), Decision Trees (OA: 88.64%, KC: 0.87), and spectral indices like AWEI (OA: 84.12%, KC: 0.82), FWEI (OA: 88.23%, KC: 0.86), NDWI (OA: 85.78%, KC: 0.84), and MNDWI (OA: 87.67%, KC: 0.85). These results underscore the superior accuracy and effectiveness of the proposed approach for NRT flood detection and monitoring using multi-sensor optical imagery. [ABSTRACT FROM AUTHOR]

Floods stand out as one of the most expensive natural calamities, causing harm to both lives and properties for millions of people globally. The increasing frequency and intensity of flooding underscores the need for accurate and timely flood mapping methodologies to enhance disaster preparedness and response. Earth observation data obtained through satellites offer comprehensive and recurring perspectives of areas that may be prone to flooding. This paper shows the suitability of high-resolution PlanetScope imagery as an efficient and accessible approach for flood mapping through a case study in South Chickamauga Creek (SCC), Chattanooga, Tennessee, focusing on a significant flooding event in 2020. The extent of the flood water was delineated and mapped using image classification and density slicing of Normalized Difference Water Index (NDWI). The obtained results indicate that PlanetScope imagery performed well in flood mapping for a narrow creek like SCC, achieving an overall accuracy of more than 90% and a Kappa coefficient of over 0.80. The findings of this research contribute to a better understanding of the flood event in Chattanooga and demonstrate that PlanetScope imagery can be utilized as a very useful resource for accurate and timely flood mapping of streams with narrow widths. [ABSTRACT FROM AUTHOR]

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The current study assesses the performance of several machine learning (ML) and deep learning (DL) models for detecting and mapping floods using Sentinel-1 SAR imagery. Three distinct approaches were used: pixel classification, object-based image analysis and object instance segmentation. The ML models are Random Forest, and Support Vector Machine and the DL models are U-NET, DeepLabV3 and Mask RCNN. Five different case studies were selected to test the models' scalability. These areas are in Romania (Prut River, at the border between Romania, the Republic of Moldova and Ukraine, Timiș River, and Râul Negru River), the United States of America (Missouri River) and Australia (Broughton Creek). For each flood, five Sentinel-1 images were used, four collected before the flood and one collected after the flood. The intensity images were stacked and scaled in the range of the intensity thresholds associated with water and non-water so that all the case studies have the same margins for intensity. Samples of water, vegetation, agricultural fields, and bare soil were collected only from the Prut River case study and used in the training process. Out of all models, the U-Net model returned the highest accuracy with a value for Intersect over Union of 0.763 for a tile size of 128x128 pixels. [ABSTRACT FROM AUTHOR]

As one of the most powerful natural catastrophes, floods pose serious risks to people’s lives, the integrity of infrastructure, and agricultural landscapes, which increases the toll they take on the economy and society. As a result, it becomes essential to continuously monitor these areas of vulnerability in order to support effective disaster response and mitigation efforts. Accurately defining the extent of floods is a problem for traditional flood mapping approaches, which emphasizes the vital need for modern technologies such as Synthetic Aperture Radar (SAR) imaging. Additionally, there is a need to develop computer-aided tools specifically designed for automatically identifying areas that are vulnerable to flooding using SAR data. Nonetheless, the lack of consistent large datasets presents a barrier that prevents these algorithms from progressing and being used in real-world scenarios. For this reason, the present study aims to develop a semi-supervised semantic segmentation algorithm for accurate flood region delineation in SAR data. In particular, the paper proposes labeling unannotated instances of data using a pseudo-label generation strategy. In order to accomplish this, the study suggests using a self-supervised trained teacher model to generate pseudo-labels and speed up the training procedure. The teacher model is then trained with a student model to efficiently extract features from the labeled data. Furthermore, the study presents a new semantic segmentation technique that uses convolutional neural networks to automatically identify flooded areas in SAR images. A comprehensive assessment conducted on publicly available datasets produces promising results. These results confirm the usefulness and possible relevance of the suggested methodology in enhancing efforts related to flood zone identification and management.

This study aims to evaluate the efficacy of Google Earth Engine (GEE) in mapping floods and their aftermath, focusing on the recent event caused by cloud burst rainfall and glacial lake outburst flood (GLOF) of Lhonak glacier lake in the Teesta River basin, North Sikkim. The objective is to utilize GEE, coupled with Sentinel-1 Synthetic Aperture Radar (SAR) data and Landsat 9 imagery, for precise remote sensing analysis, flood mapping, and Land Use and Land Cover (LULC) classification. The study employs a comprehensive methodology within the GEE platform, involving the acquisition and preprocessing of Sentinel-1 SAR data to create pre- and post-flood images. The difference between these images is calculated to generate flood maps at five-day intervals, providing a temporal evolution of the flood extent. Additionally, LULC mapping is conducted using Landsat 9 data, contributing to an understanding of pre-flood landscape characteristics. The results and discussion reveal significant impacts on various LULC types, with barren rocks, dense and medium forests, settlements, and agricultural lands experiencing notable effects. This research not only enhances our understanding of GLOFs but also serves as a critical tool for informing disaster management strategies, emphasizing the importance of accurate hazard assessment and the need for holistic approaches to mitigate the cascading effects of such events.

The frequent occurrence of El Niño events, in the context of climate change, brings heavy precipitation and extreme heat, severely disrupting agricultural production. Previous efforts have focused on monitoring crop planting areas and evaluating affected crops during disasters. Nevertheless, a comprehensive analysis, including crop planting area mapping, crop damage assessment, and mitigation effectiveness throughout the entire course of a disaster, has been seldom addressed. In this study, we built a comprehensive framework to rapidly investigate the areas of early rice, the extent of flooding impacts, and the post-flood mitigations of early rice during the El Niño flooding event in a typical rice production region – Jiangxi Province in 2023. Early rice planting areas were first mapped by integrating 15-day time series gap-filled Sentinel-1/2 datasets using the Google Earth Engine (GEE) platform, based on a random forest classifier built with the 55 optimized training features. Then the flood-affected early rice map was produced by integrating the early rice planting areas and the Sentinel-1 images-based flood map. Finally, the post-flood newly planted rice fields were identified using the random forest algorithm and classification features from the Sentinel-1/2 images composited during four phenology phases of newly planted rice. The results showed the early rice planting area map, the flooding map, and the newly planted early rice map have overall accuracies of over 90 %. The early rice planting areas reached 120 × 104 ha, and an area of 3.60 × 104 ha (3 %) was flooded due to the heavy rain, and 3.43 × 104 ha flooded areas were newly planted, eventually mitigating the flooding impacts on the production of early rice. This study showcases the potential of all the available Sentinel-1/2 data, cloud computing, and well-established mapping algorithms for tracking rice areas, flooding impacts, and mitigations (i.e., after-flooding replanting) during extreme climate events. The established framework is expected to serve as an early warning system for agricultural adaptation to extreme climate events. [ABSTRACT FROM AUTHOR]

Accurate extraction of Seasonally Inundated Area (SIA) is pivotal for precise delineation of Flood Inundation Area (FIA). Current methods predominantly rely on Water Inundation Frequency (WIF) to extract SIA, which, due to the lack of analysis of dynamic surface water changes, often yields less accurate and robust results. This significantly hampers the rapid and precise mapping of FIA. In the study, based on the Harmonic Models constructed from Long Time-series Surface Water (LTSW) dynamics, an SIA extraction approach (SHM) was introduced to enhance their accuracy and robustness, thereby improving flood mapping. The experiments were conducted in Poyang Lake, a region characterized by active hydrological phenomena. Sentinel-1/2 remote sensing data were utilized to extract LTSW. Harmonic analysis was applied to the LTSW dataset, using the amplitude terms in the harmonic model to characterise the frequency of variation between land and water for the surface units, thus extracting the SIAs. The results reveal that the harmonic model parameters are capable of portraying SIA. In comparison to the commonly used WIF thresholding method for SIA extraction, the SHM approach demonstrates superior accuracy and robustness. Leveraging the SIA extracted through SHM, a higher level of accuracy in FIA extraction is achieved. Overall, the SHM offers notable advantages, including high accuracy, automation, and robustness. It offers reliable reference water extents for flood mapping, especially in areas with active and complex hydrological dynamics. SHM can play a crucial role in emergency response to flood disasters, providing essential technical support for natural disaster management and related departments. [ABSTRACT FROM AUTHOR]

A flood susceptibility assessment is crucial for identifying areas that are susceptible to flooding. This task usually uses models, but prior flood susceptibility assessment models focused on the frequency or duration of floods, not both. Integrating the frequency and duration of floods in susceptibility assessment could provide a more accurate picture of flood susceptibility. This study aimed to utilise and assess a novel integrated model that considers the frequency and duration of floods to categorise vulnerability/susceptibility zones. This study focuses on the multi-hazard zone between Cuddalore and Sirkazhi on the east coast of Tamil Nadu, India. Sentinel-1 A and RISAT-1 A Synthetic Aperture Radar (SAR) images were analysed using the Classification and Regression Tree (CART) classifier. Eight SAR images were used to study the persistence and temporal evolution of flooding over 49 days in 2015, along with multi-temporal datasets for 2015, 2018, and 2019. The classification of flood-susceptibility zones based on the frequency and duration of flooding yielded an accuracy of 0.87, whereas the integrated model scored 0.96 in all matrices. The hybrid integrated analysis provided a comprehensive understanding of the area's flooding system, identifying the southern part of the study area as the most susceptible. The proposed model recommends a frequency-duration-based approach to demarcate flood susceptibility zones and potentially improve flood susceptibility assessments and management strategies. [ABSTRACT FROM AUTHOR]

This study leverages a Random Forest model to predict flood hazard in Arizona, New Mexico, Colorado, and Utah, focusing on enhancing sustainability in flood management. Utilizing the National Flood Hazard Layer (NFHL), an intricate flood map of Arizona was generated, with the Random Forest Classification algorithm assessing flood hazard for each grid cell. Weather variable predictions from TerraClimate were integrated with NFHL classifications and Digital Elevation Model (DEM) analyses, providing a comprehensive understanding of flood dynamics. The research highlights the model's capability to predict flood hazard in areas lacking NFHL classifications, thereby supporting sustainable flood management by elucidating weather's influence on flood hazard. This approach aligns with sustainable development goals by aiding in resilient infrastructure design and informed urban planning, reducing the impact of floods on communities. Despite recognizing constraints such as input data precision and the model's potential limitations in capturing complex variable interactions, the methodology offers a robust framework for flood hazard evaluation in other regions. Integrating diverse data sources, this study presents a valuable tool for decision-makers, supporting sustainable practices, and enhancing the resilience of vulnerable regions against flood hazards. This integrated approach underscores the potential of advanced modeling techniques in promoting sustainability in environmental hazard management. [ABSTRACT FROM AUTHOR]

Floods often occur due to river water overflowing to the surface. Flooding can also be caused by land development which increases surface runoff and impacts the drainage system. This can cause a decrease in the performance of the drainage channel. This research aims to map floods caused by runoff from the Gadjah Putih River, and the performance of existing drainage channels in RW 14 Sumber, Banjarsari, Surakarta. The rain data used is rain data from the Pabelan, Mojolaban, and Baki rain stations from 2000 to 2019. The Gumbel method is used to analyze rain return periods, runoff discharge analyzed by using Rational Method. Runoff discharge is explained at return periods of 2, 5, and 10 years, producing discharges of 8.15 m³/s respectively; 9.76 m³/day; and 10.61 m³/day. Floods are mapped using HEC-RAS by conducting steady flow analysis. The results of the flood mapping of the Gadjah Putih River showed that the area that experienced flooding at the return period of 2, 5 and 10 years respectively was 50,326.77 m²; 56,865.37 m²; 60,756.82 m². Drainage performance analysis compares hydrology and hydraulic calculations. The analysis resulted drainage performance in RW 14 Sumber show that at the 2 year discharge period, 34 types of channels are safe, and 11 types of channels require repair; in the 5 year discharge period, 30 types of channels are safe, and 15 types of channels require repair; at the 10 year discharge period, 25 types of channels are safe, and 20 types of channels require repair. [ABSTRACT FROM AUTHOR]

Floods are among the most devastating natural disasters, posing significant risks to life, property, and infrastructure globally. Earth observation satellites provide data for continuous and extensive flood monitoring, yet limitations exist in the spatial completeness of monitoring using optical images due to cloud cover. Recent studies have developed gap-filling methods for reconstructing cloud-covered areas in water maps. However, these methods are not tailored for and validated in cloudy and rainy flooding scenarios with rapid water extent changes and limited clear-sky observations, leaving room for further improvements. This study investigated and developed a novel reconstruction method for time series flood extent mapping, supporting spatially seamless monitoring of flood extents. The proposed method first identified surface water from time series images using a fine-tuned large foundation model. Then, the cloud-covered areas in the water maps were reconstructed, adhering to the introduced submaximal stability assumption, on the basis of the prior water occurrence data in the Global Surface Water dataset. The reconstructed time series water maps were refined through spatiotemporal Markov random field modeling for the final delineation of flooding areas. The effectiveness of the proposed method was evaluated with Harmonized Landsat and Sentinel-2 datasets under varying cloud cover conditions, enabling seamless flood mapping at 2–3-day frequency and 30 m resolution. Experiments at four global sites confirmed the superiority of the proposed method. It achieved higher reconstruction accuracy with average F1-scores of 0.931 during floods and 0.903 before/after floods, outperforming the typical gap-filling method with average F1-scores of 0.871 and 0.772, respectively. Additionally, the maximum flood extent maps and flood duration maps, which were composed on the basis of the reconstructed water maps, were more accurate than those using the original cloud-contaminated water maps. The benefits of synthetic aperture radar images (e.g., Sentinel-1) for enhancing flood mapping under cloud cover conditions were also discussed. The method proposed in this paper provided an effective way for flood monitoring in cloudy and rainy scenarios, supporting emergency response and disaster management. The code and datasets used in this study have been made available online (https://github.com/dr-lizhiwei/SeamlessFloodMapper). [ABSTRACT FROM AUTHOR]

Extreme meteorological events hit our planet with increasing frequency, resulting in an ever-increasing number of natural disasters. Flash floods generated by intense and violent rains are among the most dangerous natural disasters that compromise crops and cause serious damage to infrastructure and human lives. In the case of such a kind of disastrous events, timely and accurate information about the location and extent of the affected areas can be crucial to better plan and implement recovery and containment interventions. Satellite systems may efficiently provide such information at different spatial/temporal resolutions. Several authors have developed satellite techniques to detect and map inundated areas using both Synthetic Aperture Radar (SAR) and a new generation of high-resolution optical data but with some accuracy limits, mostly due to the use of fixed thresholds to discriminate between the inundated and unaffected areas. In this paper, the RST-FLOOD fully automatic technique, which does not suffer from the aforementioned limitation, has been exported for the first time to the mid–high-spatial resolution (20 m) optical data provided by the Copernicus Sentinel-2 Multi-Spectral Instrument (MSI). The technique was originally designed for and successfully applied to Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) satellite data at a mid–low spatial resolution (from 1000 to 375 m). The processing chain was implemented in a completely automatic mode within the Google Earth Engine (GEE) platform to study the recent strong flood event that occurred in May 2023 in Emilia Romagna (Italy). The outgoing results were compared with those obtained through the implementation of an existing independent optical-based technique and the products provided by the official Copernicus Emergency Management Service (CEMS), which is responsible for releasing information during crisis events. The comparisons carried out show that RST-FLOOD is a simple implementation technique able to retrieve more sensitive and effective information than the other optical-based methodology analyzed here and with an accuracy better than the one offered by the CEMS products with a significantly reduced delivery time. [ABSTRACT FROM AUTHOR]

Floods often cause significant damage to transportation infrastructure such as roads, railways, and bridges. This study identifies several topographic, environmental, and hydrological factors (slope, elevation, rainfall, land use and cover, distance from rivers, geology, topographic wetness index, and drainage density) influencing the safety of the railway infrastructure and uses multi-criteria analysis (MCA) alongside an analytical hierarchy process (AHP) to produce flood susceptibility maps within a geographic information system (GIS). The proposed methodology was applied to the catchment area of a railway track in southern Italy that was heavily affected by a destructive flood that occurred in the autumn of 2015. Two susceptibility maps were obtained, one based on static geophysical factors and another including triggering rainfall (dynamic). The results showed that large portions of the railway line are in a very highly susceptible zone. The flood susceptibility maps were found to be in good agreement with the post-disaster flood-induced infrastructural damage recorded along the railway, whilst the official inundation maps from competent authorities fail to supply information about flooding occurring along secondary tributaries and from direct rainfall. The reliable identification of sites susceptible to floods and damage may provide railway and environmental authorities with useful information for preparing disaster management action plans, risk analysis, and targeted infrastructure maintenance/monitoring programs, improving the resilience capacity of the railway network. The proposed approach may offer railway authorities a cost-effective strategy for rapidly screening flood susceptibility at regional/national levels and could also be applied to other types of linear transport infrastructures. [ABSTRACT FROM AUTHOR]

Flooding is a widespread and costly natural disaster around the world. Accurately assessing the extent of flooding in near real-time is crucial for governments and humanitarian organizations. This information strengthens early warning systems, evaluates risks, and guides effective relief efforts. Therefore, precise flood mapping is essential for saving lives through improved early warning systems and targeted emergency responses. In this study, radar imagery available on the Planetary Computer Data was utilized to train a U-Net model specifically designed to label flood-affected pixels in an image from a flood event. Different blocks of the U-Net encoder architecture were fine-tuned to identify the most efficient fine-tuned model, and their results were compared. As a result, the model with blocks 1 and 2 being fine-tuned demonstrated the highest Intersection over Union (IoU) score of 78.904%, an increase of 8.663% over the baseline methods. ABSTRAK: Banjir merupakan bencana alam yang meluas dan mahal di seluruh dunia. Penilaian yang tepat terhadap skala banjir secara hampir masa nyata adalah penting bagi kerajaan dan organisasi kemanusiaan. Maklumat ini memperkukuhkan sistem amaran awal, menilai risiko, dan membimbing usaha bantuan yang lebih berkesan. Oleh itu, pemetaan banjir yang tepat adalah penting untuk menyelamatkan nyawa melalui sistem amaran awal yang lebih baik dan respons kecemasan yang disasarkan. Dalam kajian ini, imej radar yang tersedia pada Planetary Computer Data digunakan untuk melatih model U-Net yang direka khas untuk melabelkan piksel yang terjejas oleh banjir dalam imej daripada kejadian banjir. Bagi mengenal pasti model ditala-halus yang paling cekap, blok-blok berlainan dalam arkitektur pengekod U-Net telah ditala-halus, dan hasilnya dibandingkan. Hasilnya, model dengan blok 1 dan 2 yang ditala-halus menunjukkan skor Intersection over Union (IoU) tertinggi sebanyak 78.904%, iaitu peningkatan sebanyak 8.663% berbanding kaedah asas.

IntroductionGlobal coastal flooding maps are now achieving a level of detail suitable for local applications. The resolution of these maps, derived from widely available open data sources, is approaching that of local flooding maps (0.5–100 m), increasing the need for a standardized approach to evaluate underlying assumptions and indicators for local applications.MethodsThis study introduces the Waterlevel, Elevation, Protection, Flood, Impact, Future (WEPFIF) notation, a structured notation for documenting and comparing key methodological choices and data variations across global coastal flooding studies. This approach enhances the understanding and explanation of the fitness-for- purpose of flood maps. This notation builds on commonly used methodological choices, dataset variations, and model approaches in global flooding risk research. Analysis of these workflows identifies common elements and highlights the need for a more structured reporting approach to improve comparability.ResultsApplying the WEPFIF notation to a case study in the Netherlands reveals significant variations in flood risk assessments originating from differences in Digital Elevation Model (DEM) and water level selection, and inclusion of protective infrastructure.DiscussionWEPFIF, by annotating these methodological variations, enables more informed comparisons between local and global flood studies. This allows researchers and practitioners to select appropriate data and models, based on their specific research objectives. The study proposes tailored approaches for three common types of flood studies: raising concern, optimizing flood protection investments, and representing the state of coastal risk.

The systematic mapping of flood events with Synthetic Aperture Radar (SAR) data is an area of growing importance. One global flood mapping algorithm utilized within the Copernicus Emergency Management Service is based upon a Bayesian Inference model that compares a SAR image to a simulated reference image representing no-flood conditions. This no-flood reference image is at present generated using a harmonic model trained using historic time series, thereby producing a backscatter image representing mean seasonal conditions. One known weakness of this approach is that it cannot account for changing environmental conditions from year to year, potentially causing an overestimation of flood extent during dry periods, snow and frost, or other effects causing lower-than normal backscatter. To minimize this detrimental effect, we introduce an exponential filter to estimate the no-flood reference image by weighting the most recent backscatter observations according to their time difference to the current SAR acquisition. We compare the performance of the new exponential filter model against the harmonic model using a novel time-series flood mapping assessment approach. First, we assess their predictions against the actual SAR image time series for the year 2023. Then, we analyze the false positive rate of the corresponding flood maps generated to ensure the robustness of the automated algorithm outside of flood events. Furthermore, we perform qualitative and quantitative analyses of flood maps matching with semi-automatic results from Copernicus Emergency Management Services and Sentinel Asia as a reference. Our time-series analysis confirms increased false positive rates due to well-known environmental drivers and highlights issues with agricultural overestimation. In this regard, the time-series comparisons of the no-flood reference models show a clear improvement in the TU Wien algorithm with the exponential filter, effectively reducing false positive rates on non-flooded scenes in most study sites. The exponential filter performed better than the harmonic model in most flooded scenes, where sites show generally improved Critical Success Index and User’s accuracy. However, the exponential filter model has difficulties with sites with prolonged floods in the time series, requiring further development. Overall, the exponential filter no-flood reference model shows great promise for improved global near-real-time flood mapping.

Owing to the vast development of Synthetic Aperture Radar (SAR), especially the improvement of spatio-temporal resolution, observing and quantifying the complex and dynamic flood process becomes increasingly feasible. Utilizing the Sentinel-1 Ground Range Detected (GRD) dataset, we proposed an improved probabilistic flood mapping method combining image Pareto Scaling (PS) normalization and Bayesian probability estimation. We validated our method during a flood event in Xinjiang County, Shaanxi Province of China in October 2021 using a high spatial resolution (0.1 m) Unmanned Aerial Vehicle (UAV) image. The overall reliability of the new method agrees 95% to the UAV measurements and achieves the highest accuracy (85.2%) when compared to the Sentinel-1 dual-polarized water index (SDWI) threshold method and the Z-score method. Our results distinguished four flood stages: flood emergence, peak, receding, and disappearance, which provide valuable insights into the dynamic change process of floods. Notably, we observed that pixels with different flood probabilities exhibited distinct temporal characteristics. The extremely high probability pixel experienced rapid fluctuations, while the medium probability pixel showed more gradual changes over time. We believe our proposed method can enhance our understanding of flood-prone areas and their dynamics so that decision-makers can develop targeted mitigation measures and response plans.

Floods pose a recurrent and devastating natural disaster in India, and accurate and timely assessment of their extent is crucial for effective disaster management and mitigation efforts. In this regard, this study proposes a novel approach for flood inundation estimation through the statistical analysis of two key geospatial datasets: the Normalized Difference Flood Index (NDFI) and temporal Standard Deviation images. To establish a baseline under ordinary conditions, two products were derived. The first product is the mean image during the non-flood season, and the second is the temporal Standard Deviation image for the entire year. These images are derived from Sentinel-1A Synthetic Aperture Radar (SAR) data using Google Earth Engine (GEE). The NDFI is calculated by comparing the mean image to the image obtained during flood event, allowing the identification of flood-affected areas. The process involves constructing Kernel Density Estimation (KDE) plots for NDFI and Standard Deviation, from which a 95% density ellipse is generated using the covariance matrix and eigenvalues. Multiple SAR scenes from diverse regions in India are analyzed individually, yielding density ellipses for each location. The innovative step lies in synthesizing these individual ellipses into the best fit ellipse through the convex hull method. This best fit ellipse encapsulates the general flood characteristics observed across disparate geographic regions, providing a unified representation. The derived best fit ellipse serves as a powerful tool for generating near-real-time flood inundation maps, with an overall accuracy of over 98.2%. Thus, the fusion of statistical insights and historical SAR information enables rapid near-real-time flood mapping, with enhanced accuracy of flood extent predictions.

The current study assesses the performance of several machine learning (ML) and deep learning (DL) models for detecting and mapping floods using Sentinel-1 SAR imagery. Three distinct approaches were used: pixel classification, object-based image analysis and object instance segmentation. The ML models are Random Forest, and Support Vector Machine and the DL models are U-NET, DeepLabV3 and Mask RCNN. Five different case studies were selected to test the models’ scalability. These areas are in Romania (Prut River, at the border between Romania, the Republic of Moldova and Ukraine, Timiș River, and Râul Negru River), the United States of America (Missouri River) and Australia (Broughton Creek). For each flood, five Sentinel-1 images were used, four collected before the flood and one collected after the flood. The intensity images were stacked and scaled in the range of the intensity thresholds associated with water and non-water so that all the case studies have the same margins for intensity. Samples of water, vegetation, agricultural fields, and bare soil were collected only from the Prut River case study and used in the training process. Out of all models, the U-Net model returned the highest accuracy with a value for Intersect over Union of 0.763 for a tile size of 128x128 pixels.

Synthetic aperture radar (SAR) technology has become an important means of flood monitoring because of its large coverage, repeated observation, and all-weather and all-time working capabilities. The commonly used thresholding and change detection methods in emergency monitoring can quickly and simply detect floods. However, these methods still have some problems: (1) thresholding methods are easily affected by low backscattering regions and speckle noise; (2) changes from multi-temporal information include urban renewal and seasonal variation, reducing the precision of flood monitoring. To solve these problems, this paper presents a new flood mapping framework that combines semi-automatic thresholding and change detection. First, multiple lines across land and water are drawn manually, and their local optimal thresholds are calculated automatically along these lines from two ends towards the middle. Using the average of these thresholds, the low backscattering regions are extracted to generate a preliminary inundation map. Then, the neighborhood-based change detection method combined with entropy thresholding is adopted to detect the changed areas. Finally, pixels in both the low backscattering regions and the changed regions are marked as inundated terrain. Two flood datasets, one from Sentinel-1 in the Wharfe and Ouse River basin and another from GF-3 in Chaohu are chosen to verify the effectiveness and practicality of the proposed method. [ABSTRACT FROM AUTHOR]

Global flood mapping has developed rapidly over the past decade, but previous approaches have limited scope, function, and accuracy. These limitations restrict the applicability and fundamental science questions that can be answered with existing model frameworks. Harnessing recently available data and modeling methods, this paper presents a new global ∼30 m resolution Global Flood Map (GFM) with complete coverage of fluvial, pluvial, and coastal perils, for any return period or climate scenario, including accounting for uncertainty. With an extensive compilation of global benchmark case studies—ranging from locally collected event water levels, to national inventories of engineering flood maps—we execute a comprehensive validation of the new GFM. For flood extent comparisons, we demonstrate that the GFM achieves a critical success index of ∼0.75. In the more discriminatory tests of flood water levels, the GFM deviates from observations by ∼0.6 m on average. Results indicating this level of global model fidelity are unprecedented in the literature. With an optimistic scenario of future warming (SSP1‐2.6), we show end‐of‐century global flood hazard (average annual inundation volume) increases are limited to 9% (likely range ‐6%–29%); this is within the likely climatological uncertainty of −8%–12% in the current hazard estimate. In contrast, pessimistic scenario (SSP5‐8.5) hazard changes emerge from the background noise in the 2040s, rising to a 49% (likely range of 7%–109%) increase by 2100. This work verifies the fitness‐for‐purpose of this new‐generation GFM for impact analyses with a variety of beneficial applications across policymaking, planning, and commercial risk assessment. Plain Language Summary: Computer models use a variety of data and physical equations to estimate the extent and depth of possible flood events. Global applications of these tools have been developed over the past decade, but they are not very good at simulating the behavior of real floods. In this paper, we address some key problems to make a global model that does a lot better than past ones. We apply new techniques to better understand how much water we need to put into the model for a given flood probability. This movement of water is simulated by the model over a more accurate map of the Earth's terrain than has been available previously, with river channels represented in a smarter way. We look at the projected changes in rainfall, river discharge, and sea levels for given levels of warming simulated by available climate models and adjust the probabilities of a given magnitude flood accordingly. The model results suggest that the effect of future climate change might be small relative to our ability to understand flood hazards today, but this depends heavily on how much carbon we emit in the coming decades. Key Points: New climate‐conditioned model framework represents fluvial, pluvial, and coastal flood hazards at high‐resolution globallyComprehensive validation studies suggest that the model is approaching local model skill in many casesEmissions reduction can hold flood hazards largely constant this century, though coastal flooding will increase drastically regardless [ABSTRACT FROM AUTHOR]

Topo‐bathymetric LiDAR (TBL) can provide a continuous digital elevation model (DEM) for terrestrial and submerged portions of rivers. This very high horizontal spatial resolution and high vertical accuracy data can be promising for flood plain mapping using hydrodynamic models. Despite the increasing number of papers regarding the use of TBL in fluvial environments, its usefulness for flood mapping remains to be demonstrated. This review of real‐world experiments focusses on three research questions related to the relevance of TBL in hydrodynamic modelling for flood mapping at local and regional scales: (i) Is the accuracy of TBL sufficient? (ii) What environmental and technical conditions can optimise the quality of acquisition? (iii) Is it possible to predict which rivers would be good candidates for TBL acquisition? With a root mean square error (RMSE) of 0.16 m, results from real‐world experiments confirm that TBL provides the required vertical accuracy for hydrodynamic modelling. Our review highlighted that environmental conditions, such as turbidity, overhanging vegetation or riverbed morphology, may prove to be limiting factors in the signal's capacity to reach the riverbed. A few avenues have been identified for considering whether TBL acquisition would be appropriate for a specific river. Thresholds should be determined using geometric or morphological criteria, such as rivers with steep slopes, steep riverbanks, and rivers too narrow or with complex morphologies, to avoid compromising the quality or the extent of the coverage. Based on this review, it appears that TBL acquisition conditions for hydrodynamic modelling for flood mapping should optimise the signal's ability to reach the riverbed. However, further research is needed to determine the percentage of coverage required for the use of TBL as a source of bathymetry in a hydrodynamic model, and whether specific river sections must be covered to ensure model performance for flood mapping. [ABSTRACT FROM AUTHOR]

Floods are a major contributor to deaths, infrastructure destruction, and serious economic impact to a country. Floods, as inevitable natural disasters, necessitate proactive measures from government bodies, international organizations, and the public to mitigate their impact. Synthetic Aperture Radar (SAR) operating in the microwave spectrum is crucial for flood detection in adverse weather conditions or regions with frequent cloud cover. This research paper explores the integration of SAR and Optical data's complementary roles in flood mapping, emphasizing their applicability to hazard analysis, flood risk identification, and prediction. Recognizing the challenges in flood prediction, the limitations of individual technologies are acknowledged. To address these gaps, a flood predictions and scope mapping model is proposed, leveraging the strengths of SAR and Optical data for a more accurate assessment of flood scenarios. This comprehensive approach underscores the importance of technology and collaboration in effective flood management. The study aims to enhance the timeliness, accuracy, and reliability of flood detection methodologies through the synergistic use of the two remote sensing technologies. [ABSTRACT FROM AUTHOR]

Flood mapping is a crucial tool for assisting urban planning and emergency response plans and, consequently, preventing or reducing the risks associated with flood disasters. However, in developing countries that often lack or have limited data, to produce such maps is a challenging task. When topographic data are lacking, digital elevation models (DEMs) derived from the Shuttle Radar Topography Mission (SRTM) are frequently used as a freely available surrogate, albeit with additional uncertainty. This work presents an integrated framework to investigate flood inundation areas using a Bayesian approach, while including steps for calibrating SRTM data and determining the river bathymetry below the WSE. A flood event in the Itaqui municipality, in the state of Rio Grande do Sul, southern Brazil is used to demonstrate the proposed framework. Findings suggest benefits in using calibrated SRTM DEMs for flood mapping regardless of whether flood inundation areas were derived directly from projections of WSEs on the terrain or based on hydraulic simulations. Results further highlight the potential of using a Bayesian approach to improve quality and reliability of flood hazards maps, especially in regions that lack topographic data. [ABSTRACT FROM AUTHOR]

Abstract Information on the depth of floodwater is crucial for rapid mapping of areas affected by floods. However, previous approaches for estimating floodwater depth, including field surveys, remote sensing, and machine learning techniques, can be time-consuming and resource-intensive. This paper presents an automated and rapid approach for estimating floodwater depth from on-site flood photos. A pre-trained large multimodal model, Generative pre-trained transformers (GPT-4) Vision, was used specifically for estimating floodwater. The input data were flood photos that contained referenced objects, such as street signs, cars, people, and buildings. Using the heights of the common objects as references, the model returned the floodwater depth as the output. Results show that the proposed approach can rapidly provide a consistent and reliable estimation of floodwater depth from flood photos. Such rapid estimation is transformative in flood inundation mapping and assessing the severity of the flood in near-real time, which is essential for effective flood response strategies.

Information on the depth of floodwater is crucial for rapid mapping of areas affected by floods. However, previous approaches for estimating floodwater depth, including field surveys, remote sensing, and machine learning techniques, can be time-consuming and resource-intensive. This paper presents an automated and rapid approach for estimating floodwater depth from on-site flood photos. A pre-trained large multimodal model, Generative pre-trained transformers (GPT-4) Vision, was used specifically for estimating floodwater. The input data were flood photos that contained referenced objects, such as street signs, cars, people, and buildings. Using the heights of the common objects as references, the model returned the floodwater depth as the output. Results show that the proposed approach can rapidly provide a consistent and reliable estimation of floodwater depth from flood photos. Such rapid estimation is transformative in flood inundation mapping and assessing the severity of the flood in near-real time, which is essential for effective flood response strategies. [ABSTRACT FROM AUTHOR]

Limited availability of hydrometeorological data and lack of data sharing practices have added to the challenge of hydrological modelling of large and transboundary catchments. This research evaluates the suitability of latest near real-time global precipitation measurement (GPM)-era satellite precipitation products (SPPs), IMERG-Early, IMERG-Late and GSMaP-NRT, for hydrological and hydrodynamic modelling of the Brahmaputra Basin. The HEC-HMS modelling system was used for the hydrological modelling of the Brahmaputra Basin, using IMERG-Early, IMERG-Late, and GSMaP-NRT. The findings showed good results using GPM SPPs for hydrological modelling of large basins like Brahmaputra, with Nash–Sutcliffe efficiency (NSE) and R2 values in the range of 0.75–0.85, and root mean square error (RMSE) between 7000 and 9000 m3 s−1, and the average discharge was 20611 m3 s−1. Output of the GPM-based hydrological models was then used as input to a 1D hydrodynamic model to assess suitability for flood inundation mapping of the Brahmaputra River. Simulated flood extents were compared with Landsat satellite-captured images of flood extents. In critical areas along the river, the probability of detection (POD) and critical success index (CSI) values were above 0.70 with all the SPPs used in this study. The accuracy of the models was found to increase when simulated using SPPs corrected with ground-based precipitation datasets. It was also found that IMERG-Late performed better than the other two precipitation products as far as hydrological modelling was concerned. However, for flood inundation mapping, all of the three selected products showed equally good results. The conclusion is reached that for sparsely gauged large basins, particularly for trans-boundary ones, GPM-era SPPs can be used for discharge simulation and flood inundation mapping. [ABSTRACT FROM AUTHOR]

Flooding is a recurring issue in India, affecting 10–15 states annually during monsoon season and coastal regions due to cyclones. Microwave satellite data from SAR sensors like RISAT-1A, launched in February 2022, offers near real-time flood mapping crucial for relief efforts. Various acquisition modes, including medium resolution ScanSAR (MRS) mode, coarse resolution ScanSAR (CRS) mode and fine resolution Strimap (FRS) mode, provide suitable resolutions for flood mapping. Case studies demonstrate the behaviour of the backscatter coefficient in flood pixels, essential for flood map preparation. Validation against optical datasets shows a high accuracy of 91% in CRS, 94% in MRS and 94% in FRS, which is acceptable for near real-time mapping. The 67 and 91 flood maps were generated in 2022 and 2023 respectively, aiding state and central disaster management. [ABSTRACT FROM AUTHOR]

Accurate assessment of surface water from satellite and remote sensing data plays an important role in water and flood management and supporting natural ecosystems and human development. Remote sensing imagery has significantly advanced in water extraction methods, particularly in water index, classification, and sub-pixel analysis. Water-index-based approaches offer notable advantages such as speed and convenience among these methods. The unique characteristics of surface water and flooded areas, including their extensive coverage and dynamic nature, make the water index particularly effective for monitoring large regions. However, the complexity of land surfaces in aquatic environments presents challenges that hinder accurate water extraction. These challenges differ across various factors, such as shadows in urban and mountainous areas, small water bodies, muddy water, and water leakage in unshaded regions. The current study introduces a novel Flood/Water Extraction Index (FWEI) for identifying water and flooded areas to address these challenges. The FWEI utilizes the average ratio of visible and near-infrared bands derived from Sentinel-2 images. The proposed index utilizes images with 10-m and average visible bands and more effectively compensates for errors arising from spectral and spatial changes. Therefore, it demonstrates strong performance by more accurately mapping muddy and clear water within small water bodies and narrow rivers. The performance of the offered FWEI index is compared with other indices, including the Normalized Difference Water Indices (NDWI-G, NDWI-F), Modified NDWI (MNDWI-1, MNDWI-2), and the Automatic Water Extraction Index (AWEInsh) without shadow. While other indices excel in specific scenarios, such as built-up or non-built-up areas, and bare lands versus vegetated areas, the FWEI index demonstrates consistently high accuracy and stability in extracting surface water across diverse backgrounds. The FWEI index achieves an average Overall Accuracy (OA) of 94.26% for water extraction and 93.11% for flood extraction. In comparison, the AWEInsh attains an OA of 90.48% and 90.39%, NDWI-F performs at 86.69% and 86.55%, MNDWI-1 at 77.21% and 75.82%, MNDWI-2 at 76.12% and 75.42%, and NDWI-G at 75.26% and 74.78%, respectively. The integration of visible spectral bands with the near-infrared band proves instrumental in enhancing the accuracy of water derivation in complex and expansive environments. [ABSTRACT FROM AUTHOR]

Floods are consistently ranked as the most financially devastating natural disasters worldwide. Recent flood events in the Netherlands, Caribbean, and US have drawn attention to flood risks resulting from pluvial and fluvial sources. Despite shared experiences with flooding, these regions employ distinct approaches and flood management strategies due to differences in governance and scale—offering a three-site case study comparison. A key, yet often lacking, factor for flood risk and damage assessments at the parcel level is building elevation compared to flood elevation. First-floor elevations (FFEs) are a critical element in the vulnerability of a building flooding. US-based flood insurance policies require FFEs; however, data availability limitations exist. Drone-based FFEs were measured in all locations to assess the flood vulnerabilities of structures. Flood vulnerability profiles revealed 64% of buildings were vulnerable to a form of inundation, with 40% belonging to "moderate" or "major" inundation, and inundation elevation means (IEMs) of −0.55 m, 0.19 m, and 0.71 m within the US, Netherlands, and Puerto Rico sites, respectively. Spatial statistics revealed FFEs were more responsible for flood vulnerabilities in the US site while topography was more responsible in the Netherlands and Puerto Rico sites. Additional findings in the Puerto Rico site reveal FFEs and next highest floor elevations (NHFEs) vulnerable to future sea level rise (SLR) flood elevations. The findings within the Netherlands provide support for developing novel multi-layered flood risk reduction strategies that include building elevation. We discuss future work recommendations and how the different sites could benefit significantly from strengthening FFE requirements. [ABSTRACT FROM AUTHOR]

Flooding is a major environmental problem facing urban cities, causing varying degrees of damage to properties and disruption to socio-economic activities. Nigeria is the most populous African country and Kano metropolis is the second largest urban center in Nigeria, and the most populated in Northern Nigeria. The aim of the paper was to conduct a flood risk assessment of Kano metropolis. The city is divided into two hydrological basins: the Challawa and Jakara basins. Flood frequency analyses for 2 to 100-year return periods were carried out for both the basins using a Log-Pearson Type III distribution and flood inundation and hazard mapping was carried out. The social vulnerability to flooding of both basins was assessed using the method for the improvement of vulnerability assessment in Europe (MOVE) framework. Flood risk was determined as a product of flood hazard and flood vulnerability. The results showed that areas of 50.91 and 40.56 km2 were vulnerable to a 100-year flood. The flood risk map for the two basins showed that 10.50 km2 and 14.23 km2 of land in Challawa and Jakara basins, respectively, was affected by the risk of a 100-year flood, out of which 11.48 km2 covers built-up areas. As the city is densely populated, with a population density of well over 20,000 persons per square kilometer in the highly built-up locations, this means that much more than 230,000 persons will be affected by the flood risk in the two basins. [ABSTRACT FROM AUTHOR]

Throughout history, natural disasters have caused severe damage to people and properties worldwide. Flooding is one of the most disastrous types of natural disasters. A key feature of flood assessment has been making use of the information derived from remote-sensing imagery from optical sensors on satellites using spectral indices. Here, a study was conducted about a recent spectral index, the Normalised Difference Inundation Index, and a new ensemble spectral index, the Concatenated Normalised Difference Water Index, and two mature spectral indices: Normalised Difference Water Index and the differential Normalised Difference Water Index with four different machine learning algorithms: Decision Tree, Random Forest, Naive Bayes, and K-Nearest Neighbours applied to the PlanetScope satellite imagery about the Brisbane February 2022 flood which is in urban environment. Statistical analysis was applied to evaluate the results. Overall, the four algorithms provided no significant difference in terms of accuracy and F1 score. However, there were significant differences when some variations in the indices and the algorithms were combined. This research provides a validation of existing measures to identify floods in an urban environment that can help to improve sustainable development. [ABSTRACT FROM AUTHOR]

Managing the levee system of a river network is an essential aspect to reduce the flood risk and protect communities and urbanised areas. Too many times, the viewpoint of the managing institution in charge of the restoration of river levees is of an emergency type so that the restoration works are conducted only after flood occurrence, without the consideration for a complete and necessary overview of priority criteria at a basin scale. The present work aims to develop a basin-scale methodology to analyse the current state of the Ombrone Pistoiese River (Italy) levees, so as to identify targeted solutions for breakage prevention. By applying a vulnerability index, producing maps of flooded areas and counting the exposed elements induced by levees breaks, this study wants to provide a decision support procedure able to define a priority scale of interventions. [ABSTRACT FROM AUTHOR]

1- Introduction Floods pose significant risks as natural disasters on a global scale. In addition to climate change, anthropogenic activities have exacerbated the damaging effects of flooding over the past decade. Findings indicate that over 91 billion hectares of land in Iran are susceptible to flooding. Extensive hydrologic records reveal a total of 467 flooding events in the country up until 2002, contributing to a loss of 630 lives between 1982 and 1992. These alarming statistics underscore the necessity of studying and analyzing floods, as well as mapping inundation areas, in order to mitigate potential damage associated with future flooding events. One particular region in western Iran that has been heavily affected by recurring floods is the Kashkan River. This river traverses numerous urban and rural areas that are prone to annual flooding. Although various methodologies exist to investigate floods and identify areas susceptible to inundation, many of these approaches require data that may not be readily accessible. Consequently, this study employs the Height Above the Nearest Drainage (HAND) model to examine the flood-prone sections along the Kashkan River. Unlike other methods, the HAND model solely relies on a Digital Elevation Model (DEM), making it a promising and accurate technique for mapping inundation areas. By utilizing the HAND model, this research aims to identify specific sections of the Kashkan River that are prone to flooding. The findings of this study will contribute to a better understanding of flood dynamics in the region, enabling the development of effective strategies to minimize potential damage. 2 - Methodology In order to examine the areas that are prone to flooding, we employed the Height Above Nearest Drainage (HAND) model. This model was originally introduced by Rennó et al. (2008) and operates by utilizing a Digital Elevation Model (DEM) to compute the distance between a grid-cell within the topography data and the nearest cell along the stream it drains into. By considering cells with HAND values below a specific threshold as inundated, we can identify areas that are susceptible to flooding. Notably, the HAND model offers the advantage of being raster-based, eliminating the need for the construction of cross-sections, which is a requirement for alternative techniques like HEC-RAS. For our investigation, we employed the Shuttle Radar Topography Mission (SRTM) DEM, which possesses a resolution of 30 meters. To validate the findings obtained from the HAND model, we employed flood maps generated from Sentinel-1 Synthetic Aperture Radar (SAR) data from March 2019. These flood maps were created using the Sentinel Application Platform (SNAP) software and the OTSU thresholding method. This method allows us to distinguish flooded areas within the study region and validate the results obtained from the HAND model. 3- Results The findings from this study indicate that a significant portion of the study area, encompassing 54.49 square km, is classified as highly flood prone. Additionally, an area measuring 31.78 square km is categorized as flood prone. Based on the HAND maps, it is evident that more than 30 percent of the study area is susceptible to flooding with varying intensities. The outcomes further reveal that the majority of both rural and urban regions situated alongside the Kashkan River are located within highly flood prone sections. Moreover, a comparison between the flood maps derived from Synthetic Aperture Radar (SAR) and the HAND results demonstrates that the HAND model successfully identified and classified the flood area in March 2019 as highly flood prone and flood prone. By examining the cross-section along the river, it becomes apparent that the inner banks of meanders are more susceptible to flooding when compared to the outer banks. Cross-section 1 analysis revealed that the sections with a high susceptibility to flooding are situated at higher elevations when compared to the maximum flood level observed in March 2019. This implies that in the event of more sever floods in the future, a larger portion of the areas along cross-section 1 will likely to be inundated. Additionally, the analysis demonstrated that during elevated flood flows, the outer bank of the meander is more prone to flooding compared to the inner bank. Similarly, in cross-section 2, the outer bank of the meander exhibits a greater extent of highly flood-prone areas compared to the inner bank. Furthermore, cross-section 2 findings indicate that the flood flow during March 2019 did not reach the upper limit of the highly flood-prone category. In contrast to the previous cross-sections, crosssections 3 and 4 primarily experienced flooding along the inner bank of the meanders, and the HAND model also classified them as highly flood prone. These results suggest that the distribution of inundation in the Kashkan river is predominantly influenced by the underlying topography. 4- Discussion & Conclusions The findings indicated that both rural and urban regions situated along the Kashkan river exhibit a high vulnerability to flooding, with a propensity for inundation during flooding events. The results revealed the efficacy of the HAND model in accurately identifying the flood-prone segments of the Kashkan River. Analysis of cross-sections along the river revealed that the inundation patterns in mountainous meandering rivers are predominantly influenced by the underlying topography. Overall, the HAND model represents a swift and precise approach for delineating areas at risk of flooding, thereby assisting authorities in enhancing planning strategies and implementing effective measures for mitigating damages. [ABSTRACT FROM AUTHOR]

IntroductionFlood is one of the most destructive natural disasters that has a negative impact on social, economic and environmental dimensions. Floods usually occur following a prolonged period of rain or snowmelt in combination with unfavorable conditions. In this regard, all over the world, the occurrence of floods has intensified by 40% in the last two decades. In Asia, almost 90% of all human casualties caused by natural disasters are due to floods. The increase in flooding is usually due to increased environmental degradation such as urbanization, increased population growth, and deforestation. Periodic and regular occurrences of floods over a certain timeframe significantly amplify the detrimental impacts on living organisms. Urban areas in close proximity to rivers bear the brunt of these damages, owing to high population density, economic infrastructure, and transportation networks. However, these consequences can be alleviated through meticulous vulnerability analysis. One of the primary objectives pursued by researchers and policymakers is the precise modeling and zoning of floods to mitigate associated risks. Consequently, a myriad of methods and approaches have been devised for flood risk modeling and zoning to address this pressing issue. Among them, hydrological methods such as rainfall-runoff modeling and data-based techniques, which are unable to comprehensively analyze rivers and flood zones due to their one-dimensional nature. This is despite the fact that the morphology of the river is not stable and due to its high erosion potential, it also has a dynamic characteristic. In addition, these methods require fieldwork and large budgets for data collection. Hence, comprehensive flood management is necessary to reduce these effects. Therefore, this study was conducted with the aim of identifying areas sensitive to the risk of flooding in Famnat watershed located in Gilan province. Fomanat watershed is located in Gilan province and is considered a part of the first grade watershed of the Central Plateau. This area is located in the range of 36.89 to 37.57 degrees north latitude and 48.77 to 49.69 degrees east longitude. This region has an area of 3595 square kilometers, the highest point of which is 3088 meters and the lowest point is -69 meters. Materials and Methods To carry out the current research, firstly, by reviewing the sources and history of the research, as well as knowing the region, a map and layers of information related to the factors affecting flood susceptibility zoning were prepared. These layers include land use map, slope degree, geology, distance from waterway, digital map of height, direction, shape of land curvature, land curvature profile, rainfall and topographic humidity index, which are created using the collected data and also various additions in the environment. Geographic information system (Arcgis 10.4) was prepared. In this regard, machine learning models such as generalized linear model (GLM), multivariate adaptive regression model (MARS) and classification and regression tree model (CART) were used to zone the sensitivity of the watershed to floods. Also, among 100 flood events, 70% (70) were considered for training and 30% (30) for validation. In the following, using field survey and review of previous studies, 10 factors influencing the occurrence of floods in the watershed area were identified and used. Finally, the area under the ROC curve and the TSS index were used to evaluate the models.Results and Discussion The results of the evaluation of the most important factors affecting the sensitivity of the watershed to floods indicated that the distance from the river, the height and the curvature profile had the greatest impact on the sensitivity of the region, and on the other hand, the factors of slope, geology and topographic humidity index had the least impact. Based on the obtained results, the areas covered by very low, low, medium, high and very high classes in the CART model were 26.6, 17.6, 21.2, 0.1 and 34.0%, respectively. These results for the GLM model were 13.6, 12.7, 16.2, 25.1 and 32.4 percent, respectively. Based on the obtained results, the CART model performed better than other models, so that AUC for MARS model was equal to 0.76, CART model was equal to 0.9 and GLM model was equal to 0.84. Also, the better performance of CART model compared to other models was confirmed by other indicators. So, based on TSS, MARS model equal to 0.52, CART model equal to 0.77 and GLM model equal to 0.66 were obtained.ConclusionImplementing the findings of this study can facilitate the adoption of effective management strategies to mitigate losses and casualties. Moreover, in developing nations grappling with restricted access to hydrogeological and soil data, the utilization of geographic information systems (GIS) and data mining techniques assumes a pivotal role in conducting comprehensive studies. These technologies offer valuable insights and support decision-making processes, enabling proactive measures to address flood risks and enhance disaster resilience in vulnerable regions.

Floods are the most common phenomenon and cause the most significant economic and social damage to the population. They are becoming more frequent and dangerous. Consequently, it is necessary to create strategies to intervene effectively in the mitigation and resilience of the affected areas. Different methods and techniques have been developed to mitigate the damage caused by this phenomenon. Satellite programs provide a large amount of data on the earth's surface, and geospatial information processing tools help manage different natural disasters. Likewise, deep learning is an approach capable of forecasting time series that can be applied to satellite images for flood prediction and mapping. This article presents an approach for flood segmentation and visualization using the U-Net architecture and Sentinel-1 synthetic aperture radar (SAR) satellite imagery. The U-Net architecture can capture relevant features in SAR images. The approach comprises various phases, from data loading and preprocessing to flood inference and visualization. For the study, the georeferenced dataset Sen1Floods11 is used to train and validate the model through different epochs and training. A study area in southeastern Mexico that presents frequent floods was chosen. The results demonstrate that the segmentation model achieves high accuracy in detecting flooded areas, with promising metrics regarding loss, precision, and F1-score.

Floods are among the world's worst natural disasters, causing significant damage to people, infrastructure, and the economy. Since synthetic aperture radar (SAR) can work in all weather and is not affected by clouds and rain, the use of SAR for flood mapping and disaster assessment has obvious advantages. However, SAR images are highly susceptible to speckle noise, shadows, and distortions, which affects the accuracy of traditional water body extraction methods. To solve this problem, we designed a new model called WaterDetectionNet (WDNet) based on SAR remote sensing images and convolutional neural network, which has a strong water extraction capability for accurate flood mapping. In order to improve the generalization ability of the model, we used a semiautomatic strategy to generate the SAR dataset S1Water containing rich semantic information with diversity. Compared with the traditional machine learning and deep learning methods, we introduced a self-attention module to increase spatial and channel attention, and adaptively update the network weights, which improved the model performance and extraction accuracy of the real case study of the Poyang Lake flood in 2020. The experimental results show that the accuracy, recall, intersection over union, and F1 score of the WDNet model were 0.986, 0.994, 0.974, and 0.987, respectively. This method is expected to provide a cost-effective alternative to global rapid flood mapping, improve the reliability of flood disaster analysis, and offer a reference for postdisaster emergency management.

Floods are one of the extreme events and widespread natural disasters that significantly affect the civil infrastructure and livelihoods of people. Recently, climate change has significantly altered the rainfall pattern and increased flood events worldwide, especially in India. Therefore, it has become essential to map potential flood inundation regions for various future extreme events to develop appropriate flood mitigation and management strategies. This study aims to develop flood inundation maps for different return periods under climate change scenarios for the Chaliyar basin, Kerala. The Hydrologic Engineering Center-Hydrologic Modelling System model was used to simulate streamflow under SSP2-4.5 and SSP5-8.5 scenarios. Later, flood inundation maps were developed for different return periods using the Hydrologic Engineering Center-River Analysis System model. It was observed that for the near future (2031–2040) and far future (2071–2080), simulated streamflow is higher for SSP5-8.5. However, the mid-future (2051–2060) resulted in a higher streamflow for SSP2-4.5 than the SSP5-8.5 scenario. A maximum of 19.52 m of water surface elevation occurred at Kizhupparamba during mid-future for SSP2-4.5, followed by 18.38 m of water surface elevation at Cheekode during the near future for SSP5-8.5, for 100-year return period events. This study showed that hydrologic and hydraulic models could be effectively combined for mapping the flood inundation areas. HIGHLIGHTS This study integrates the hydrological model, hydraulic model and climate change scenarios to develop flood inundation maps for various return periods.; SSP2-4.5 and SSP5-8.5 are considered for climate change scenarios to simulate the streamflow for the near future (2031–2040), mid-future (2051–2060) and far future (2071–2080).; Future streamflow in the Chaliyar basin will likely to increase for both SSP2-4.5 and SSP5-8.5 scenarios.;