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Coal mining activities greatly damage water resources, explicitly concerning water quality. The adverse effects of coal mining and potential routes for contaminants to migrate, either through surface water or infiltration, into the groundwater table. Dealing with pollution from coal mining operations is a significant surface water contamination concern. Consequently, surface water resources get contaminated, harming nearby agricultural areas, drinking water sources, and aquatic habitats. Moreover, the percolation process connected with coal mining could alter groundwater quality. Subsurface water sources can get contaminated by toxins generated during mining activities that infiltrate the soil and reach the groundwater table. The aims of this study are the creation of models and the provision of proposals for corrective measures. Twenty-five scenarios were simulated using MODFLOW; according to the percolation percentage and contamination, 35% of the study area, i.e., the middle of the research area, was the most affected. About 38.08% of the area around the mining zones surrounding Margherita is prone to floods. Agricultural areas, known for applying chemical fertilizers, are particularly vulnerable, generating a risk of pollution to surrounding water bodies during flooding. The outputs of this research contribute to identifying and assessing flood-vulnerable regions, enabling focused measures for flood risk reduction, and strengthening water resource management.

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States of India like Bihar, Uttar Pradesh, and West Bengal along the Ganga River, endure natural disasters periodically, resulting in repeated trends of economic loss and damages. Especially, most of the districts of Bihar, India, are prone to floods. Based on this background, this study aims to assess the flood vulnerabilities across districts of Bihar, India, employing data from the Central Water Commission from 1953 to 2020. Further, we explore trends and patterns of loss and damage due to flood risks in Bihar. Using the flood vulnerability integrated method and the principal component analysis, the index is constructed by incorporating the three major indicators: exposure, sensitivity, and adaptive capacity. This study is unique, and advances from previous studies in using a greater number of variables in exposure indicator. The proxy variable for each indicator is identified through both inductive and deductive approaches, and the composite index is constructed using all three indicators. Also, we identify the districts with high level of education and per capita income are less likely to expose flood vulnerability. The comparison of the districts reflects wide range of variation in terms of flood vulnerability as per their adaptive capacity and sensitivity. Specifically, these findings align with Target Sustainable Development Goal 11.5. This study addresses the policy for disaster prevention, risk reduction, and mitigation measures, as well as the enhancement of the capability of adaptation to floods by the affected community.

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Time constraints, financial limitations, and inadequate tools restrict the flood data collection in undeveloped countries, especially in the Asian and African regions. Engaging citizens in data collection and contribution has the potential to overcome these challenges. This research demonstrates the applicability of citizen science for gathering flood risk-related data on residential flooding, land use information, and flood damage to paddy fields for the Bui River Basin in Vietnam. Locals living in or around flood-affected areas participated in data collection campaigns as citizen scientists using self-investigation or investigation with a data collection app, a web form, and paper forms. We developed a community-based rainfall monitoring network in the study area using low-cost rain gauges to draw locals' attention to the citizen science program. Fifty-nine participants contributed 594 completed questionnaires and measurements for four investigated subjects in the first year of implementation. Five citizen scientists were active participants and contributed more than 50 completed questionnaires or measurements, while nearly 50% of citizen scientists participated only one time. We compared the flood risk-related data obtained from citizen scientists with other independent data sources and found that the agreement between the two datasets on flooding points, land use classification, and the flood damage rate to paddy fields was acceptable (overall agreement above 73%). Rainfall monitoring activities encouraged the participants to proactively update data on flood events and land use situations during the data collection campaign. The study's outcomes demonstrate that citizen science can help to fill the gap in flood data in data-scarce areas.

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Climate change and increasing urbanization are two primary factors responsible for the increased risk of serious flooding around the world. The prediction and monitoring of the effects of land use/land cover (LULC) and climate change on flood risk are critical steps in the development of appropriate strategies to reduce potential damage. This study aimed to develop a new approach by combining machine learning (namely the XGBoost, CatBoost, LightGBM, and ExtraTree models) and hydraulic modeling to predict the effects of climate change and LULC change on land that is at risk of flooding. For the years 2005, 2020, 2035, and 2050, machine learning was used to model and predict flood susceptibility under different scenarios of LULC, while hydraulic modeling was used to model and predict flood depth and flood velocity, based on the RCP 8.5 climate change scenario. The two elements were used to build a flood risk assessment, integrating socioeconomic data such as LULC, population density, poverty rate, number of women, number of schools, and cultivated area. Flood risk was then computed, using the analytical hierarchy process, by combining flood hazard, exposure, and vulnerability. The results showed that the area at high and very high flood risk increased rapidly, as did the areas of high/very high exposure, and high/very high vulnerability. They also showed how flood risk had increased rapidly from 2005 to 2020 and would continue to do so in 2035 and 2050, due to the dynamics of climate change and LULC change, population growth, the number of women, and the number of schools - particularly in the flood zone. The results highlight the relationships between flood risk and environmental and socio-economic changes and suggest that flood risk management strategies should also be integrated in future analyses. The map built in this study shows past and future flood risk, providing insights into the spatial distribution of urban area in flood zones and can be used to facilitate the development of priority measures, flood mitigation being most important.

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The study made a comprehensive effort to examine climatic uncertainties at both yearly and monthly scales, along with mapping flood risks based on different land use categories. Recent studies have progressively been engrossed in demonstrating regional climate variations and associated flood probability to maintain the geo-ecological balance at micro to macro-regions. To carry out this investigation, various historical remote sensing record, reanalyzed and in-situ data sets were acquired with a high level of spatial precision using the Google Earth Engine (GEE) web-based remote sensing platform. Non-parametric techniques and multi-layer integration methods were then employed to illustrate the fluctuations in climate factors alongside creating maps indicating the susceptibility to floods. The study reveals an increased pattern in LST (Land Surface Temperature) (0.03 °C/year), albeit marginal declined in southern coastal regions (-0.15 °C/year) along with uneven rainfall patterns (1.42 mm/year). Moreover, long-term LULC change estimation divulges increased trends of urbanization (16.4 km2/year) together with vegetation growth (8.7 km2/year) from 2002 to 2022. Furthermore, this inquiry involves numerous environmental factors that influence the situation (elevation data, topographic wetness index, drainage density, proximity to water bodies, slope, and soil properties) as well as socio-economic attributes (population) to assess flood risk areas through the utilization of Analytical Hierarchy Process and overlay methods with assigned weights. The outcomes reveal nearly 55 percent of urban land is susceptible to flood in 2022, which were 45 and 37 percent in 2012 and 2002 separately. Additionally, 106 km2 of urban area is highly susceptible to inundation, whereas vegetation also occupies a significant proportion (52 km2). This thorough exploration offers a significant chance to formulate flood management and mitigation strategies tailored to specific regions during the era of climate change.

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This research assesses the flood vulnerability of Thailand's flood-prone province of Pathum Thani using a comprehensive vulnerability assessment framework. The assessment framework incorporates three key components: exposure, sensitivity, and adaptive capacity, consisting of 10, 12 and 11 flood vulnerability indicators, respectively. The flood vulnerability components and the flood vulnerability indicators are statistically validated by confirmatory factor analysis to determine the factor loadings and reliability of the components and indicators. The flood vulnerability questionnaire corresponding to the flood vulnerability indicators is subsequently developed and applied to the flood-prone districts of the province. The results show that proximity to rivers (with an indicator score of 0.685), household debt levels (0.612), land use patterns (0.617), and the proportion of low-income households (0.621) significantly contribute to the flood exposure of the province (with an exposure index score of 0.531). Larger household size (with an indicator score of 0.901), disruptions in public utility services (0.747), and workplace absenteeism due to flooding (0.741) contribute to the province's higher flood sensitivity (with a sensitivity index score of 0.633). Drainage capacity of natural and man-made waterways (0.571) contributes to low to moderate levels of flood adaptive capacity. The flood vulnerability of seven administrative districts of Pathum Thani, as measured by the flood vulnerability index scores (0.454-0.608), range from moderate to high. Local authorities need to invest in flood warning and response systems, prioritize infrastructure development and encourage community engagement to reduce the flood vulnerability.

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Flood is a frequent experience for the people living in Bangladesh, especially in the south-western region. But due to its complexity and multidisciplinary nature, flood management remains a very difficult task. This research focused on finding the most vulnerable areas to flooding for each polder within the Khulna and Satkhira districts since those areas can be identified as one of the most vulnerable areas to flooding. Water level data from fourteen stations of seven rivers (Sibsa, Rupsa-Pasur, Kobadak, Bhadra, Kobadak, Ichamati (Western Border), Betna-Kholpetua, and Satkhira Khal) were analyzed to calculate water levels for 2, 5, 10, 25, and 100-year return period applying normal distribution, extreme value type-I (EV-I), and log person type-III (LP-III) distribution methods. The EV-I distribution method was showing the best fit. The study revealed that station SW243 (Rupsa-Pasur River) in the Dacope region has the most extreme water level, station SW259 (Sibsa River) has the second-highest water level, and station SW254.5 (Satkhira Khal) in Satkhira Sadar has the third-highest water level for the return period of 100 years. A flood inundation map was prepared using the EV-I method's 10-year return period value. The Analytic Hierarchy Process (AHP) was used to demonstrate the polders' vulnerability depending on several factors. Overall, polder15 (Ghubra, Satkhira) is the most vulnerable polder, while polder 33 and polder 32 respectively are the second and third most vulnerable polders for flooding, both located in the Dacope region.

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It is a well-accepted notion that women are more vulnerable to natural disasters than men, especially in developing countries. However, in developed countries, how women's empowerment by economic and social development has reduced the gender gap in vulnerability remains insufficiently answered. As Japan passed its golden age, moving into an aging society, a study on how the gender difference in flood vulnerability has evolved can contribute to a better understanding of the types and causes of vulnerability, leading to better flood risk management in a new social context. Following this thinking, the present study conducted a longitudinal analysis using representative flooding cases in Japan over a period of forty years. It found that the women's fatality rate increased with age much faster than men's in the 1980s but reversed in a recent major flood disaster. It also revealed that most flood disaster victims were elderly in recent years. These findings suggest that the flood vulnerability at present is more driven by age-related physical ability decline, much less relevant to gender. Based on the results, it proposed a new framework for assessing flood vulnerability in an aging society. Such outcomes can help with the better formulation of flood management policies and probing into solutions.

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Flooding is a serious challenge that increasingly affects residents as well as policymakers. Many studies have noted that decreasing the urban flood vulnerability (UFV) is an indispensable strategy for reducing flood risks; however, some studies have several pertinent assessment limitations. The objective of this study is to assess the UFV of the Xuanwu-Qinhuai-Jianye-Gulou-Yuhua (XQJGY) region from 2012 to 2018 by integrating various indicators into a composite index. This study uses the environment for visualizing images (ENVI) and the geographic information system (GIS) to extract indicators that have geographic attributes for the assessment of UFV and the process analysis method is then used to explore the relationship between these indicators. The results indicated that: (1) The UFV of Xuanwu, Qinhuai, and Gulou decreased from 2012 to 2018 and the UFV of Jianye and Gulou increased from 2012 to 2015 and decreased from 2015 to 2018. (2) The vegetation coverage, precipitation during the flood season, population density, and highway density significantly contributed to the UFV. (3) There also exist transformation pathways between the indicators that led to vulnerability in five districts. This study provides a theoretical basis for the government to manage floods.

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Urban floods caused by expanding impervious areas due to urban development and short-term heavy precipitation adversely affect many coastal cities. Notably, Seoul, one of the coastal cities that experiences acute urban floods, suffers annually from urban floods during the rainfall season. Consequently, to mitigate the impacts of urban floods in Seoul, we established flood-vulnerable areas as target areas where green infrastructure planning was applied using the Stormwater Runoff Reduction Module (SRRM). We selected the Gangdong, Gangbuk, and Dobong districts in Seoul, Korea, all of which demonstrate high flood vulnerability. Analyses in reducing the runoff amount and peak time delay effect were estimated by model simulation using the SRRM. The reduction in peak discharge for the whole area occurred in the following order: Gangdong district, then Gangbuk district, and lastly Dobong district. In contrast, the reduction in peak discharge per unit area was most prominent in Gangbuk district, followed by Dobong and Gangdong districts. However, the delay effect was almost identical in all target areas. Based on the simulation results in this study, we planned green infrastructure, including green roofs, infiltration storage facilities, and porous pavement. We believe that the results of this study can significantly enhance the efficiency of urban flood restoration and green infrastructure planning in coastal cities. Supplementary Information: The online version contains supplementary material available at 10.1007/s11069-022-05477-7.

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Flooding is one of the major natural catastrophic disasters that causes massive environmental and socioeconomic destruction. The magnitude of losses due to floods has prompted researchers to focus more on robust and comprehensive modeling approaches for alleviating flood damages. Recently developed multi-criteria decision making (MCDM) methods are being widely used to construct decision-making process more participatory, rational, and efficient. In this study, two statistical MCDM approaches, namely the analytical hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS), have been employed to generate flood risk maps together with hazard and vulnerability maps in a GIS framework for Navsari city in Gujarat, India, to identify the vulnerable areas that are more susceptible to inundation during floods. The study area was divided into 10 sub areas (i.e., NC1 to NC10) to appraise the degree of flood hazard, vulnerability and risk intensities in terms of areal coverage and categorized under 5 intensity classes, viz., very low, low, moderate, high, and very high. A total of 14 flood indicators, seven each for hazard (i.e., elevation, slope, drainage density, distance to river, rainfall, soil, and flow accumulation) and vulnerability (i.e., population density, female population, land use, road network density, household, distance to hospital, and literacy rate) were considered for evaluating the flood risk. Flood risk coverage evaluated from the two approaches were compared with the flood extent computed from the actual flood data collected at 36 random locations. Results revealed that the TOPSIS approach estimated more precise flood risk coverage than the AHP approach, yielding high R2 values, i.e., 0.78 to 0.95 and low RMSE values, i.e., 0.95 to 0.43, for all the 5 risk intensity classes. The sub areas identified under "very high" and "high" risk intensity classes (i.e., NC1, NC4, NC6, NC7, NC8, and NC10) call for immediate flood control measures with a view to palliate the extent of flood risk and consequential damages. The study demonstrates the potential of AHP and TOPSIS integrated with GIS towards precise identification of flood-prone areas for devising effective flood management strategies.

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The world has faced many disasters in recent years, but flood impacts have gained immense importance and attention due to their adverse effects. More than half of global flood destruction and damages occur in the Asia region, which causes losses of life, damage infrastructure, and creates panic conditions among the communities. To provide a better understanding of flood hazard management, flood vulnerability assessment is the primary objective. In this case, vulnerability is the central construct in flood analysis and assessment. Many researchers have defined different approaches and methods to understand vulnerability assessment and how geographic information systems assess the flood vulnerability and their associated risk. Geographic information systems track and predict the disaster trend and mitigate the risk and damages. This study systematically reviews the methodologies used to measure floods and their vulnerabilities by integrating geographic information system. Articles on flood vulnerability from 2010 to 2020 were selected and reviewed. Through the systematic review methodology of five research engines, the researchers discovered a difference in flood vulnerability assessment tools and techniques that can be bridged by integrating high-resolution data with a multidimensional vulnerability methodology. The study reviewed several vulnerability components and directly examined the shortcomings in flood vulnerability approaches at different levels. The research contributed that the indicator-based approach gives a better understanding of vulnerability assessment. The geographic information system provides an effective environment for mapping and precise analysis to mitigate the flood disaster.

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Agricultural lands are often impacted by flooding, which results in economic losses and causes food insecurity across the world. Due to the world's growing population, land-use alteration is frequently practiced meeting global demand. However, land-use changes combined with climate change have resulted in extreme hydrological changes (i.e., flooding and drought) in many areas. The state of Iowa has experienced several flooding events over the last couple of decades (e.g., 1993, 2008, 2014, 2016, 2019). Also, agribusiness is conducted across 85% of the state. In this research, we present a comprehensive assessment for agricultural flood risk in the state of Iowa utilizing most up-to-date flood inundation maps and crop layer raster datasets. The study analyzes the seasonal variation of the statewide agricultural flood risk by focusing on corn, soybean, and alfalfa crops. The results show that over $230 million average annualized losses estimated at statewide considering studied crop types. The crop frequency layers and corn suitability rating datasets are investigated to reveal regions with lower or higher productivity ratings. The study founds nearly half a million acres of cropland is under 2-year return period flood zone. Additionally, a data-driven flood model, Height Above the Nearest Drainage (HAND), is used to analyze performance against the FEMA maps. We found that the HAND flood maps performed with the correlation of 0.93 and 0.94 for 100-year and 500-year flood events regarding to the FEMA maps.

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Although the effect of digital elevation model (DEM) and its spatial resolution on flood simulation modeling has been well studied, the effect of coarse and finer resolution image and DEM data on machine learning ensemble flood susceptibility prediction has not been investigated, particularly in data sparse conditions. The present work was, therefore, to investigate the performance of the resolution effects, such as coarse (Landsat and SRTM) and high (Sentinel-2 and ALOS PALSAR) resolution data on the flood susceptible models. Another motive of this study was to construct very high precision and robust flood susceptible models using standalone and ensemble machine learning algorithms. In the present study, fifteen flood conditioning parameters were generated from both coarse and high resolution datasets. Then, the ANN-multilayer perceptron (MLP), random forest (RF), bagging (B)-MLP, B-gaussian processes (B-GP) and B-SMOreg algorithms were used to integrate the flood conditioning parameters for generating the flood susceptible models. Furthermore, the influence of flood conditioning parameters on the modelling of flood susceptibility was investigated by proposing an ROC based sensitivity analysis. The validation of flood susceptibility models is also another challenge. In the present study, we proposed an index of flood vulnerability model to validate flood susceptibility models along with conventional statistical techniques, such as the ROC curve. Results showed that the coarse resolution based flood susceptibility MLP model has appeared as the best model (area under curve: 0.94) and it has predicted 11.65 % of the area as very high flood susceptible zones (FSz), followed by RF, B-MLP, B-GP, and B-SMOreg. Similarly, the high resolution based flood susceptibility model using MLP has predicted 19.34 % of areas as very high flood susceptible zones, followed by RF (14.32 %),B-MLP (14.88 %), B-GP, and B-SMOreg. On the other hand, ROC based sensitivity analysis showed that elevation influences flood susceptibility largely for coarse and high resolution based models, followed by drainage densityand flow accumulation. In addition, the accuracy assessment using the IFV model revealed that the MLP model outperformed all other models in the case of a high resolution imageThe coarser resolution image's performance level is acceptable but quite low. So, the study recommended the use of high resolution images for developing a machine learning algorithm based flood susceptibility model. As the study has clearly identified the areas of higher flood susceptibility and the dominant influencing factors for flooding, this could be used as a good database for flood management.

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Among natural disasters, flood is increasingly recognized as a serious worldwide concern that causes the most damages in parts of agriculture, fishery, housing, and infrastructure and strongly affects economic and social activities. Universally, there is a requirement to increase our conception of flood vulnerability and to outstretch methods and tools to assess it. Spatial analysis of flood vulnerability is part of non-structural measures to prevent and reduce flood destructive effects. Hence, the current study proposes a methodology for assessing the flood vulnerability in the area of watershed in a severely flooded area of Iran (i.e., Kashkan Watershed). First interdependency analysis among criteria (including population density (PD), livestock density (LD), percentage of farmers and ranchers (PFR), distance to industrial and mining areas (DTIM), distance to tourist and cultural heritage areas (DTTCH), land use, distance to residential areas (DTRe), distance to road (DTR), and distance to stream (DTS)) was conducted using the decision-making trial and evaluation laboratory (DEMATEL) method. Hence, the cause and effect factors and their interaction levels in the whole network were investigated. Then, using the interdependency relationships among criteria, a network structure from flood vulnerability factors to determine their importance of factors was constructed, and the analytical network process (ANP) was applied. Finally, with the aim to overcome ambiguity, reduce uncertainty, and keep the data variability, an appropriate fuzzy membership function was applied to each layer by analyzing the relationship of each layer with flood vulnerability. Importance analysis indicated that land use (0.197), DTS (0.181), PD (0.180), DTRe (0.140), and DTR (0.138) were the most important variables. The flood vulnerability map produced by the integrated method of DEMATEL-ANP-fuzzy showed that about 19.2% of the region has a high to very high flood vulnerability.

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Flood is a danger to human beings and their properties because less consideration is given for risk analysis and assessment of risk areas. The main aim of this study was to assess the flood vulnerability areas of Fetam watershed. For analyzing this issue, flood generating factors: slope, elevation, land use/land cover, drainage density, rainfall, and soil types were rated and collected to mark out flood vulnerability zones using a multi-criteria evaluation technique within the Geographic Information System (GIS). Maps were constructed using past data on river banks and discharge of earlier floods along with topographic data to illustrate areas susceptible to flood for various discharges. The influences of all overflow distribution factors were calculated using pair-wise evaluation techniques for decisive weighted-overlay investigation of each factor in flood vulnerability assessment. The flood vulnerability valuation map showed that 67.54, 634.11, 280.89, 121.28, and 2.81 km2 areas correspond to very high, high, moderate, low, and very low flood vulnerabilities respectively. Residential areas and farming fields are located along with the flood hazard areas, which are susceptible to flooding. Hence, these areas are vulnerable to social and economic development due to loss of life and damage to properties of resident people. The main result of this study showed that the upstream and center part of Fetam watershed is highly sighted than the downstream part.

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Flooding in urban and periurban areas is a complex phenomenon that results from the interplay between urban expansion and the dynamics of the hydrological system. Understanding both processes is essential to manage flood risk. This study aimed to analyze the flood risk in urban and periurban areas of the upper and middle basin of the Luján River (Metropolitan Region of Buenos Aires, Argentina) between 1985 and 2015. We assessed the factors that affect flood frequency by analyzing the precipitation variations obtained from meteorological data and applying hydrological models. We also used supervised classification of remote sensing imagery to detect increases in impervious surface areas that could enhance flooding. Furthermore, we combined both analyses to identify flood risk situations in the region. Our results indicated that maximum precipitation and hydrometric values remained stable during the study period, with a marked interannual variability due to the presence of dry and wet phases. During the dry phase (2011-2015), when flooding events were infrequent, there was a steady urban sprawl in the floodplain area and, as a result, more people would have subsequently become exposed to flood risk. Our results evidence the lack of regional policies to regulate the urban sprawl in flood risk regions.

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Commercial assets comprise buildings, machinery and equipment, which are susceptible to floods. Existing damage models and exposure estimation methods for this sector have limited transferability between flood events and therefore limited potential for pan-European applications. In this study we introduce two methodologies aiming at improving commercial flood damage modelling: (1) disaggregation of economic statistics to obtain detailed building-level estimates of replacement costs of commercial assets; (2) a Bayesian Network (BN) damage model based primarily on post-disaster company surveys carried out in Germany. The BN model is probabilistic and provides probability distributions of estimated losses, and as such quantitative uncertainty information. The BN shows good accuracy of predictions of building losses, though overestimates machinery/equipment loss. To test its suitability for pan-European flood modelling, the BN was applied to three case studies, comprising a coastal flood in France (2010) and fluvial floods in Saxony (2013) and Italy (2014). Overall difference between modelled and reported average loss per company was only 2-19% depending on the case study. Additionally, the BN model achieved better results than six alternative damage models in those case studies (except for one model in the Italian case study). Further, our exposure estimates mostly resulted in better predictions of the damage models compared to previously published pan-European exposure data, which tend to overestimate exposure. All in all, the methods allow easy modelling of commercial flood losses in the whole of Europe, since they are applicable even if only publicly-available datasets are obtainable. The methods achieve a higher accuracy than alternative approaches, and inherently provide confidence intervals, which is particularly valuable for decision making under high uncertainty.

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The flood phenomenon in the Krueng Jreue Sub-Watershed, Aceh Besar, Indonesia, in recent years indicates biophysical damage to the land. Floods are influenced by factors from biophysical conditions of the land and high rainfall with small river cross-sectional capacity causing water to overflow the embankment and flood low areas. This research aims to analyse the flood vulnerability level in the Krueng Jreue Sub-Watershed, Aceh Besar, Indonesia. The results showed that flood vulnerability in the research area consisted of four classes: very vulnerable, vulnerable, moderately vulnerable and somewhat vulnerable, with each area averaging a score of 43.0, 38.8, 30.0 and 21.7. Types of land use that are particularly vulnerable to flooding are rice fields with a mean total score of 43.0. The vulnerable classes are found in settlements and moorings, with a total score of 42.0 and 36.5, respectively. While open land, shrubs, grasslands, primary forests and secondary forests are quite vulnerable to flooding, with a mean total score of 32.5 each: 30.0, 30.0, 28.0 and 27.0. The main components affecting flood vulnerability are rainfall, temperature and land use, while additional components are soil infiltration and slope. Mechanised hydrological disaster mitigation can be performed through optimisation of weir, embung, rorak and check-dam. Vegetative hydrological mitigation efforts can be performed by reforestation and agroforestry systems, maps and flood prediction. Non-technically, hydrological disaster mitigation efforts can be undertaken with legal policies, law enforcement, map creation and prediction of droughts and socialisation of legislation.

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This study assesses flood vulnerability, levels of vulnerability, determinants of flood vulnerability and coping strategies for flood hazards. The vulnerability and resilience of the local communities are key concepts in this study. Most households are vulnerable to flood hazards. It is therefore important to measure their levels of vulnerability and assess their responses for current and future planning. A flood vulnerability index was used to measure the extent of flood vulnerability. Key informant interviews, field surveys and household questionnaires were used to collect the data. The results show that vulnerability to flood in this community is determined by the nature of soil, dwelling type, employment, education and amount of rainfall in a season. Social and economic components scored higher than the physical environment, while social factors are higher than the economic factors. Contextual coping strategies in this community were temporary relocation, evacuation to a safe area and waiting for government and neighbours to help. The study recommends that public awareness campaigns, early warning systems and improved disaster management strategies must take into consideration differentiated levels of vulnerability and community coping mechanisms and preferences.