Flood-prone Regions Research Articles

Enhancing flood verification using Signal Detection Theory (SDT) and IoT Sensors: A spatial scale evaluation

Inundation mapping by flood simulation is subject to uncertainties which spatial accuracy assessment could not be processed as normal mapping does. This paper presents a comprehensive study on the spatial accuracy of flood verification using Internet of Things (IoT) sensors in several flood-prone regions in Taiwan, including Changhua, Yunlin, Chiayi, Tainan, Kaohsiung, and Pingtung, during three storm events in 2021. The study adopts Signal Detection Theory (SDT) and proposes an ensemble-based approach that combines multiple evaluation metrics to enhance the accuracy and reliability of flood verification in these regions. The study examines the inundation availabilities between flood observation from IoT sensors and flood simulation. A range of spatial scales varies from 100 m to 1000 m and also administrative divisions are considered including neighborhood-level and village-level scales. The results show that increasing the spatial scale generally leads to higher True Positives, indicating improved flood detection, but it also results in increased False Positives, indicating more misclassification of non-flooded points. The findings reveal that the optimal spatial scale for all cities/counties is 450 m which indicates the averaged spatial accuracy of the flood simulation. However, there is notable variation in the optimal spatial scale across different regions. The ensemble approach demonstrates high consistency in determining the optimal spatial scale, as eight out of ten metrics consistently identify the same value. Additionally, the study compares flood verification across administrative divisions in Taiwan, finding that the village-level spatial scale performs best in Changhua and Yunlin counties, while the neighborhood-level scale is preferred in Tainan, Kaohsiung, and Pingtung. This research provides a valuable framework to estimate inundation mapping accuracy under flood verification using IoT sensors in the specified region of Taiwan and highlights the importance of considering local conditions and administrative divisions in determining the optimal spatial scale. These findings can contribute to the improvement of flood detection and monitoring systems in the area with IoT flood sensors.

Formulating flood mitigation through public participation, remote sensing analysis, and flood genetic understanding: study case Samarinda City flood January 2020

Samarinda, a city characterized by its unique topography and proximity to major rivers, has been grappling with recurring and escalating flood events. The frequency and severity of these floods have been well-documented in numerous past academic studies, underscoring the pressing need for innovative flood mitigation strategies. In response to this challenge, this study employs a multi-faceted approach by incorporating public participation, remote sensing analysis, and flood genetic understanding. To facilitate a comprehensive flood mitigation strategy, we initiated the collection of real-time reports from the public during flood events. The participation of the local community in this data collection process has provided critical insights into the dynamics of flood occurrence and its impacts. The integration of crowdsourced data has been instrumental in augmenting our understanding of the local flood patterns. In parallel, we harnessed the capabilities of the Sentinel-1 satellite to conduct remote sensing analysis, enabling us to identify and map inundated areas during specific flood events. This approach, utilizing Sentinel-1, has been validated in prior flood studies. Our research further combines these datasets, merging live reports with remote sensing flood mapping to detect and assess affected areas. We introduce a novel categorization system based on flood genetics, dividing the floodprone region into three distinct zones according to hydrogeological condition of each water catchment area. We divided the area into three zones based on its genetic. This model guides us to the innovative approach enriching our flood mitigation strategy based on those valuable insights. The culmination of our study is the formulation of tailored mitigation plans for each of the three identified flood genetic zones. Incorporating public participation, remote sensing, and flood genetic understanding, our study provides a holistic framework for flood mitigation in the flood-prone city of Samarinda. By harnessing the collective intelligence of the community, leveraging cutting-edge technology, and integrating advanced scientific concepts, we present a comprehensive approach to address and mitigate the ongoing flood challenges in the region.

Trends and amount changes of temperature and precipitation under future projections in high–low groups and intra-period for the Eastern Black Sea, the Wettest Basin in Türkiye

This study investigates the possible effects of climate change on temperature and precipitation variables in the Eastern Black Sea Basin, Türkiye’s wettest and flood-prone region. The outputs of three GCMs under historical, RCP4.5, and RCP8.5 scenarios were downscaled to regional scale using the multivariate adaptive regression splines method. The future monthly temperature and precipitation for 12 stations in the basin were projected for three periods: the 2030s (2021–2050), 2060s (2051–2080), and 2090s (2081–2100). In addition to relative changes, high and low groups and intra-period trends were analyzed for the first time using innovative methods. For the pessimistic scenario, an increase of 3.5 °C in the interior and 3.0 °C in the coastal areas of the basin is projected. For the optimistic scenario, these values are expected to be 2.5 and 2.0 °C, respectively. A decrease in precipitation is projected for the interior region, and a significant increase is expected for the eastern and coastal areas of the basin, especially in spring. This result indicates that floods will occur frequently coastal areas of the basin in the coming periods. Also, although the monotonic trends of temperatures during periods are higher than precipitation in interior regions, these regions may have more uncertainty as their trends are in different directions of low and high groups of different scenarios and GCMs and contribute to all trends, especially precipitation.

Empowering real-time flood impact assessment through the integration of machine learning and Google Earth Engine: a comprehensive approach.

Floods cause substantial losses to life and property, especially in flood-prone regions like northwestern Bangladesh. Timely and precise evaluation of flood impacts is critical for effective flood management and decision-making. This research demonstrates an integrated approach utilizing machine learning and Google Earth Engine to enable real-time flood assessment. Synthetic aperture radar (SAR) data from Sentinel-1 and the Google Earth Engine platform were employed to generate near real-time flood maps of the 2020 flood in Kurigram and Lalmonirhat. An automatic thresholding technique quantified flooded areas. For land use/land cover (LULC) analysis, Sentinel-2's high resolution and machine learning models like artificial neural networks (ANN), random forests (RF) and support vector machines (SVM) were leveraged. ANN delivered the best LULC mapping with 0.94 accuracy based on metrics like accuracy, kappa, mean F1 score, mean sensitivity, mean specificity, mean positive predictive value, mean negative value, mean precision, mean recall, mean detection rate and mean balanced accuracy. Results showed over 600,000 people exposed at peak inundation in July-about 17% of the population. The machine learning-enabled LULC maps reliably identified vulnerable areas to prioritize flood management. Over half of croplands flooded in July. This research demonstrates the potential of integrating SAR, machine learning and cloud computing to empower authorities through real-time monitoring and accurate LULC mapping essential for effective flood response. The proposed comprehensive methodology can assist stakeholders in developing data-driven flood management strategies to reduce impacts.

Assessing flood susceptibility in the Godavari River Basin, Bhadrachalam Region, Sothern India: A GIS-AHP Multi-Criteria Approach

Floods, as recurrent natural disasters, exert significant impacts on both the environment and human settlements. This study focuses on evaluating the flood susceptibility of the Bhadradri Kothagudem district in Telangana, situated within the Godavari River Basin. The research integrates Geographic Information System (GIS) and the Analytic Hierarchy Process (AHP) in a multi-criteria approach to analyze and map flood susceptibility. Landsat-8 data, digital elevation model data and rainfall serve as inputs for assessing the flood susceptibility. The study considers various topographical features such as elevation, slope, roughness, contours and aspect, along with factors like land use land cover, flow accumulation, stream direction, stream network, drainage density, flow length, distance from the river, soil, normalized difference vegetation index and topographic wetness index. These variables are rescaled on a scale of one to five and combined to generate a comprehensive flood susceptibility map of the Kothagudem district using GIS. The AHP is implemented through GIS, assigning weightages on a scale of one to five based on the priority of spatial classes within thematic maps. The flood susceptibility map is produced on a scale of five, designating scale class five as of very high susceptibility and scale class one as low. Scale classes two, three and four represent intermediate levels of susceptibility. The resulting flood susceptibility maps offer valuable insights for disaster preparedness, risk mitigation and land-use planning in the Kothagudem district. The integration of GIS and AHP provides a robust methodology for assessing and visualizing flood susceptibility, enabling informed decision-making for resilient and sustainable development in flood-prone regions.

Methods and techniques of architectural environment forming under the conditions of global climate change: the problems of flooding

This article highlights the methods and techniques of forming an architectural environment based on sustainable development, namely, how to adapt to the consequences of global climate change caused by the anthropogenic environment and its activities. One of the serious consequences of global climate change is an increase in the frequency and power of floods, especially they pose a danger to cities, where problems related to the comfort of human life are worsening. Floods destroy city infrastructure and endanger the city's water supply sources. Such natural disasters activate the scientific community to eliminate their consequences, on the one hand, on the other - to solve the shortage of water as an energy resource, raising the issue of developing technologies for accumulating its surplus and further use in many types of human life, as well as in the functioning systems of the city as a whole. The world scientific and practical experience of innovative methods of adaptation of the new and reconstruction of the existing architectural environment to climate changes in countries vulnerable to frequent flooding, which is a problem for Ukraine as well, is analyzed. The article analyzes methods from Denmark, Great Britain, the Netherlands and Pakistan, with which they create a flood-resistant architectural environment and turn the disadvantages of their flood-prone region of residence into advantages. The experience of these countries and their methods of combating natural disasters helps other regions of our planet to form a comfortable architectural environment that corresponds to today's rapidly changing weather conditions and the goals of sustainable development. The conducted analysis demonstrates a wide range of solutions to problems, from small elements of the architectural environment to the planning of individual districts and the city as a whole. These solutions are used individually, as well as in combination, which increases their effectiveness. Therefore, the use of a complex approach as the main method of forming the architectural environment under conditions of global climate change is revealed.

Delineation of Water Bodies Using Vegetation and Water Indices

Mapping the water surface is a significant aspect of identifying the extension or shrinkage of any water body. It is a beneficial technique for a region, experiencing gradual river shifting, erosion, or frequent flood events. Climate change often causes excessive rainfall or drought conditions in a riverine region, significantly impacting water over-spilling or shrinkage and sedimentation of a river. The spatio-temporal study of delineation and mapping of water surface area helps to determine the pattern of change of a water body, analyze the trend of shifting of a river course, and estimate the change in the proportion of water surface area. All these kinds of assessments are helpful in analyzing the inundated area of a flood prone region. This study aims to delineate water bodies of Lakhimpur district of Assam using Geographic Information System (GIS) and remote sensing over a temporal scale of twenty years from 2001 to 2021. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) are used in this study to map and estimate the changes and extension of water bodies of the region. It is observed that there is an increase of 16.75% in water bodies in the district from 2001 to 2021

Assessment of flood risk using Hierarchical Analysis Process method and Remote Sensing systems through arid catchment in southeastern Tunisia

Flood hazard mapping is a fundamental tool for estimating vulnerability in flood-prone areas and assessing flood risk in arid areas, enabling better management. This study specifically focuses on analyzing flood risk in the Gabes catchment, South-Tunisia, which is marked by exceptional flash floods attributed to climate change, notably in the past fifty years. For this aim, a combination of Geographic Information Systems (GIS) coupled to Remote Sensing Techniques and the Analytic Hierarchy Process (AHP) method was applied in this work to identify flood-prone regions within the Gabes catchment. The selected physical parameters are rainfall, slope, slope curvature, elevation, land cover, distance from river, drainage density, and soil type. The relative significance of each parameter was analyzed within a pairwise matrix. Eventually, a regional flood risk map was performed that shows the global susceptibility index classification of various areas within the catchment. Notably, flood risk zones were predominantly concentrated in the coastal areas, accounting for 57.54% of Gabes catchment. The major findings of this study, focusing on potential flood risks in arid regions, offer crucial insights for decision-makers, providing essential data for flood preparedness and mitigation strategies. Nevertheless, it is still required to validate the obtained results with field records and flood inventory maps.

Spatial analysis of urban green space and its utilization rate for the flood-prone region Assam, India

Spatial analysis of urban green space and its utilization rate for the flood-prone region Assam, India

Cost of high-level flooding as a consequence of climate change driver?: A case study of China’s flood-prone regions

The extent of flooding in China is more significant than in any other country. Our research reveals that approximately 66 % of China’s landmass is submerged by flooding, affecting about 50 % of the population. Furthermore, the financial toll of flooding now accounts for approximately 1.42 % of the annual gross domestic product (GDP), which is almost 40 times higher than the corresponding figure for the United States. We have observed that Zhengzhou city in Henan province, which faced a devastating flood in 2021, received a significant amount of rainfall, specifically a total of 552.5 mm within a 24-hour period. The floods in Henan province in 2021 caused considerable damage, including impacting nearly 15 million people, resulting in almost 400 deaths, damaging over 10,000 square kilometers of agricultural land, causing almost $19 billion in economic losses, and leading to the collapse of over 35,000 households and damage to various properties. In a similar manner, the significant flooding that occurred in southern China in 2020 impacted approximately 7.1 million individuals across eight provinces and resulted in 54 fatalities, the collapse of 6,700 houses, and incurred a direct economic loss of US$3.33 billion. We found that rainstorms have significantly increased to 10 % in the last 60 years in China. In this paper, we delved into exploring the existing published articles, reports, and government and authoritative legal texts to analyze the causes and impacts of flooding in China’s flood-prone regions and potential mitigation strategies to reduce the repercussions of distressing flood events. We believe this study will help policymakers by providing new insights while formulating policy to reduce the loss caused by high-level flooding.

Assessment of cyclone induced flood using microwave Synthetic Aperture Radar (SAR) data in four blocks of East Midnapore district, India

East Midnapore district is one of the flood prone regions of West Bengal, India. Geomorphological character, topographic nature and geographical location are the main causes of such kind of flood extensiveness in this region. Four most flood prone blocks of East Midnapore district are considered in this study to analyse, monitor and estimate the flood consequences. Flood intensity, duration of water logging, frequency, interval of flood and measurement of the vulnerability are the important parameters to assess the flood risk. Synthetic Aperture Radar (SAR) data has its distinct character to collect the data at the flood time even in bad weather and it can also penetrate the cloud cover and can avoid weather caused signal attenuation. European Space Agency’s (ESA) Sentinel-1 SAR before and after flood data (2021) of Vertical transmit and Vertical receives (VV) polarization has been used to monitor and detect the flood extension and assess the inundated areas. The decibel (dB) value is mentioned and speckle noise correction is applied before and after both images. By applying the threshold dB values on both images, the flood inundated areas are detected and extracted from the natural water bodies. A village wise water logged condition of this region was observed for a long time and the study reveals that total 11612.28 ha. areas are submerged due to flood inundation. For the development of the agriculture and aquaculture in terms of the local economy a special care should be taken in this low land flood, affected region.

Flood teleconnections from levees undermine disaster resilience

Inland levees can amplify flood risk in unprotected communities by altering floodwater levels away from their location. While these nonlocal effects of levees, which we term flood teleconnections, have been studied for specific river segments, their impact on flood risks along a river network remains underexplored. By combining data-driven, hydrodynamic, and economic models, we quantify the magnitude, spatial distribution, and economic damages associated with flood teleconnections for a large river network system with extensive levees. We find that due to levees, the 100-year flood inundation extent grows by 25% of the total levee-protected area regionally, and the flood inundation depth increases by up to 2 m at specific locations. Levees also increase the vulnerability of unprotected, marginalized communities to flooding. Our results demonstrate that flood teleconnections are spatially widespread, involve unaccounted costs, and can lead to flood inequities. These findings will be critical to climate adaptation efforts in flood-prone regions.

Sustainable Asphalt Mixtures with Enhanced Water Resistance for Flood-Prone Regions Using Recycled LDPE and Carnauba-Soybean Oil Additive.

This manuscript presents a comprehensive study on the sustainable optimization of asphalt mixtures tailored for regions prone to flooding. The research addresses the challenges associated with water damage to asphalt pavements by incorporating innovative additives. The study centers on incorporating recycled Low-Density Polyethylene (LDPE) and a tailored Carnauba-Soybean Oil Additive, advancing asphalt mixtures with a Control mix, LDPE (5%) + Control, and LDPE (5%) + 3% Oil + Control. A critical aspect of the research involves subjecting these mixtures to 30 wetting and drying cycles, simulating the conditions prevalent in tropical flood-prone areas. The incorporation of innovative additives in asphalt mixtures has demonstrated significant improvements across various performance parameters. Tensile Strength Ratio (TSR) tests revealed enhanced tensile strength, with the LDPE (5%) + 3% Oil-modified mixture exhibiting an impressive TSR of 85.7%. Dynamic Modulus tests highlighted improved rutting resistance, showcasing a remarkable increase to 214 MPa in the LDPE (5%) with a 3% Oil-modified mixture. The Semi-Circular Bending (SCB) test demonstrated increased fracture resistance and energy absorption, particularly in the LDPE (5%) with 3% Oil-modified mixture. Hamburg Wheel-Tracking (HWT) tests indicated enhanced moisture resistance and superior rutting resistance at 20,000 cycles for the same mixture. Cantabro tests underscored improved aggregate shatter resistance, with the LDPE (5%) + 3% Oil-modified mixture exhibiting the lowest weight loss rate at 9.820%. Field tests provided real-world insights, with the LDPE (5%) + 3% Oil mixture displaying superior stability, a 61% reduction in deflection, and a 256% improvement in surface modulus over the control mixture. This research lays the groundwork for advancing the development of sustainable, high-performance road pavement materials, marking a significant stride towards resilient infrastructure in flood-prone areas.

Flooding in the Yellow River Basin, China: Spatiotemporal patterns, drivers and future tendency

Study regionThe Yellow River Basin (YRB) in China. Study focusThis study is devoted to simulating flood changes with return periods of 10, 20, 30, 50, 100 and 200 years under three Representative Concentration Pathways (RCPs) using CaMa-Flood models, and examining the drivers. New hydrological insights for the regionIn the historical period (1961–2005), the middle and lower YRB and specifically the Wei River Basin (WRB) (a tributary of the middle YRB) are dominated by higher flood risks with once-in-200-year flood magnitude of 9989.63 m3/s. Under climate change scenarios, the relative change in the once-in-100-year flood magnitude increased from 125.0% (RCP2.6) to 204.9% (RCP8.5) during the historical period (1961–2005) to the long-term period (2076–2092). The historical flood-prone regions were more susceptible to increased flood magnitude. Meanwhile, the area with increased flood risks ranged from 73.6% to 95.2% in the YRB. Increased flood magnitude was typically related to lengthened return interval of floods under the RCP2.6 and RCP6.0 scenarios, while decreased flood magnitude was commonly linked to increased return intervals under the RCP8.5 scenarios. Notably, we observed higher correlations between 7-day maximum precipitation and floods compared to those between daily maximum precipitation, soil moisture, and floods. Co-occurrences between flood events and hydrometeorological extremes were higher in the upper and middle YRB.

Investigating ecosystem resiliency in different flood zones of south Brooklyn, New York

With climate change and rising sea levels, south Brooklyn is exposed to massive flooding and intense precipitation. Previous research discovered that flooding shifts plant species distribution, decreases soil pH, and increases salt concentration, nitrogen, phosphorus, and potassium levels. We predicted a decreasing trend from Zone 1 to 6: high-pH, high-salt, and high-nutrients in more flood-prone areas to low-pH, low-salt, and low-nutrient in less flood-prone regions. We performed DNA barcoding to identify plant species inhabiting flood zones with expectations of decreasing salt tolerance and moisture uptake by plants' soil from Zones 1-6. Furthermore, we predicted an increase in invasive species, ultimately resulting in a decrease in biodiversity. After barcoding, we researched existing information regarding invasiveness, ideal soil, pH tolerance, and salt tolerance. We performed soil analyses to identify pH, nitrogen (N), phosphorus (P), and potassium (K) levels. For N and P levels, we discovered a general decreasing trend from Zone 1 to 6 with low and moderate statistical significance respectively. Previous studies found that soil moisture can increase N and P uptake, helping plants adopt efficient resource-use strategies and reduce water stress from flooding. Although characteristics of plants were distributed throughout all zones, demonstrating overall diversity, the soil analyses hinted at the possibility of a rising trend of plants adapting to the increase in flooding. Future expansive research is needed to comprehensively map these trends. Ultimately, investigating trends between flood zones and the prevalence of different species will assist in guiding solutions to weathering climate change and protecting biodiversity in Brooklyn.

Study On Flood Management and Analysis Using Geographical Information Systems On Godavari, Konaseema District, India

The Konasema area in Andhra Pradesh, India is prone to flooding due to coastal storms, storm surges, and river overflows. Flooding can be dangerous and pose a threat to human life and property. Floods are unpredictable and can happen suddenly, causing significant damage. Therefore, planning for flood management is essential for rescuing flood victims, resolving flood issues, and implementing preventive measures. In recent years, flooding has become more frequent in the Konaseema area, especially in the Godavari river's and near surrounding areas. Heavy floods from the Godavari River have been caused severe flooding in nearby konaseema villages. Flood management by water resource planners using GIS (Geographic Information System) developed land use and land cover maps, Flood zone maps in the study region. This study aims to analyse impact of Godavari River floods in Konaseema and East Godavari district Andhra Pradesh, India. DEM (Digital Elevation Model) is generated and prepared various thematic maps of Flood prone regions. The GIS generated flood impacted maps are analyzed and these maps will help to rescue the people from flood regions and migrate them in time to the safe zones.

Investigating the association between floods and low birth weight in India: Using the geospatial approach

BackgroundFrequent natural disasters like floods pose a major threat to India, with significant implications for public health. Low birth weight (LBW) is a critical global health concern, contributing to neonatal mortality. However, the association between floods and LBW remains underexplored. This study aims to address this gap by investigating the association between flood hazards and LBW in India using a geospatial approach. By analyzing data from the National Family Health Survey (NFHS-5) and flood zonation maps, the study aims to uncover the spatial dynamics of this association, offering insights into the implications of floods on birth weight across diverse geographical regions. MethodsThe study used the fifth round of NFHS data, 2019–21, which involved 202,194 children selected through a multi-stage stratified sampling technique. The Vulnerability Atlas of India 2019 maps were also utilized to classify areas as flood or non-flood zones. Birth weight data from the NFHS-5 were categorized into three groups: very low, low, and normal birth weight (VLBW, LBW and NBW). Control variables including flood exposure, socio-demographic attributes, and geographic region were considered. Bivariate analysis and multinomial logistic regression were employed for statistical analysis. The spatial analysis involved Moran's I statistics and Geographically Weighted Regression to explore spatial dynamics of the association between floods and birth weight in India. ResultsFloods predominantly affect India's lower Himalayan belts and western coastal regions. Flood-affected areas show higher proportions of VLBW and LBW infants. Groundwater usage and unimproved sanitation are associated with higher risk of VLBW and LBW. Sex, wealth, maternal education, residence type, and geographic region significantly influence birth weights. Multinomial logistic regression reveals 8 % and 27 % higher risks for LBW and VLBW in flood-affected regions. LISA cluster maps identify high-risk areas for both LBW and floods. Geographically Weighted Regression highlights 52 % of the variability in LBW occurrences can be attributed to the influence of flood hazards. Families hailing from the poorest wealth background and exposed to flood hazards bear a 5 % heightened likelihood of delivering LBW infants, in stark contrast to their counterparts from the same economic background yet unaffected by floods. ConclusionsThe significant association between floods and LBW underscores the importance of robust disaster preparedness and public health strategies. By unraveling the spatial intricacies of flood-induced LBW disparities, this research provides valuable insights for promoting healthier birth outcomes and reducing child mortality rates, particularly in flood-prone regions. These findings emphasize the importance of holistic policies that address both environmental challenges and socioeconomic inequalities to safeguard maternal and infant health across the nation.

Catastrophic risk perceptions and attitudes in aquaculture: Evidence from flood prone areas of Bangladesh

Aquaculture is still an emerging industry that is highly dependent on the environment and more unstable than other conventional agricultural operations. Diverse environmental and production hazards must be managed by aquaculture farmers for sustainability. Farmers' decisions on farm operations and risk management are significantly influenced by their risk attitudes and perceptions of risk. However, few empirical studies on risk management have been done, but literature on aquaculture is scarce. In light of this knowledge vacuum, the current study investigated how farmers perceive catastrophic risk and their attitudes toward various sources. The information was gathered using a stratified random sample method, with 300 aquaculture farmers interviewed from two major flood-prone regions in Bangladesh. A cubic utility function and the Equally Likely Certainty Equivalent (ELCE) approach were used to quantify farmers' risk aversion. The risk matrix technique was used to assess farmers' perceptions of risk. The effects of socioeconomic factors on farmers' risk attitudes were examined using a Logit model. Floods, strong rains, and pest and diseases all posed potential productivity hazards and most farmers were risk averse in nature. Age, educational position, income, and land proprietorship were the most important predictors of risk attitude, while social and agricultural characteristics had little influence on farmers' risk perceptions. The study's findings will eventually allow policymakers to forecast the suitable risk management measures for the aquaculture farmers in Bangladesh.

Understanding Women’s Empowerment Challenges in Flood-Prone Rural Bangladesh: A Qualitative Study

Women’s empowerment has consistently been a subject of debate within the intricate socio-cultural context of Bangladesh. The occurrence of flood disasters presents increased difficulties within rural communities. The objective of this research is to gain an understanding of the obstacles encountered by women residing in rural flood-prone regions as they strive to enhance their empowerment. To achieve this objective, a qualitative research approach was employed, utilizing Focus Group Discussions with six distinct groups and conducting In-depth interviews with a total of thirty participants. The data demonstrates that rural communities and flood events exacerbate women’s empowerment issues because the patriarchal culture embedded in rural Bangladesh makes it difficult to modify women’s positions. Therefore, this paper advocates for the government’s firm dedication to empowering women and harnessing their full potential to integrate them into the mainstream of society, thus fostering constructive societal development.

Object Oriented Design Systems for WebGIS Flood Risk Classification in Samarinda, Indonesia

Floods are a prevalent environmental concern that frequently impacts urban regions. In conjunction with the growth of urbanization, the expansion of urban areas frequently gives rise to issues related to flooding. Samarinda, the administrative center of East Kalimantan Province, is intrinsically linked to the problems of flooding and waterlogging. The occurrence of floods in this urban area is frequently observed during periods of heavy rainfall and elevated water discharge from the Mahakam River and its associated tributaries. The utilization of the Web Geographics Information System to present maps depicting areas susceptible to flooding serves as a digital instrument and informational resource for urban communities. This research aims to design and implement WebGIS software for the classification of flood-prone regions in Samarinda City, employing the Object-Oriented Design System (OODS) approach. The process of flood classification modeling involves the utilization of various factors, including rainfall data, road kinds, slope gradients, and land use types. The design of this WebGIS system incorporates OODS principles, utilizing the Unified Modeling Language (UML) methodology. This approach encompasses many UML techniques, including Use Case Diagrams, Class Diagrams, and Entity Relationship Diagrams. The outcome of this study resulted in the design and implementation of the WebGIS program, which serves the purpose of classifying flood-prone areas in Samarinda City. This result encompasses object designs and interfaces that visually represent flood zones on a map. The system validation test was conducted by comparing the classification results and historical flood data, with a result of 90%.