Search Results (675)

Urban Waterlogging Susceptibility Assessment (UWSA) is vital for resilient urban planning and disaster preparedness. Conventional methods depend heavily on Historical Waterlogging Records (HWR), which are limited by their reliance on extreme rainfall events and prone to human omissions, resulting in spatial bias and incomplete coverage. While hydrodynamic models can simulate waterlogging scenarios, their large-scale application is restricted by the lack of accessible underground drainage data. Recently released flood control plans and risk maps provide valuable physics-informed priors (PI-Priors) that can supplement HWR for susceptibility modeling. This study introduces a dual-source integration framework that fuses HWR with PI-Priors to improve UWSA performance. PI-Priors rasters were vectorized to delineate two-dimensional waterlogging zones, and based on the Three-Way Decision (TWD) theory, a Multi-dimensional Connection Cloud Model (MCCM) with CRITIC-TOPSIS was employed to build an index system incorporating membership degree, credibility, and impact scores. High-quality samples were extracted and combined with HWR to create an enhanced dataset. A Maximum Entropy (MaxEnt) model was then applied with 20 variables spanning natural conditions, social capital, infrastructure, and built environment. The results demonstrate that this framework increases sample adequacy, reduces spatial bias, and substantially improves the accuracy and generalizability of UWSA under extreme rainfall. Full article

Rapid urbanization across Southern Asia’s coastal regions has significantly increased electricity demand, driving India’s solar sector expansion under the National Solar Mission and positioning the country as the world’s fourth-largest solar market. Nonetheless, methodological limitations remain in applying GIS-based multi-criteria decision analysis (MCDA) frameworks to coastal urban microclimates, which involve intricate land-use dynamics and resilience constraints. To address this gap, this study proposes a multi-criteria GIS- based Analytical Hierarchy Process (AHP) framework, incorporating remote sensing and geospatial data, to assess Solar Farm Sites (SFSs) suitability, supplemented by sensitivity analysis in Thoothukudi coastal city, India. Ten parameters—covering photovoltaic, climatic, topographic, environmental, and accessibility factors—were used, with Global Horizontal Irradiance (18%), temperature (11%), and slope (11%) identified as key drivers. Results show that 9.99% (13.61 km²) of the area has excellent suitability, mainly in the southwest, while 28.15% (38.33 km²) exhibits very high potential along the southeast coast. Additional classifications include good (22.29%), moderate (32.41%), and low (7.16%) suitability zones. Sensitivity analysis confirmed photovoltaic variables as dominant, with GHI (0.25) and diffuse radiation (0.23) showing the highest impact. The largest excellent zone could support approximately 390 MW, with excellent and very high zones combined offering up to 2080 MW capacity. The findings also underscore opportunities for dual-use solar deployment, particularly on salt pans (17.1%), as well as elevated solar installations in flood-prone areas. Overall, the proposed framework provides robust, spatially explicit insights to support sustainable energy planning and climate-resilient infrastructure development in coastal urban settings. Full article

Urban flooding represents a growing concern on a global scale, particularly in regions characterized by rapid urbanization and increased climate variability. This study concentrates on the Rangsit area in Pathum Thani Province, Thailand, an urbanizing peri-urban area north of Bangkok and within the Chao Phraya River Basin where transitions in land use and the intensification of rainfall induced by climate change are elevating flood risks. A physically based hydrodynamic model was developed utilizing PCSWMM to assess current and future flood scenarios that considered future build-out plans and climate change scenarios. The model underwent calibration and validation using a continuous modeling approach that conservatively focused on wet year conditions, based on available rainfall and water level data. In assessing future scenarios, we considered land use projections based on regional development plans and climate projections downscaled under RCP4.5 and RCP8.5 pathways. Results indicate that both urban expansion and intensifying rainfall are likely to increase flood magnitudes, durations, and impacted areas, although in this rapidly developing peri-urban area, land use change was the most important driver. The findings suggest that a physically based modeling approach could support a smart-control framework that could effectively inform evidence-based urban planning and infrastructure investments. These insights are of paramount importance for flood-prone regions in Thailand and Southeast Asia, where dynamic modeling tools must underpin governance, climate adaptation, and risk communication. Furthermore, given the greater impact of future build-out on flood risk, as compared to climate change, there is an opportunity to effectively and proactively improve flood resilience through the implementation of integrated Nature-based Solution and hard engineering approaches, in combination with effective flood management policy. Full article

Leptospirosis remains a pressing yet under-recognized public health burden in the Philippines with an alarming 43.45% rise in cases in early 2025. Outbreaks closely follow flooding, disproportionately affecting impoverished communities in informal, flood-prone settlements where poor sanitation, unsafe housing, and limited healthcare access compound vulnerability. Current responses remain largely hospital-based and reactive, straining resources during seasonal surges while leaving structural drivers unaddressed. This article calls for a shift to multisectoral, preventive strategies that reduce socioeconomic vulnerabilities through stronger intersectoral collaboration, investments in flood control and basic services, and enhanced digital surveillance. Without systemic reforms that integrate health, environment, and social policy, leptospirosis will continue to impose a recurring and inequitable burden on marginalized populations. Full article

The intensification of extreme hydrologic events, such as flash floods and landslides, has amplified the challenges of ensuring timely and effective emergency response. A key factor in the efficiency of such interventions is the accessibility of affected areas, which often becomes compromised during hazard events. In this context, the present study focuses on the Vărbilău River catchment in Romania, a region highly exposed to frequent flash floods and terrain instability. The research evaluates the spatial accessibility of emergency intervention units. Four major intervention centers were assessed under both normal and constrained scenarios. Accessibility was quantified through travel-time thresholds, incorporating variables such as road quality, network density, topography, and hazard-induced disruptions. Findings indicate that southern localities enjoy relatively short intervention times (less than 10 or between 10 and 20 min) due to favorable terrain and proximity to well-equipped centers. In such cases, the speed on main roads is 50–60 km/h, while the accessibility index is 5. Conversely, northern areas and villages like Lutu Roşu face elevated isolation risks, as single-road access and weak connectivity heighten their vulnerability during floods or landslides. In such cases, speeds reduce to 10 km/h and accessibility is very low, with the accessibility index of 1. Scenario modeling further demonstrated that the loss of key hubs (e.g., Ploieşti or Văleni) severely undermines coverage efficiency, particularly in high-risk zones, where the access times increases over 40 min. These results emphasize the need for dynamic intervention planning, infrastructure reinforcement, and the systematic integration of hazard-prone areas into emergency response strategies. Moreover, the methodological framework developed here can be adapted to other regions exposed to hydrologic hazards. Full article

Inundation dynamics in low-lying deltas are becoming increasingly important to monitor due to the impacts of climate change and human alterations to hydrological systems, which disrupt natural inundation patterns. In the Nemunas River Delta, where seasonal and extreme floods impact agricultural and natural landscapes, we used Sentinel-1 synthetic aperture radar (SAR) imagery (2015–2019), validated with drone data, to map flood extents. SAR provides consistent, 10 m resolution data unaffected by cloud cover, while drone imagery provides high-resolution (10 cm) data at 90 m flight height for validation during SAR acquisitions. Results revealed peak inundation during spring snowmelt and colder months, with shorter, rainfall-driven summer floods. Approximately 60% of inundated areas were low-lying agricultural fields, which experienced prolonged waterlogging due to poor drainage and soil degradation. Inundation duration was shaped by lithology, land cover, and topography. A consistent 5–10-day lag between peak river discharge and flood expansion suggests discharge data can complement SAR when imagery is unavailable. This study confirms SAR’s value for flood mapping in cloud-prone, temperate regions and highlights its scalability for monitoring flood-prone deltas where agriculture and infrastructure face increasing climate-related risks. Full article

The integration of Artificial Intelligence (AI) into remote sensing data classification has revolutionized agriculture and environmental monitoring. AI is one of the main technologies used in smart farming that enhances and optimizes the sustainability of agricultural production. The use of AI in agriculture can involve land use mapping and crop detection, crop yield monitoring, flood-prone area detection, pest disease monitoring, droughts prediction, soil content analysis and soil production capacity detection, and for monitoring the evolution of forests and vegetation. This review examines recent advancements in AI-driven classification techniques for various applications regarding agriculture and environmental monitoring to answer the following research questions: (1) What are the main problems that can be solved through incorporating AI-driven classification techniques into the field of smart agriculture and environmental monitoring? (2) What are the main methods and strategies used in this technology? (3) What type of data can be used in this regard? For this study, a systematic literature review approach was adopted, analyzing publications from Scopus and WoS (Web of Science) between 1 January 2020 and 31 December 2024. By synthesizing recent developments, this review provides valuable insights for researchers, highlighting the current trends, challenges and future research directions, in the context of achieving the Sustainable Development Goals. Full article

Urban metro systems (UMSs) primarily consist of underground structures and are therefore highly susceptible to disasters, such as rainstorms and waterlogging. The damages caused by such events are often substantial and difficult to recover from, highlighting the urgent need to enhance the resilience of metro networks against waterlogging. Based on the principles of urban hydrology, this paper constructs scenarios to analyze the risk of waterlogging under varying rainstorm recurrence intervals and intensities. The ArcGIS geographic information system was employed to improve the existing passive inundation algorithm, enabling more accurate identification of flood-prone areas during heavy rainfall, which supports the topological modeling of UMSs. Structural connectivity was used as an external indicator of network resilience, and tools such as Gephi and NetworkX were applied to evaluate network performance. Using the Nanjing Metro as a case study, the resilience of the UMS under different risk scenarios was assessed by analyzing the impact of waterlogging events. Subsequently, recovery sequences following disruptions were prioritized to optimize post-disaster restoration, and targeted strategies for improving network resilience were proposed. The calculation results indicate that the overall resilience of the Nanjing UMS network is at a relatively high level. When connectivity is used as the performance indicator, the operating network resilience value is between 0.78 and 0.952, while the planned network resilience value is between 0.887 and 0.939. Full article

Ice disasters in the Yellow River’s Inner Mongolia reach exhibit sudden onset and high destructiveness, driven by climatic and channel constraints. The Shisifen Bend, within this reach, is particularly prone to initial ice jamming during freeze-up periods annually. This susceptibility arises from channel narrowing, increased upstream ice influx, and complex river morphology. To address persistent ice flood risks and mitigation challenges at Shisifen Bend, this study developed a coupled ice-transport numerical model. Utilizing MIKE21’s hydrodynamic and particle tracking modules alongside measured bathymetric and depth data, the model simulates ice movement under three distinct flow conditions: 2000, 2500, and 3000 m³/s. Analysis of ice trajectories and distribution patterns under varying flow conditions reveals key transport mechanisms for both ice and water. These findings provide critical insights for enhancing ice flood prevention and disaster reduction strategies along the Inner Mongolia Yellow River during freeze-up period. Full article

Monitoring urban pluvial floods remains a challenge, particularly in dense city environments where drainage overflows are localized, and sensor-based systems are often impractical. Physical sensors can be costly, prone to theft, and difficult to maintain in areas with high human activity. To address this, we developed an innovative flood detection framework that utilizes publicly accessible CCTV imagery and large language models (LLMs) to classify flooding conditions directly from images using natural language prompts. The system was tested in Bandung, Indonesia, across 340 CCTV locations over a one-year period. Four multimodal LLMs, ChatGPT-4.1, Gemini 2.5 Pro, Mistral Pixtral, and DeepSeek-VL Janus, were evaluated based on classification accuracy and operational cost. ChatGPT-4.1 achieved the highest overall accuracy at 85%, with higher performance during the daytime (89%) and lower accuracy at night (78%). A cost analysis showed that deploying GPT-4.1 every 15 min across all locations would require approximately USD 59,568 per year. However, using compact models like GPT-4 nano could reduce costs by up to seven times, with minimal loss of accuracy. These results highlight the trade-off between performance and affordability, especially in developing regions. This approach offers a scalable, passive flood monitoring solution that can be integrated into early warning systems. Future improvements may include multi-frame image analysis, automated confidence filtering, and multi-level flood classification for enhanced situational awareness. Full article

The intensification of global climate change has led to an increased frequency of extreme rainfall and temperature events, posing severe threats to China’s ecosystems and socio-economic systems. This study, based on multi-year daily precipitation, monthly surface air temperature, and daily near-surface temperature datasets, employs multi-year averaging, EOF mode analysis, Mann–Kendall testing, and R/S analysis. By selecting heavy-rain days, rainfall amount, rainfall intensity, and drought indices, it explores the spatiotemporal evolution and driving mechanisms of extreme rainfall, drought, and compound events across China. The analysis of extreme rainfall reveals that precipitation in China shows a “more in the southeast, less in the northwest; abundant in the southeast, sparse in the northwest” pattern. EOF analysis identifies two spatial modes for rainfall parameters, the “Eastern Coordination Mode” and the “North–South Antiphase Mode,” corresponding to heavy rainfall days, rainfall amount, and rainfall intensity. The Mann–Kendall test shows that some regions in the eastern monsoon zone have experienced a significant increase in heavy rainfall parameters, while certain areas in the northeast, southern China, and northwest have also undergone significant changes. By contrast, parts of the southwest have seen a decrease. R/S analysis reveals that the Hurst index is high in the eastern monsoon region, indicating a strong likelihood of continued upward trends in the future, while regions in the western arid and semi-arid zones and parts of the Tibetan Plateau exhibit stronger randomness in trends, leading to more alternating drought and flood events. The analysis of the drought index (SPI-3) reveals synchronized drought patterns in the central-eastern and northern regions, with “synergistic consistency,” “Northwest–Northeast Antiphase,” and “Northern–Central-South Antiphase” characteristics. The Mann–Kendall test indicates a “north-wet, south-dry” differentiation, with significant wetting in the northern regions and parts of the Tibetan Plateau, and significant drying in the central-eastern and southwestern regions. R/S analysis shows high Hurst indices across most of the northwest and northern regions, indicating stronger drought persistence, while coastal areas in the east are more prone to dry–wet transitions. In terms of compound events, high-temperature and heavy rainfall events have increased from northwest to southeast over the past 40 years, with southern China experiencing more than 200 days of such events. Significant changes have been observed in the eastern and southern coastal regions, with high Hurst indices and strong persistence in the eastern coastal areas. Low-temperature and heavy rainfall events are more frequent in the eastern coast and southwestern regions, with higher Hurst indices in the eastern and central regions, indicating strong persistence. Full article

Plastic pollution is an increasing global concern, with estuaries being especially vulnerable as transition zones between freshwater and marine systems. These ecosystems often accumulate large amounts of waste, affecting wildlife and water quality. This study focuses on analysing the circulation patterns of the Ave Estuary, a small, shallow system on Portugal’s north-western coast, and their influence on litter transport and distribution. This site was selected for installing an aquatic litter removal technology under the EU-funded MAELSTROM project. A 2DH hydrodynamic model using Delft3D FM, coupled with the Wflow hydrological model, was implemented and validated. Various scenarios were simulated to assess estuarine dynamics and pinpoint zones prone to litter accumulation and flood risk. The results show that tidal action and river discharge mainly drive the estuary’s behaviour. Under low discharge, floating litter should be mostly transported toward the ocean, while high discharge conditions should result in litter movement at all depths due to stronger currents. High water levels and flooding occur mainly upstream and in specific low-lying areas near the mouth. Low-velocity zones, which can favour litter accumulation, were found around the main channel and on the western margin near the estuary’s mouth, even during high flows. These findings highlight persistent accumulation zones, even under extreme event conditions. Full article

Among various natural hazards, floods stand out due to their frequency and severe impact on society and the environment. This study aimed to develop a flood susceptibility model for Demak District, Indonesia, by integrating remote sensing data, machine learning techniques, and CMIP6 Global Climate Model (GCM) data. The approach involved mapping current flood susceptibility using Sentinel-1 SAR data as the flood inventory and applying machine learning algorithms such as MLP-NN, Random Forest, Support Vector Machine (SVM), and XGBoost to predict flood-prone areas. Additionally, future flood susceptibility was projected using CMIP6 GCM precipitation data under three Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) covering the 2021–2100 period. To enhance the reliability of future projections, a multi-model ensemble approach was employed by combining the outputs of multiple GCMs to reduce model uncertainties. The results showed a significant increase in flood susceptibility, especially under higher emission scenarios (SSP5-8.5), with very high susceptibility areas growing from 16.67% in the current period to 27.43% by 2081–2100. The XGBoost model demonstrated the best performance in both current and future projections, providing valuable sustainable planning insights for flood risk management and adaptation to climate change. Full article

Machine learning techniques have been increasingly used in flood management worldwide to enhance the effectiveness of traditional methods for flood susceptibility mapping. Although these models have achieved higher accuracy than traditional ones, their application has not yet reached full maturity. We focus on applying machine learning models to create flood susceptibility maps (FSMs) for Thessaly, Greece, a flood-prone region with extreme flood events recorded in recent years. This study utilizes 13 explanatory variables derived from topographical, hydrological, hydraulic, environmental and infrastructure data to train the models, using Storm Daniel—one of the most severe recent events in the region—as the primary reference for model training. The most significant of these variables were obtained from satellite data of the affected areas. Four machine learning algorithms were employed in the analysis, i.e., Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost). Accuracy evaluation revealed that tree-based models (RF, XGBoost) outperformed other classifiers. Specifically, the RF model achieved Area Under the Curve (AUC) values of 96.9%, followed by XGBoost, SVM and LR, with 96.8%, 94.0% and 90.7%, respectively. A flood susceptibility map corresponding to a 1000-year return period rainfall scenario at 24 h scale was developed, aiming to support long-term flood risk assessment and planning. The analysis revealed that approximately 20% of the basin is highly prone to flooding. The results demonstrate the potential of machine learning in providing accurate and practical flood risk information to enhance flood management and support decision making for disaster preparedness in the region. Full article

Flooding, exacerbated by climate change, urbanization, and poor land-use practices, is a growing challenge for rural households in the Volta Basin. This study examines the effectiveness of flood management practices in improving household resilience in Benin and Togo. Using a mixed-methods approach, including focus group discussions, individual interviews, and structural equation modeling, we analyze three categories of flood management practices: Endogenous Knowledge-Based Practices (EKPs), Community Engagement-Based Practices (CEPs), and Agricultural Technology-Based Practices (ATPs). The results show significant contributions from CEPs to resilience and highlight the role of social cohesion and collective action. EKPs also have a positive impact, reflecting the importance of local knowledge, especially in Benin. However, the adoption of ATPs varies, with greater effectiveness observed in Togo than in Benin. Factors such as age, gender, education, and access to advisory services influence the acceptability and effectiveness of these practices. The findings highlight the need for tailored, integrative interventions that combine traditional knowledge and community participation with modern technologies to strengthen resilience in flood-prone communities. This study provides actionable insights for policymakers and development practitioners who aim to improve disaster risk reduction and climate resilience strategies in the Volta Basin. Full article