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21 pages, 4789 KB  
Article
AI-Driven Ensemble Learning for Spatio-Temporal Rainfall Prediction in the Bengawan Solo River Watershed, Indonesia
by Jumadi Jumadi, Danardono Danardono, Efri Roziaty, Agus Ulinuha, Supari Supari, Lam Kuok Choy, Farha Sattar and Muhammad Nawaz
Sustainability 2025, 17(20), 9281; https://doi.org/10.3390/su17209281 (registering DOI) - 19 Oct 2025
Abstract
Reliable spatio-temporal rainfall prediction is a key element in disaster mitigation and water resource management in dynamic tropical regions such as the Bengawan Solo River Watershed. However, high climate variability and data limitations often pose significant challenges to the accuracy of conventional prediction [...] Read more.
Reliable spatio-temporal rainfall prediction is a key element in disaster mitigation and water resource management in dynamic tropical regions such as the Bengawan Solo River Watershed. However, high climate variability and data limitations often pose significant challenges to the accuracy of conventional prediction models. This study introduces an innovative approach by applying ensemble stacking, which combines machine learning models such as Random Forest (RF), Extreme Gradient Boosting (XGB), Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Light Gradient-Boosting Machine (LGBM) and deep learning models like Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Temporal Convolutional Networks (TCN), Convolutional Neural Network (CNN), and Transformer architecture based on monthly Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) data (1981–2024). The novelty of this research lies in the systematic exploration of various model combination scenarios—both classical and deep learning and the evaluation of their performance in projecting rainfall for 2025–2030. All base models were trained on the 1981–2019 period and validated with data from the 2020–2024 period, while ensemble stacking was developed using a linear regression meta-learner. The results show that the optimal ensemble scenario reduces the MAE to 53.735 mm, the RMSE to 69.242 mm, and increases the R2 to 0.795826—better than all individual models. Spatial and temporal analyses also indicate consistent model performance at most locations and times. Annual rainfall projections for 2025–2030 were then interpolated using IDW to generate a spatio-temporal rainfall distribution map. The improved accuracy provides a strong scientific basis for disaster preparedness, flood and drought management, and sustainable water planning in the Bengawan Solo River Watershed. Beyond this case, the approach demonstrates significant transferability to other climate-sensitive and data-scarce regions. Full article
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25 pages, 9479 KB  
Article
Stepwise Multisensor Estimation of Shelter Hazard and Lifeline Outages for Disaster Response and Resilience: A Case Study of the 2024 Noto Peninsula Earthquake
by Satomi Kimijima, Chun Ping, Shono Fujita, Makoto Hanashima, Shingo Toride and Hitoshi Taguchi
Sustainability 2025, 17(20), 9261; https://doi.org/10.3390/su17209261 (registering DOI) - 18 Oct 2025
Abstract
Addressing earthquake risk remains a significant global challenge, requiring rapid assessment of evacuation shelters for effective disaster response. Existing frameworks, such as FEMA’s Hazus, Copernicus EMS, and UNOSAT, offer valuable insights but are typically regional, static, and event-focused, lacking mechanisms for continuous shelter-level [...] Read more.
Addressing earthquake risk remains a significant global challenge, requiring rapid assessment of evacuation shelters for effective disaster response. Existing frameworks, such as FEMA’s Hazus, Copernicus EMS, and UNOSAT, offer valuable insights but are typically regional, static, and event-focused, lacking mechanisms for continuous shelter-level updates. This study introduces the Shelter Hazard Impact and Lifeline Outage Estimation (SHILOE) framework. SHILOE is a stepwise estimation approach integrating multisensor datasets for time-scaled assessments of shelter functionality and operability. These datasets include seismic intensity, liquefaction probability, tsunami inundation, IoT-derived power outage data, communication network disruptions, and social media. Application to the 2024 Noto Peninsula earthquake showed that ≥93.6% of designated and activated shelters were impacted by at least one hazard, with all experiencing at least one lifeline outage. The framework delivers estimates through three phases: immediate (within tens of minutes, e.g., simulation-based hazard models and lifeline data), intermediate (days, e.g., observation-based datasets), and refinement (ongoing, e.g., Social Networking Service and detailed field surveys). By progressively incorporating new data across these phases, SHILOE generates dynamic, facility-level insights that capture evolving hazard exposure and lifeline status. These outputs provide actionable information for emergency managers to prioritize resources, reinforce shelters, and sustain critical services, thereby advancing disaster resilience. Full article
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26 pages, 2560 KB  
Review
A Review of Transmission Line Icing Disasters: Mechanisms, Detection, and Prevention
by Jie Hu, Longjiang Liu, Xiaolei Zhang and Yanzhong Ju
Buildings 2025, 15(20), 3757; https://doi.org/10.3390/buildings15203757 - 17 Oct 2025
Abstract
Transmission line icing poses a significant natural disaster threat to power grid security. This paper systematically reviews recent advances in the understanding of icing mechanisms, intelligent detection, and prevention technologies, while providing perspectives on future development directions. In mechanistic research, although a multi-physics [...] Read more.
Transmission line icing poses a significant natural disaster threat to power grid security. This paper systematically reviews recent advances in the understanding of icing mechanisms, intelligent detection, and prevention technologies, while providing perspectives on future development directions. In mechanistic research, although a multi-physics coupling framework has been established, characterization of dynamic evolution over complex terrain and coupled physical mechanisms remains inadequate. Detection technology is undergoing a paradigm shift from traditional contact measurements to non-contact intelligent perception. Visual systems based on UAVs and fixed platforms have achieved breakthroughs in ice recognition and thickness retrieval, yet their performance remains constrained by image quality, data scale, and edge computing capabilities. Anti-/de-icing technologies have evolved into an integrated system combining active intervention and passive defense: DC de-icing (particularly MMC-based topologies) has become the mainstream active solution for high-voltage lines due to its high efficiency and low energy consumption; superhydrophobic coatings, photothermal functional coatings, and expanded-diameter conductors show promising potential but face challenges in durability, environmental adaptability, and costs. Future development relies on the deep integration of mechanistic research, intelligent perception, and active prevention technologies. Through multidisciplinary innovation, key technologies such as digital twins, photo-electro-thermal collaborative response systems, and intelligent self-healing materials will be advanced, with the ultimate goal of comprehensively enhancing power grid resilience under extreme climate conditions. Full article
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32 pages, 9776 KB  
Article
Application of Comprehensive Geophysical Methods in the Exploration of Fire Area No. 1 in the Miaoergou Coal Field, Xinjiang
by Xinzhong Zhan, Haiyan Yang, Bowen Zhang, Jinlong Liu, Yingying Zhang and Fuhao Li
Appl. Sci. 2025, 15(20), 11164; https://doi.org/10.3390/app152011164 - 17 Oct 2025
Abstract
Coal spontaneous combustion in arid regions poses severe threats to both ecological security and resource sustainability. Focusing on the detection challenges in Fire Zone No. 1 of the Miaoergou Coalfield, Xinjiang, this study proposes an Integrated Geophysical Collaborative Detection Framework that combines high-precision [...] Read more.
Coal spontaneous combustion in arid regions poses severe threats to both ecological security and resource sustainability. Focusing on the detection challenges in Fire Zone No. 1 of the Miaoergou Coalfield, Xinjiang, this study proposes an Integrated Geophysical Collaborative Detection Framework that combines high-precision magnetic surveys, spontaneous potential (SP) measurements, and transient electromagnetic (TEM) methods. This innovative framework effectively overcomes the limitations of traditional single-method detection approaches, enabling the precise delineation of fire zone boundaries and the accurate characterization of spatial dynamics of coal fires. The key findings of the study are as follows: (1) High-magnetic anomalies (with a maximum ΔT of 1886.3 nT) exhibit a strong correlation with magnetite-enriched burnt rocks and dense fracture networks (density > 15 fractures/m), with a correlation coefficient (R2) of 0.89; (2) Negative SP anomalies (with a minimum SP of −38.17 mV) can effectively reflect redox interfaces and water-saturated zones (moisture content > 18%), forming a “positive–negative–positive” annular spatial structure where the boundary gradient exceeds 3 mV/m; (3) TEM measurements identify high-resistivity anomalies (resistivity ρ = 260–320 Ω·m), which correspond to non-waterlogged goaf collapse areas. Spatial integration analysis of the three sets of geophysical data shows an anomaly overlap rate of over 85%, and this result is further validated by borehole data with an error margin of less than 10%. This study demonstrates that multi-parameter geophysical coupling can effectively characterize the thermo-hydro-chemical processes associated with coal fires, thereby providing critical technical support for the accurate identification of fire boundaries and the implementation of disaster mitigation measures in arid regions. Full article
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18 pages, 4255 KB  
Article
Enhanced Velocity Extraction of Moving Subject Using Through-Wall-Imaging Radar
by Yea-Jun Jung, Hak-Hoon Lee and Hyun-Chool Shin
Appl. Sci. 2025, 15(20), 11120; https://doi.org/10.3390/app152011120 - 16 Oct 2025
Viewed by 100
Abstract
Detecting human movement through walls is vital for disaster response and security, where obstacles obscure visibility and endanger rescue operations, necessitating advanced through-wall radar (TWR) solutions. This study introduces a novel method to enhance velocity estimation accuracy in TWR systems despite signal attenuation [...] Read more.
Detecting human movement through walls is vital for disaster response and security, where obstacles obscure visibility and endanger rescue operations, necessitating advanced through-wall radar (TWR) solutions. This study introduces a novel method to enhance velocity estimation accuracy in TWR systems despite signal attenuation caused by walls. Our proposed method dynamically adjusts the beamforming range based on radar-target distance, improving point cloud reconstruction and enabling precise velocity measurements. We conducted experiments in 1D and 2D indoor environments, with and without a brick wall, to validate the proposed method’s effectiveness. In both 1D and 2D experiments, the proposed method successfully restored the velocity information of five subjects located behind a brick wall, achieving root mean square error (RMSE) values of approximately 0.1–0.2 m/s in most cases. Furthermore, statistical comparisons before and after applying the proposed method in the brick wall environment revealed significant reductions in RMSE (p < 0.05) and significant increases in the number of detected point clouds (p < 0.05), confirming the method’s effectiveness in enhancing both velocity extraction accuracy and detection capability. Full article
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23 pages, 2648 KB  
Article
QL-AODV: Q-Learning-Enhanced Multi-Path Routing Protocol for 6G-Enabled Autonomous Aerial Vehicle Networks
by Abdelhamied A. Ateya, Nguyen Duc Tu, Ammar Muthanna, Andrey Koucheryavy, Dmitry Kozyrev and János Sztrik
Future Internet 2025, 17(10), 473; https://doi.org/10.3390/fi17100473 - 16 Oct 2025
Viewed by 161
Abstract
With the arrival of sixth-generation (6G) wireless systems comes radical potential for the deployment of autonomous aerial vehicle (AAV) swarms in mission-critical applications, ranging from disaster rescue to intelligent transportation. However, 6G-supporting AAV environments present challenges such as dynamic three-dimensional topologies, highly restrictive [...] Read more.
With the arrival of sixth-generation (6G) wireless systems comes radical potential for the deployment of autonomous aerial vehicle (AAV) swarms in mission-critical applications, ranging from disaster rescue to intelligent transportation. However, 6G-supporting AAV environments present challenges such as dynamic three-dimensional topologies, highly restrictive energy constraints, and extremely low latency demands, which substantially degrade the efficiency of conventional routing protocols. To this end, this work presents a Q-learning-enhanced ad hoc on-demand distance vector (QL-AODV). This intelligent routing protocol uses reinforcement learning within the AODV protocol to support adaptive, data-driven route selection in highly dynamic aerial networks. QL-AODV offers four novelties, including a multipath route set collection methodology that retains up to ten candidate routes for each destination using an extended route reply (RREP) waiting mechanism, a more detailed RREP message format with cumulative node buffer usage, enabling informed decision-making, a normalized 3D state space model recording hop count, average buffer occupancy, and peak buffer saturation, optimized to adhere to aerial network dynamics, and a light-weighted distributed Q-learning approach at the source node that uses an ε-greedy policy to balance exploration and exploitation. Large-scale simulations conducted with NS-3.34 for various node densities and mobility conditions confirm the better performance of QL-AODV compared to conventional AODV. In high-mobility environments, QL-AODV offers up to 9.8% improvement in packet delivery ratio and up to 12.1% increase in throughput, while remaining persistently scalable for various network sizes. The results prove that QL-AODV is a reliable, scalable, and intelligent routing method for next-generation AAV networks that will operate in intensive environments that are expected for 6G. Full article
(This article belongs to the Special Issue Moving Towards 6G Wireless Technologies—2nd Edition)
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25 pages, 1736 KB  
Article
Interdisciplinary Drivers of Puerto Rico’s Informal Housing Cycle: A Review of Key Factors
by Clifton B. Farnsworth, Andrew J. South, Kezia I. Tripp and Keona S. Wu
World 2025, 6(4), 142; https://doi.org/10.3390/world6040142 - 16 Oct 2025
Viewed by 168
Abstract
In many disaster-prone regions, lower-income communities face disproportionate impacts due to the prevalence of informal housing. Informal housing, characterized by substandard construction and lack of adherence to building codes, exacerbates vulnerabilities during disasters, leading to widespread destruction and hampered recovery efforts. This study [...] Read more.
In many disaster-prone regions, lower-income communities face disproportionate impacts due to the prevalence of informal housing. Informal housing, characterized by substandard construction and lack of adherence to building codes, exacerbates vulnerabilities during disasters, leading to widespread destruction and hampered recovery efforts. This study examines the multifaceted causes of informal housing in Puerto Rico using a qualitative content analysis of applicable literature. Seven interdisciplinary factors were derived from 42 relevant manuscripts with identifiable factors linked to informal housing in Puerto Rico: Knowledge, Perception, Government Dynamics, Institutional Support, Enforcement, Culture, and Resources. Despite post-disaster efforts advocating for building back better, systemic challenges perpetuate informal housing practices, reinforcing cycles of vulnerability. This research underscores the need for integrated decision making in pre-disaster preparation and post-disaster reconstruction efforts. This research presents a detailed understanding of the Informal Housing Cycle, demonstrates how interdisciplinary factors are barriers to safe and sustainable housing, and explores the complex relationships between these factors. This study aims to guide policy and practice to reduce future disaster impacts on Puerto Rico housing, thus breaking the cycle of vulnerability, empowering communities, and fostering sustainable resilience in post-disaster reconstruction efforts. Full article
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18 pages, 5006 KB  
Article
Hazardous Gas Emission Laws in Tunnels Based on Gas–Solid Coupling
by Yansong Li, Peidong Su, Li Luo, Yougui Li, Weihua Liu and Junjie Yang
Processes 2025, 13(10), 3308; https://doi.org/10.3390/pr13103308 - 16 Oct 2025
Viewed by 200
Abstract
This study investigates the mechanisms of hazardous gas outbursts in geologically complex non-coal tunnels. This is a critical safety concern during excavation, particularly at specific locations and during time-sensitive periods. To address this, a gas–solid coupled numerical model is established to simulate gas [...] Read more.
This study investigates the mechanisms of hazardous gas outbursts in geologically complex non-coal tunnels. This is a critical safety concern during excavation, particularly at specific locations and during time-sensitive periods. To address this, a gas–solid coupled numerical model is established to simulate gas seepage processes under such conditions. The simulations systematically reveal the spatiotemporal evolutionary patterns of the velocity and direction of the gas seepage and elucidate the migration mechanism driven by excavation-induced pressure gradients. The model specifically analyzes how geological structures, such as rock joints and fractures, control the seepage pathways. The model also demonstrates the dynamic variations in and enrichment behavior of the gas escape velocities near these discontinuities. Field measurements obtained from the Hongdoushan Tunnel validated the simulated emission patterns along jointed fissures. The findings clarify the intrinsic relationships between the outburst dynamics and key factors that include pressure differentials, geological structures, and temporal effects. This work provides a crucial theoretical foundation and practical strategy for the prediction and prevention of hazardous gas disasters in analogous tunnel engineering projects, thereby enhancing overall construction safety. Full article
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13 pages, 3412 KB  
Article
Research into Acoustic Emission Monitoring in Similar Simulation of Coal Mining Under Roof-Confined Water Drainage
by Fenghui Li, Dong Li and Yuming Gu
Processes 2025, 13(10), 3287; https://doi.org/10.3390/pr13103287 - 14 Oct 2025
Viewed by 160
Abstract
The combined disaster of roof water and rock burst occurs while working faces with roof-confined water during the mining process, and poses a significant threat to mine production safety. The drainage of confined water from the coal seam roof is a key factor [...] Read more.
The combined disaster of roof water and rock burst occurs while working faces with roof-confined water during the mining process, and poses a significant threat to mine production safety. The drainage of confined water from the coal seam roof is a key factor contributing to the risk of rock bursts during mining. To examine the impact of the drainage of roof-confined water on coal seam mining, a similar simulation method was employed to study fracture development, rock layer displacement, and fracture evolution laws in the working face under a drainage condition. The results indicated that the actual fracture height of the model aligns with the theoretical fracture height, and the model fractures extend through the drainage area. The displacement of the rock layer below the drainage area exhibits a distinct step-like distribution at the drainage boundary, whereas the displacement of the rock layer above the drainage area forms a “V” shape distribution. The gradient on the drainage side is significantly smaller than that of the non-drainage area. The number of acoustic emission events and amount of energy concentration is the highest at the boundary of the drainage module. In terms of event occurrence, the temporal concentration is 1.35 times greater than the space concentration, while in terms of energy, the temporal concentration is 2.5 times greater than the space concentration. The findings hold important theoretical and practical significance for ensuring the safety of roof water-rich working faces in mining. Full article
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31 pages, 6251 KB  
Article
Flood Risk Prediction and Management by Integrating GIS and HEC-RAS 2D Hydraulic Modelling: A Case Study of Ungheni, Iasi County, Romania
by Loredana Mariana Crenganis, Claudiu Ionuț Pricop, Maximilian Diac, Ana-Maria Olteanu-Raimond and Ana-Maria Loghin
Water 2025, 17(20), 2959; https://doi.org/10.3390/w17202959 - 14 Oct 2025
Viewed by 188
Abstract
Floods are among the most frequent and destructive natural hazards worldwide, with increasingly severe socioeconomic consequences due to rapid urbanization, land use changes, and climate variability. While the combination of Geographic Information Systems (GIS) with models such as HEC-RAS has been extensively explored [...] Read more.
Floods are among the most frequent and destructive natural hazards worldwide, with increasingly severe socioeconomic consequences due to rapid urbanization, land use changes, and climate variability. While the combination of Geographic Information Systems (GIS) with models such as HEC-RAS has been extensively explored for flood risk management, many existing studies remain limited to one-dimensional (1D) models or use coarse-resolution terrain data, often underestimating flood risk and failing to produce critical multivariate flood characteristics in densely built urban areas. This study applies a two-dimensional (2D) hydraulic modeling framework in HEC-RAS combined with GIS-based spatial analysis, using a high-resolution (1 × 1 m) LiDAR-derived Digital Terrain Model (DTM) and a hybrid mesh refined between 2 × 2 m and 8 × 8 m, with the main contributions represented by the specific application context and methodological choices. A key methodological aspect is the direct integration of synthetic hydrographs with defined exceedance probabilities (10%, 1%, and 0.1%) into the 2D model, thereby reducing the need for extensive hydrological simulations and defining a data-driven approach for resource-constrained environments. The primary novelty is the application of this high-resolution urban modeling framework to a Romanian urban–peri-urban setting, where detailed hydrological observations are scarce. Unlike previous studies in Romania, this approach applies detailed channel and floodplain discretization at high spatial resolution, explicitly incorporating anthropogenic features like buildings and detailed land use roughness for the accurate representation of local hydraulic dynamics. The resulting outputs (inundation extents, depths, and velocities) support risk assessment and spatial planning in the Ungheni locality (Iași County, Romania), providing a practical, transferable workflow adapted to data-scarce regions. Scenario results quantify vulnerability: for the 0.1% exceedance probability scenario (with a calibration accuracy of ±15–30 min deviation for peak flow timing), the flood risk may affect 882 buildings, 42 land parcels, and 13.5 km of infrastructure. This framework contributes to evidence-based decision-making for climate adaptation and disaster risk reduction strategies, improving urban resilience. Full article
(This article belongs to the Special Issue Hydrological Hazards: Monitoring, Forecasting and Risk Assessment)
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17 pages, 3831 KB  
Article
Multi-Level Loess Slope Displacement Calculation Based on Lumped Mass Method
by Bo Liu, Shuaihua Ye, Jingbang Li and Weina Ye
Buildings 2025, 15(20), 3695; https://doi.org/10.3390/buildings15203695 - 14 Oct 2025
Viewed by 148
Abstract
Earthquakes are highly unpredictable and often lead to secondary disasters such as slope collapses, landslides, and debris flows, posing serious threats to human life and property. To explore how multi-stage loess slopes respond to seismic loading, improve both the efficiency and precision of [...] Read more.
Earthquakes are highly unpredictable and often lead to secondary disasters such as slope collapses, landslides, and debris flows, posing serious threats to human life and property. To explore how multi-stage loess slopes respond to seismic loading, improve both the efficiency and precision of seismic analysis, and better capture the random characteristics of earthquakes in reliability assessment, this research proposes a new analytical framework. The approach adopts the pseudo-dynamic method, divides the slope soil into layers through the lumped mass scheme, and applies the Newmark-β integration method to construct a displacement response model that incorporates seismic variability. By comparing and analyzing results from Geo-Studio finite element simulations, the study reveals the dynamic response behavior of multi-level loess slopes subjected to seismic loads. The key findings are as follows: (1) The formation of unloading platforms introduces a graded energy dissipation effect that significantly reduces stress concentration along potential sliding surfaces; (2) The combined influence of the additional vertical load from the overlying soil and the presence of double free faces has a notable effect on the stability of secondary slopes; (3) The peak displacement response exhibits a nonlinear relationship with slope height, initially increasing and then decreasing. The proposed improved analysis method demonstrates clear advantages over traditional approaches in terms of computational efficiency and accuracy, and provides a valuable theoretical basis for the seismic design of high loess slopes. Full article
(This article belongs to the Special Issue Soil–Structure Interactions for Civil Infrastructure)
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20 pages, 49845 KB  
Article
DDF-YOLO: A Small Target Detection Model Using Multi-Scale Dynamic Feature Fusion for UAV Aerial Photography
by Ziang Ma, Chao Wang, Chuanzhi Chen, Jinbao Chen and Guang Zheng
Aerospace 2025, 12(10), 920; https://doi.org/10.3390/aerospace12100920 - 13 Oct 2025
Viewed by 347
Abstract
Unmanned aerial vehicle (UAV)-based object detection shows promising potential in intelligent transportation and disaster response. However, detecting small targets remains challenging due to inherent limitations (long-distance and low-resolution imaging) and environmental interference (complex backgrounds and occlusions). To address these issues, this paper proposes [...] Read more.
Unmanned aerial vehicle (UAV)-based object detection shows promising potential in intelligent transportation and disaster response. However, detecting small targets remains challenging due to inherent limitations (long-distance and low-resolution imaging) and environmental interference (complex backgrounds and occlusions). To address these issues, this paper proposes an enhanced small target detection model, DDF-YOLO, which achieves higher detection performance. First, a dynamic feature extraction module (C2f-DCNv4) employs deformable convolutions to effectively capture features from irregularly shaped objects. In addition, a dynamic upsampling module (DySample) optimizes multi-scale feature fusion by combining shallow spatial details with deep semantic features, preserving critical low-level information while enhancing generalization across scales. Finally, to balance rapid convergence with precise localization, an adaptive Focaler-ECIoU loss function dynamically adjusts training weights based on sample quality during bounding box regression. Extensive experiments on VisDrone2019 and UAVDT benchmarks demonstrate DDF-YOLO’s superiority. Compared to YOLOv8n, our model achieves gains of 8.6% and 4.8% in mAP50, along with improvements of 5.0% and 3.3% in mAP50-95, respectively. Furthermore, it exhibits superior efficiency, requiring only 7.3 GFLOPs and attaining an inference speed of 179 FPS. These results validate the model’s robustness for UAV-based detection, particularly in small-object scenarios. Full article
(This article belongs to the Section Aeronautics)
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32 pages, 1052 KB  
Article
Transit-Oriented Development Urban Spatial Forms and Typhoon Resilience in Taipei: A Dynamic Analytic Network Process Evaluation
by Chia-Nung Li, Yi-Kai Hsieh and Chien-Wen Lo
Atmosphere 2025, 16(10), 1178; https://doi.org/10.3390/atmos16101178 - 13 Oct 2025
Viewed by 329
Abstract
Taipei’s metropolitan region faces frequent typhoon impacts that test its urban resilience. This study examines the relationship between Transit-Oriented Development (TOD) urban spatial forms and Taipei’s resilience against typhoons, considering both physical urban morphology and planning factors. We apply a Dynamic Analytic Network [...] Read more.
Taipei’s metropolitan region faces frequent typhoon impacts that test its urban resilience. This study examines the relationship between Transit-Oriented Development (TOD) urban spatial forms and Taipei’s resilience against typhoons, considering both physical urban morphology and planning factors. We apply a Dynamic Analytic Network Process (DANP), an integrated DEMATEL-ANP multi-criteria approach to evaluate and prioritize key resilience-related spatial and planning factors in TOD areas. Rather than using GIS flood modeling, we emphasize empirical indicators derived from local data, including urban density, transit accessibility, historical typhoon flood impacts, infrastructure vulnerability, and demographic exposure. An extensive literature review covers TOD principles, urban resilience theory, and DANP methodology, with a particular emphasis on the Taiwanese context and case studies. Empirical results reveal that specific TOD characteristics indeed enhance typhoon resilience. High-density, mixed-use development around transit can reduce overall exposure to hazards by curbing sprawl into floodplains and enabling efficient evacuations. Using DANP, we find that infrastructure robustness and emergency planning capacity emerge as the most influential factors for resilience in Taipei’s TOD neighborhoods, followed by land use and management and transit accessibility. Weighted rankings of Taipei’s districts suggest that centrally located TOD-intensive districts score higher in resilience metrics, while peripheral districts with flood-prone areas tend to lag. The Discussion explores these findings, considering planning policies—noting that TOD can bolster resilience if coupled with adaptive infrastructure and inclusive planning—and compares them with examples like Singapore’s integrated land use and transit strategy, which dramatically reduced flood risk. The study concludes with policy implications for integrating TOD and climate resilience in urban planning, and contributions of the DANP approach for complex urban resilience evaluations. Full article
(This article belongs to the Special Issue Urban Adaptation to Heat and Climate Change)
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37 pages, 5073 KB  
Article
Spatiotemporal Variation and Network Correlation Analysis of Flood Resilience in the Central Plains Urban Agglomeration Based on the DRIRA Model
by Lu Liu, Huiquan Wang and Jixia Li
ISPRS Int. J. Geo-Inf. 2025, 14(10), 394; https://doi.org/10.3390/ijgi14100394 - 12 Oct 2025
Viewed by 283
Abstract
To address the flood risks driven by climate change and urbanization, this study proposes the DRIRA model (Driving Force, Resistance, Influence, Recoverability, Adaptability). Distinct from BRIC (Baseline Resilience Indicators for Communities) and PEOPLES (Population, Environmental/Ecosystem, Organized Governmental Services, Physical Infrastructure, Lifestyle, Economic Development, [...] Read more.
To address the flood risks driven by climate change and urbanization, this study proposes the DRIRA model (Driving Force, Resistance, Influence, Recoverability, Adaptability). Distinct from BRIC (Baseline Resilience Indicators for Communities) and PEOPLES (Population, Environmental/Ecosystem, Organized Governmental Services, Physical Infrastructure, Lifestyle, Economic Development, Social–Cultural Capital), the model emphasizes dynamic interactions across the entire disaster lifecycle, introduces the “Influence” dimension, and integrates SNA (Social Network Analysis) with a modified gravity model to reveal cascading effects and resilience linkages among cities. Based on an empirical study of 30 cities in the Central Plains Urban Agglomeration, and using a combination of entropy weighting, a modified spatial gravity model, and social network analysis, the study finds that: (1) Urban flood resilience increased by 35.5% from 2012 to 2021, but spatial polarization intensified, with Zhengzhou emerging as the dominant core and peripheral cities falling behind; (2) Economic development, infrastructure investment, and intersectoral governance coordination are the primary factors driving resilience differentiation; (3) Intercity resilience connectivity has strengthened, yet administrative fragmentation continues to undermine collaborative effectiveness. In response, three strategic pathways are proposed: coordinated development of sponge and resilient infrastructure, activation of flood insurance market mechanisms, and intelligent cross-regional dispatch of emergency resources. These strategies offer a scientifically grounded framework for balancing physical flood defenses with institutional resilience in high-risk urban regions. Full article
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15 pages, 55607 KB  
Article
An Enhanced SAR-Based ISW Detection Method Using YOLOv8 with an Anti-Interference Strategy and Repair Module and Its Applications
by Zheyu Lu, Hui Du, Shaodong Wang, Jianping Wu and Pai Peng
Remote Sens. 2025, 17(19), 3390; https://doi.org/10.3390/rs17193390 - 9 Oct 2025
Viewed by 269
Abstract
The detection of internal solitary waves (ISWs) in the ocean using Synthetic Aperture Radar (SAR) images is important for the safety of marine engineering structures. Based on 4120 Sentinel SAR images obtained from 2014 to 2024, an ISW dataset covering the Andaman Sea [...] Read more.
The detection of internal solitary waves (ISWs) in the ocean using Synthetic Aperture Radar (SAR) images is important for the safety of marine engineering structures. Based on 4120 Sentinel SAR images obtained from 2014 to 2024, an ISW dataset covering the Andaman Sea (AS), the South China Sea (SCS), the Sulu Sea (SS), and the Celebes Sea (CS) is constructed, and a deep learning dataset containing 3495 detection samples and 2476 segmentation samples is also established. Based on the YOLOv8 lightweight model, combined with an anti-interference strategy, a multi-size block detection strategy, and a post-processing repair module, an ISW detection method is proposed. This method reduces the false detection rate by 44.20 percentage points in terms of anti-interference performance. In terms of repair performance, the repair rate reaches 85.2%, and the error connection rate is less than 3.1%. The detection results of applying this method to Sentinel images in multiple sea areas show that there are significant regional differences in ISW activities in different sea areas: in the AS, ISW activities peak in the dry season of March and are mainly concentrated in the eastern and southern regions; the western part of the SS and the southern part of the CS are also the core areas of ISW activities. From the perspective of temporal characteristics, the SS maintains a relatively high ISW activity level throughout the dry season, while the CS exhibits more complex seasonal dynamic features. The lightweight detection method proposed in this study has good applicability and can provide support for marine disaster prevention work. Full article
(This article belongs to the Section Ocean Remote Sensing)
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