Search Results (1,239)

Urban flooding has become a critical environmental challenge under global climate change and rapid urbanization. This study develops a multi-indicator hazard mechanism framework for flood hazard assessment in Rizhao, a coastal city in China, by integrating three fundamental hydrological processes: runoff generation, flow convergence, and drainage. Based on geospatial data—including DEM, road networks, land cover, and soil characteristics—six key indicators were evaluated using the TOPSIS method: runoff curve number, impervious surface percentage, topographic wetness index, time of concentration, pipeline density, and distance to rivers. The results show that extreme-hazard zones, covering 6.41% of the central urban area, are primarily clustered in northern sectors, where flood susceptibility is driven by the synergistic effects of high imperviousness, short concentration time, and inadequate drainage infrastructure. Independent validation using historical flood records confirmed the model’s reliability, with 83.72% of documented waterlogging points located in predicted high-hazard zones and an AUC value of 0.737 indicating good discriminatory performance. Based on spatial hazard patterns and causal mechanisms, an integrated mitigation strategy system of “source reduction, process regulation, and terminal enhancement” is proposed. This strategy provides practical guidance for pipeline rehabilitation and sponge city implementation in Rizhao’s resilience planning, while the developed hazard mechanism framework of “runoff–convergence–drainage” provides a transferable methodology for flood hazard assessment in large-scale urban environments. Full article

This study investigates the kinematic behavior of a reconfigurable Delta parallel robot aiming to enhance its performance in real industrial applications such as high-speed packaging, precision pick-and-place operations, automated inspection, and lightweight assembly tasks. While Delta robots are widely recognized for their speed and accuracy, their practical use is often limited by workspace constraints and singularities that compromise motion stability and control safety. Through a detailed analysis, it is shown that classical Jacobian-based performance indices are unsuitable for resolving the redundancy introduced by geometric reconfiguration, as they may lead the system toward singular or ill-conditioned configurations. To overcome these limitations, this work introduces an adjustable singularity-sensitive performance index designed to penalize extreme velocity and force singular values and enables tuning between velocity and force performance. Simulation results demonstrate that optimizing the reconfiguration parameter using this index increases the usable workspace by approximately 82% and improves the uniformity of manipulability across the workspace. These findings suggest that the proposed approach provides a robust framework for enhancing the operational range and kinematic safety of reconfigurable Delta robots, while remaining adaptable to different design priorities. Full article

The leaf area index (LAI) serves as a critical parameter for assessing wetland ecosystem functions, and accurate LAI retrieval holds substantial significance for wetland conservation and ecological monitoring. To address the spatial constraints of traditional ground-based measurements and the limited accuracy of single-source remote sensing data, this study utilized unmanned aerial vehicle (UAV)-borne hyperspectral and LiDAR sensors to acquire high-quality multi-source remote sensing data of coastal wetlands in the Yellow River Delta. Three machine learning algorithms—random forest (RF), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost)—were employed for LAI retrieval modeling. A total of 38 vegetation indices (VIs) and 12-point cloud features (PCFs) were extracted from hyperspectral imagery and LiDAR point cloud data, respectively. Pearson correlation analysis and the Shapley Additive Explanations (SHAP) method were integrated to identify and select the most informative VIs and PCFs. The performance of LAI retrieval models built on single-source features (VIs or PCFs) or multi-source feature fusion was evaluated using the coefficient of determination (R²) and root mean square error (RMSE). The main findings are as follows: (1) Multi-source feature fusion significantly improved LAI retrieval accuracy, with the RF model achieving the highest performance (R² = 0.968, RMSE = 0.125). (2) LiDAR-derived structural metrics and hyperspectral-derived vegetation indices were identified as critical factors for accurate LAI retrieval. (3) The feature selection method integrating mean absolute SHAP values (|SHAP| values) with Pearson correlation analysis enhanced model robustness. (4) The intertidal zone exhibited pronounced spatial heterogeneity in the vegetation LAI distribution. Full article

Air pollution is a major factor influencing hospital admissions worldwide, highlighting the need for robust predictive tools to support healthcare planning and public health measures. Machine learning (ML) has been widely employed to simulate the intricate relationships between pollution and health outcomes. This paper examines publications indexed in the Scopus database, from 2010 to 2024 focusing on using ML techniques to forecast outcomes related to air pollution and hospital admissions. A bibliometric study of the 89 identified papers was also conducted to determine dominant research themes, commonly employed methodologies, and the geographical distribution of publications. The results indicate that research activity increased notably after 2020, with the United States of America, China, and Brazil contributing the highest number of publications. Moreover, the findings indicate that approximately 83% of the reviewed research applied predictive models appropriately, suggesting that ML techniques can effectively forecast healthcare outcomes. Random Forest was the most frequently used method (33 studies), followed by Neural Networks (18 studies). Extreme Gradient Boosting (XGBoost) algorithm, although less frequent, showed the highest reported accuracy, with values ranging from 87% to 95%. The most studied pollutants were particulate matter (PM_2.5), nitrogen dioxide (NO₂), and coarse particulate matter (PM₁₀). Demographic and meteorological data were the most frequently used complementary (71% and 65%, respectively), followed by temporal (46%) and socioeconomic factors (20%). The combination of several variable categories not only enhanced understanding of how environmental exposure affects health outcomes but also improved the accuracy and reliability of the reviewed ML models. Full article

The effects of extreme heat events on Surface Urban Heat Island Intensity (SUHII) were investigated in Lecce (southern Italy) during the summer months (June–August) from 2018 to 2025. The analysis began with the identification of heatwave frequency, duration, and intensity using the Warm Spell Duration Index (WSDI), based on a homogenized long-term temperature record, which indicated a progressive increase in persistent extreme events in recent years. High-resolution ECOSTRESS land surface temperature (LST) data were then processed and combined with CORINE Land Cover (CLC) information to examine the thermal response of different urban fabrics, compact residential areas, continuous/discontinuous urban fabric, and industrial–commercial zones. SUHII was derived from each ECOSTRESS acquisition and evaluated across multiple diurnal intervals to assess temporal variability under both normal and WSDI conditions. The results show a consistent diurnal asymmetry: daytime SUHII becomes more negative during WSDI periods, reflecting enhanced rural warming under dry and highly irradiated conditions, despite overall higher absolute LST during heatwaves, whereas nighttime SUHII intensifies, particularly in dense urban areas where higher thermal inertia promotes persistent heat retention. Statistical analyses confirm significant differences between normal and extreme conditions across all classes and time intervals. These findings demonstrate that extreme heat events alter the urban–rural thermal contrast by amplifying nighttime heat accumulation and reinforcing daytime negative SUHII values. The integration of WSDI-derived heatwave characterization with multi-year ECOSTRESS observations highlights the increasing thermal vulnerability of compact urban environments under intensifying summer extremes. Full article

Current assessments of urban green spaces (UGS) rely largely on two-dimensional (2D) indicators, which fail to capture the three-dimensional (3D) structure necessary for evaluating ecological functions and human exposure. Among these, the Normalized Difference Vegetation Index (NDVI) describes top-down canopy greenness from a nadir perspective, whereas the Green View Index (GVI) quantifies vegetation visibility at street level from a pedestrian perspective. Because the relationship between NDVI and GVI remains unclear, multi-indicator assessments become difficult to interpret, limiting their ability to jointly characterize urban greenery. To address these gaps, we develop a synergy framework that integrates remote sensing with street-view images. First, we aligned the observation scales through street-view depth estimation and converted NDVI into fractional vegetation cover (FVC) through nonlinear mapping to unify measurement units. Correlation experiments revealed that the consistency between GVI and FVC was weak across the city (R² = 0.27) but substantially stronger along arterial roads with continuous vegetation (R² = 0.61). On this basis, we design a Green Synergy Index (GSI) that combines FVC and GVI using fractional power-law adjustments and an interaction term to capture their joint effects. Robustness tests indicate that GSI effectively handles extreme or mismatched cases, differentiates greening patterns, and integrates complementary information from nadir and street views without numerical instability. Furthermore, we assess the consistency between GSI and land surface temperature (LST), showing that the proposed index improves explanatory power compared with FVC and GVI alone (by 5.6% and 8.8%, respectively). Application to the study area yields a mean GSI value of 0.44 on a 0–1 scale, with spatial variations closely associated with road geometry and functional zoning. This enables the identification of mismatched canopy and visibility segments and supports targeted, climate-sensitive green infrastructure planning. Full article

Accurate and rapid estimation of aboveground biomass (AGB) in cotton is crucial for precise agricultural management. However, current AGB estimation methods are limited by data homogeneity and insufficient model accuracy, which fail to comprehensively reflect the cotton growth status. This study introduces a novel approach by coupling cotton canopy Soil and Plant Analyzer Development (SPAD) values with multispectral (MS) data to achieve precise estimation of cotton AGB. Two experimental treatments, involving varied nitrogen fertilizer rates and organic manure applications, were conducted from 2022 to 2023. MS data from UAVs were collected across multiple cotton growth stages, while AGB and canopy SPAD values were synchronously measured. Using the coefficient of variation method, SPAD values were coupled with existing vegetation indices to develop a novel vegetation index termed CGSI_VI. Moreover, the applicability of various machine learning algorithms—including Random Forest Regressor (RFR), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Particle Swarm Optimization-XGBoost (PSO-XGBoost), and Particle Swarm Optimization-CatBoost (PSO-CatBoost)—was evaluated for inverting cotton AGB. The results indicated that, compared to the original vegetation indices, the correlation between the improved vegetation index (CGSI_VI) and AGB was enhanced by 13.60% overall, with the CGSI_CIre exhibiting the highest correlation with cotton AGB (R² = 0.87). The overall AGB estimation accuracy across different growth stages, spanning the entire growth period, ranged from 0.768 to 0.949, peaking during the flowering stage. Furthermore, when the CGSI_VI was used as an input parameter in comparisons of different machine learning algorithms, the PSO-XGBoost algorithm demonstrated superior estimation accuracy across the entire growth stage and within individual growth stages. This high-throughput crop phenotyping analysis method enables rapid and accurate estimation. It reveals the spatial heterogeneity of cotton growth status, thereby providing a powerful tool for accurately identifying growth differences in the field. Full article

This study evaluated the applicability of the dual drainage model, Hyper Connected–Solution for Urban Flood (HC-SURF), for real-time urban flood forecasting. The model was applied to the extreme rainfall event of August 2022 in the Sillim and Daerim drainage basins in Seoul. Its accuracy and computational efficiency were quantitatively compared with those of two widely used commercial models, the Personal Computer Storm Water Management Model (PC-SWMM) and InfoWorks Integrated Catchment Modelling (ICM). Accuracy was assessed by measuring spatial agreement with observed inundation trace maps using binary indicators, including the Critical Success Index (CSI), Probability of Detection (POD), and False Alarm Ratio (FAR). Computational efficiency was evaluated by comparing simulation times under identical conditions. In terms of accuracy against observations, HC-SURF achieved CSI values ranging from 0.26 to 0.45, with POD values from 0.37 to 0.81 and FAR values from 0.49 to 0.53 across the two basins. In inter-model comparisons, the model showed high hydraulic consistency, demonstrating CSI values between 0.72 and 0.88, POD between 0.82 and 0.99, and FAR between 0.08 and 0.15. In terms of computational efficiency, HC-SURF reduced calculation times by approximately 9% and 44% compared with InfoWorks ICM and PC-SWMM, respectively, for a 48 h simulation. The model also completed a 6 h rainfall simulation in approximately 8 min, meeting the lead time requirements for rapid urban flood forecasting. Overall, these findings show that HC-SURF effectively balances simulation accuracy with computational efficiency, demonstrating its suitability for real-time urban flood forecasting. Full article

Under the extensive influence of the monsoon climate, droughts in the Yangtze River Basin (YRB) occur frequently and pose a serious threat to grain security. To better understand the evolution and drivers of soil drought, this study employed remote sensing-based soil moisture and atmospheric circulation data from 2000 to 2022. It assessed the spatiotemporal characteristics of soil drought across the YRB and its sub-basins, identified the main mutation points and types, and quantified the relative contributions of climatic and circulation factors. The results show that: (1) the most severe soil drought month occurred in August 2022 (Standardized Soil Moisture Index SSMI = –1.69), with two major mutation points in May 2011 (“decrease to increase”) and June 2019 (“increase to decrease”); (2) drought mutations were mainly categorized as “interrupted decrease” (9 sub-basins) and “increase to decrease” (1 sub-basin), most occurring after 2010; (3) the year 2022 experienced the most severe annual drought (SSMI = –0.94), with extreme drought covering 39.36% of the basin in August; (4) precipitation (PC) was the dominant climatic factor influencing drought (percentage area of significant coherence PASC = 15.48%), while the Interannual Pacific Oscillation (IPO), Pacific Decadal Oscillation (PDO), and Dipole Mode Index (DMI) all showed significant remote-correlation effects, with mean Shapley additive explanations (SHAP) values of 0.138, 0.111, and 0.090, respectively. This study clarifies the spatiotemporal patterns and drivers of soil drought in the YRB, providing a scientific basis for improved drought monitoring and agricultural risk management. Full article

The purpose of this study is to enhance the performance of the mesohabitat model MesoHABSIM by lowering the necessary hydraulic modelling effort. This proof-of-concept study tests an application of the MesoHydraulics model to simulate the hydraulic characteristics of hydromorphological units (HMUs) occurring in a regulated river at different low discharges. In this quantitative approach, hydraulic patterns are transferred from a source site, where depth and velocity distributions were derived from field measurements and a 2D hydrodynamic model, to a target site, where only a single field hydrometric survey was conducted. Instead of modelling changes in individual hydraulic measurement values to estimate hydraulic responses to discharge, the model relies on statistical distributions of these values within HMUs. We were testing whether changes in the distribution of HMU’s and their hydraulics can be transferred between morphologically comparable river sections to serve as a sufficient hydraulic input for mesoscale habitat modelling. The hydrodynamic component of the River2D software (V.0.95a), routinely used in MesoHABSIM, served as a baseline for testing the MesoHydraulic model’s performance and for producing source data for deriving distribution functions. The test was conducted using data from two one-kilometre sites on the upper Oder River (Poland). The model transfers the HMU area distributions, along with corresponding depth and velocity frequency distributions, for a number of flows from one site (the source) to another (the target). The hydraulics at both sites were surveyed under single-discharge conditions. For the source site, the hydrodynamic model was applied to classify the HMU mosaic at three additional discharge stages. At the target reach, the HMU mapping was conducted based on survey data, and statistical frequency functions were used to model distributions of hydraulic patterns at discharges modelled for the source. The hydraulic model’s performance was evaluated at the target reach by comparing simulated hydraulics and HMU patterns with those modelled using River2D. Finally, both models were used to calculate habitat availability for the fish communities, and dissimilarities were observed. The resulting hydraulic distributions were similar, with an average affinity index of 90%. Higher affinity indices were reached at flows close to the measured value, with increasing model disagreement toward flow extremes, most notably for Run and Backwater units. Regardless, habitat models for the fish community were also highly correlated with R² = 0.98 for amounts of suitable habitat and almost identical habitat distribution among the species. Yet, the MesoHydraulics-based model slightly, but consistently, overestimated habitat availability. While the model was tested in a large and regulated river system, its accuracy may vary depending on the natural river morphology. Further research should evaluate modelling uncertainties and their applicability in less-modified water bodies. Full article

The coastal environment is a dynamic system shaped by both natural processes and human activities. In recent decades, increasing anthropogenic pressure and climate change—manifested through sea-level rise and more frequent extreme events—have accelerated coastal retreat, highlighting the need for improved management strategies and standardized tools for coastal risk assessment. Existing approaches remain highly heterogeneous, differing in structure, input data, and the range of factors considered. To address this gap, this study proposes an index-based methodology of general validity designed to quantify coastal erosion risk through the combined analysis of hazard, vulnerability, and exposure factors. The approach was developed for multi-scale and multi-risk applications and implemented across 54 representative sites along the Calabrian coast in southern Italy, demonstrating strong adaptability and robustness for regional-scale assessments. Results reveal marked spatial variability in coastal risk, with the Tyrrhenian sector exhibiting the highest values due to the combined effects of energetic wave conditions and intense anthropogenic pressure. The proposed framework can be easily integrated into open-access GIS platforms to support evidence-based planning and decision-making, offering practical value for public administrations and stakeholders, and providing a flexible, accessible tool for integrated coastal risk management. Full article

Monitoring land use and land cover (LULC) transformations is essential for understanding socio-ecological dynamics. This study assesses structural shifts in Romania’s landscapes between 1990 and 2018 by integrating algorithmic complexity, fractal analysis, and Grey-Level Co-occurrence Matrix (GLCM) texture analysis. Multi-year maps were used to compute Kolmogorov complexity, fractal measures, and 15 GLCM metrics. The measures were compiled into a unified matrix, and temporal trajectories were explored with principal component analysis and k-means clustering to identify inflection points. Informational complexity and Higuchi 2D decline over time, while homogeneity and angular second moment rise, indicating greater local uniformity. A structural transition around 2006 separates an early heterogeneous regime from a more ordered state; 2012 appears as a turning point when several indices reach extreme values. Strong correlations between fractal and texture measures imply that geometric and radiometric complexity co-evolve, whereas large-scale fractal dimensions remain nearly stable. The multi-index approach provides a replicable framework for identifying critical transitions in LULC. It can support landscape monitoring, and future work should integrate finer temporal data and socio-economic drivers. Full article

Against the backdrop of insufficient accuracy and adaptability of satellite precipitation products in complex terrain areas, this study focused on the Min River Basin (MRB) on the eastern edge of the Qinghai–Tibet Plateau. A two-step machine learning fusion framework was established, which integrates precipitation event identification and quantitative intensity estimation in a systematic manner. This framework incorporated 5 precipitation products (PERSIANN-CDR, CMORPH, GSMaP, IMERG, MSWEP), measured data, and environmental variables. The study compared the precipitation estimation performance of Random Forest (RF), Extreme Learning Machine (ELM), eXtreme Gradient Boosting (XGBoost), Bagging, and Double Machine Learning (DML) models, and analyzed the models’ performance under different precipitation intensities and altitudes, as well as their variable sensitivity. The results showed that: (1) DML models outperformed Single Machine Learning (SML) models and original precipitation products, with RF-Bagging being the optimal model. The daily-scale Correlation Coefficient (CC) of RF-Bagging was over 50% higher than that of original products, while the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were reduced by more than 40% and 35%, respectively. (2) For moderate-to-heavy precipitation, the RF-Bagging and RF-RF models maintain a stable Critical Success Index (CSI) of 0.7. In high-altitude regions, their Probability of Detection (POD) approaches 1, and the Heidke Skill Score (HSS) is 30–40% higher than that in mid-altitude areas, significantly outperforming other models and demonstrating strong adaptability to complex terrain. For light precipitation, while the POD values of these two models are comparable to those of other models, their False Alarm Rate (FAR) is reduced by 15–20%, effectively mitigating precipitation false alarms. (3) GSMaP, IMERG, and MSWEP were the core input variables for all models. RF and ELM models were more dependent on environmental variables, while XGBoost and Bagging models relied more on satellite data. This framework can provide technical references for precipitation estimation in complex terrain areas and contribute to watershed water resource management as well as flood prevention and mitigation. Full article

Nowadays, the number of people enduring extreme commuting is increasing, exacerbating traffic problems and harming individual well-being. To quantify the extreme commuting, we propose an extreme commuting severity (ECS) index that combines the number of extreme commuting trips with their specific distances, where a one-way trip with a commuting distance of at least 25 km is regarded as an extreme commuting trip. In Beijing, the ECS index shows substantial spatial variability, with maximum values exceeding 30,000 for origins and 50,000 for destinations, underscoring the severe commuting burden in specific areas. By integrating the geographically weighted random forest (GWRF) with Shapley additive explanations (SHAP), we model both nonlinear effects and spatial heterogeneity in how the built environment shapes extreme commuting. Compared with benchmark models, the proposed GWRF model achieves the highest predictive performance, yielding the largest R² and the lowest absolute and relative indicators across both generation and attraction scenarios. Notably, the GWRF improves explanatory power over the global model by a substantial margin, highlighting the importance of incorporating spatial heterogeneity. SHAP-based global importance results show that residential density (17.58%) is the most influential factor for ECS, whereas in the attraction scenario, company density exhibits the strongest contribution (20.7%), reflecting the strong pull of major employment clusters. Local importance maps further reveal pronounced spatial differences in effect direction and magnitude. For instance, although housing prices have modest global importance, they display clear spatial heterogeneity: they exert the strongest influence on extreme commuting generation within the Fourth Ring Road and around the North Fifth Ring, whereas in the attraction scenario, their effects concentrate in the southern part of the core area. These findings provide new empirical insights into the mechanisms underlying extreme commuting and highlight the need for spatially differentiated planning strategies. Full article

Extreme climates pose increasing threats to ecosystems, particularly in ecologically fragile regions such as the Yellow River Basin (YRB). Leaf area index (LAI) reflects vegetation response to climatic stressors, yet spatiotemporal dynamics of such responses under future climate scenarios remain poorly understood. This study examined LAI responses to extreme climatic factors across the YRB from 2025 to 2065, utilizing Coupled Model Intercomparison Project Phase 6 (CMIP6) outputs under three Shared Socioeconomic Pathways (SSP) scenarios. Partial least squares regression was performed using historical consistency-validated and future scenario LAI data alongside 26 extreme climate indices to identify extreme climate impacts on vegetation dynamics. Time-lag and cumulative effect analyses using Pearson correlation further quantified the potential impacts of extreme climate on future vegetation dynamics. Results indicate that the regionally averaged LAI in the YRB exhibits a consistent increasing trend under all three SSP scenarios, with linear rates of 0.0016–0.0020 yr⁻¹ and the highest values under SSP5-8.5, accompanied by clear scenario-dependent spatial differences in LAI distribution and vegetation response to extreme climates, particularly in the lag and cumulative effects that depend on local hydro-climatic conditions. Partial least squares regression results identified annual total wet-day precipitation, frost days, growing season length, summer days, and ice days as the dominant extreme climate indices regulating LAI variability. In the arid and semiarid Loess Plateau regions, relatively long lag and cumulative effects imply vegetation vulnerability to delayed or prolonged climatic stress, necessitating enhanced soil and water conservation practices. These findings support region-specific ecological conservation and climate mitigation strategies for the YRB and other ecologically vulnerable watersheds. Full article