Search Results (4,481)

Accurate mapping of marine aquaculture areas is critical for environmental management and sustainable development for marine ecosystem protection and sustainable resource utilization. However, remote sensing imagery based on single-sensor modalities has inherent limitations when extracting aquaculture zones in complex marine environments. To address this challenge, we constructed a multi-modal dataset from five Chinese coastal regions using cloud detection methods and developed Multi-modal Spatial–Frequency Adaptive Fusion Network (MSAFNet) for optical-radar data fusion. MSAFNet employs a dual-path architecture utilizing a Multi-scale Dual-path Feature Module (MDFM) that combines CNN and Transformer capabilities to extract multi-scale features. Additionally, it implements a Dynamic Frequency Domain Adaptive Fusion Module (DFAFM) to achieve deep integration of multi-modal features in both spatial and frequency domains, effectively leveraging the complementary advantages of different sensor data. Results demonstrate that MSAFNet achieves 76.93% mean intersection over union (mIoU), 86.96% mean F1 score (mF1), and 93.26% mean Kappa coefficient (mKappa) in extracting floating raft aquaculture (FRA) and cage aquaculture (CA), significantly outperforming existing methods. Applied to China’s coastal waters, the model generated 2020 nearshore aquaculture distribution maps, demonstrating its generalization capability and practical value in complex marine environments. This approach provides reliable technical support for marine resource management and ecological monitoring. Full article

From 2011 to 2021, marine and brackish water aquaculture production in the Mediterranean and Black Seas increased by 91.3% in volume and 74.5% in value, primarily due to the rise in finfish marine aquaculture. In the Aegean Sea, a significant aquaculture hotspot, Greece and Turkey lead in fish farm numbers and production volume. This study uses Google Earth satellite imagery to map and analyze fish farming cages along the Aegean Sea, comparing findings with the EMODnet dataset. By cataloging fish farm cages along the Greek and Turkish coastlines, we identified 4729 cages in Greece and 2349 in Turkey, with Turkey’s cages occupying a larger area (1.64 km²) than Greece (1.35 km²) due to their larger average size. The analysis revealed significant discrepancies between satellite-derived data and EMODnet records, particularly along the Greek coastline, highlighting gaps in existing datasets. Our findings underscore the need for improved marine spatial planning and management as well as for consistent data collection to support sustainable aquaculture. Full article

Total alkalinity (TAC) plays a pivotal role in buffering acid–base fluctuations and maintaining pH stability in aquatic ecosystems. This study presents a data-driven approach to model TAC using decision tree regression, applied to a comprehensive dataset of 454 water samples collected in diverse aquatic environments of the Médéa region, Algeria. Twenty physicochemical parameters, including concentrations of bicarbonates, hardness, major ions, and trace elements, were analyzed as input features. The decision tree algorithm was optimized using the Dragonfly metaheuristic algorithm coupled with 5-fold cross-validation. The optimized model (DT_DA) demonstrated exceptional predictive performance, with a correlation coefficient R of 0.9999, and low prediction errors (RMSE = 0.3957, MAE = 0.3572, and MAPE = 0.4531). External validation on an independent dataset of 68 samples confirmed the model’s robustness (R = 0.9999; RMSE = 0.4223; MAE = 0.3871, and MAPE = 0.4931). The tree structure revealed that total hardness (threshold: 78.5 °F) and bicarbonate concentration (threshold: 421.68 mg/L) were the most influential variables in TAC determination. The model offers not only accurate predictions but also interpretable decision rules, allowing the identification of critical physicochemical thresholds that govern alkalinity. These findings provide a valuable tool for anticipating pH instability and guiding water quality management and protection strategies in freshwater ecosystems. Full article

Waste stabilization ponds (WSPs) in humid, subtropical climates rely on stable temperatures and mechanical aeration to promote microbial activity. These critical infrastructures can lack operational resources to ensure efficient treatment, which can impact downstream communities. This study aims to use remote water quality sensor data to establish trends in a yearly dataset and correlate various water quality parameters for simplistic identification of pond health. A facultative WSP was monitored in two stages: the primary settling over a period of 14 months to evaluate partially treated water, and the secondary treatment pond for a period of 11 months to monitor final stage water quality parameters. A statistical analysis was performed on the measured parameters (dissolved oxygen, temperature, conductivity, pH, turbidity, nitrate, and ammonium) to establish a comprehensive yearly, seasonal, and monthly dataset to show fluctuations in water parameter correlations. Standard relationships in dissolved oxygen, conductivity, pH, and temperature were traced during the seasonal fluctuations, which provided insight into nitrogen processing by microbial communities. During this study, the summer period showed the most variability, specifically a deviation in the dissolved oxygen and temperature relationship from a yearly moderate negative correlation (−0.593) to a moderate positive correlation (0.459), indicating a direct relationship. The secondary treatment pond data showed more nitrogen species correlation, which can indicate final cycling during seasonal transitions. Understanding pond dynamics can lead to impactful, proactive operational decisions to address pond imbalance or chemical dosing for final treatment. By establishing parameter correlations, facilities with WSPs can strategically integrate sensor networks for real-time pond health and treatment efficiency monitoring during seasonal fluctuations. Full article

One-part geopolymers (OPG) offer a low-carbon alternative to Portland cement, yet mix design remains largely empirical. This study couples machine learning with SHAP (Shapley Additive Explanations) to quantify how mix and curing factors govern performance in Ca-containing OPG. We trained six regressors—Random Forest, ExtraTrees, SVR, Ridge, KNN, and XGBoost—on a compiled dataset and selected XGBoost as the primary model based on prediction accuracy. Models were built separately for four targets: compressive strength at 3, 7, 14, and 28 days. SHAP analysis reveals four dominant variables across targets—Slag, Na₂O, Ms, and the water-to-binder ratio (w/b)—while the sand-to-binder ratio (s/b), temperature, and humidity are secondary within the tested ranges. Strength evolution follows a reaction–densification logic: at 3 days, Slag dominates as Ca accelerates C–(N)–A–S–H formation; at 7–14 days, Na₂O leads as alkalinity/soluble silicate controls dissolution–gelation; by 28 days, Slag and Na₂O jointly set the strength ceiling, with w/b continuously regulating porosity. Interactions are strongest for Slag × Na₂O (Ca–alkalinity synergy). These results provide actionable guidance: prioritize Slag and Na₂O while controlling w/b for strength. The XGBoost+SHAP workflow offers transparent, data-driven decision support for OPG mix optimization and can be extended with broader datasets and formal validation to enhance generalization. Full article

There has been significant interest in deploying unmanned aerial vehicles (UAVs) for their ability to perform precise and rapid remote mapping and inspection of critical environmental assets for structural health monitoring. This case study investigates the use of UAV-based LiDAR and photogrammetry at Melbourne Water’s Western Treatment Plant (WTP) to routinely monitor high-density polyethylene floating covers on anaerobic lagoons. The proposed approach integrates LiDAR and photogrammetry data to enhance the accuracy and efficiency of generating digital elevation models (DEMs) and orthomosaics by leveraging the strengths of both methods. Specifically, the photogrammetric images were orthorectified onto LiDAR-derived DEMs as the projection plane to construct the corresponding orthomosaic. This method captures precise elevation points directly from LiDAR, forming a robust foundation dataset for DEM construction. This streamlines the workflow without compromising detail, as it eliminates the need for time-intensive photogrammetry processes, such as dense cloud and depth map generation. This integration accelerates dataset production by up to four times compared to photogrammetry alone, while achieving centimetre-level accuracy. The LiDAR-derived DEM achieved higher elevation accuracy with a root mean square error (RMSE) of 56.1 mm, while the photogrammetry-derived DEM achieved higher in-plane accuracy with an RMSE of up to 35.4 mm. An analysis of cover deformation revealed that the floating cover had elevated rapidly within the first two years post-installation before showing lateral displacement around the sixth year, which was also evident from a significant increase in wrinkling. This approach delivers valuable insights into cover condition that, in turn, clarifies scum accumulation and movement, thereby enhancing structural integrity management and supporting environmental sustainability at WTP by safeguarding methane-rich biogas for renewable-energy generation and controlling odours. The findings support the ongoing collaborative industry research between Monash University and Melbourne Water, aimed at achieving comprehensive structural and prognostic health assessments of these high-value assets. Full article

Levees are critical for flood defence, but their integrity is threatened by hazards such as piping and seepage, especially during high-water-level periods. Traditional manual inspections for these hazards and associated emergency response elements, such as personnel and assets, are inefficient and often impractical. While UAV-based remote sensing offers a promising alternative, the effective fusion of multi-modal data and the scarcity of labelled data for supervised model training remain significant challenges. To overcome these limitations, this paper reframes levee monitoring as an unsupervised anomaly detection task. We propose a novel, fully automated framework that unifies geophysical hazards and emergency response elements into a single analytical category of “abnormal targets” for comprehensive situational awareness. The framework consists of three key modules: (1) a state-of-the-art registration algorithm to precisely align infrared and visible images; (2) a generative adversarial network to fuse the thermal information from IR images with the textural details from visible images; and (3) an adaptive, unsupervised segmentation module where a mean-shift clustering algorithm, with its hyperparameters automatically tuned by Bayesian optimization, delineates the targets. We validated our framework on a real-world dataset collected from a levee on the Pajiang River, China. The proposed method demonstrates superior performance over all baselines, achieving an Intersection over Union of 0.348 and a macro F1-Score of 0.479. This work provides a practical, training-free solution for comprehensive levee monitoring and demonstrates the synergistic potential of multi-modal fusion and automated machine learning for disaster management. Full article

Climate change research is increasingly important in regions vulnerable to extreme hydrometeorological events like droughts, which pose significant socio-economic and environmental challenges. This study examines future variability of meteorological drought in northernmost South America using the Standardized Precipitation Index (SPI) and precipitation projections from CMIP6 models. We first evaluated model performance by comparing historical simulations with observational data from the Climate Hazards Group InfraRed Precipitation with Station dataset for 1981–2014. Among the models, CNRM-CM6-1-HR was selected for its superior accuracy, demonstrated by the lowest errors and highest correlation with observed data—specifically, a correlation coefficient of 0.60, a normalized root mean square error of 1.08, and a mean absolute error of 61.37 mm/month. Under SSP1-2.6 and SSP5-8.5 scenarios, projections show decreased rainfall during the wet months in the western Perijá mountains, with reductions of 3% to 26% between 2025 and 2100. Conversely, the Sierra Nevada of Santa Marta is expected to see increases of up to 33% under SSP1-2.6. During dry months, northern Colombia and Venezuela—particularly coastal lowlands—are projected to experience rainfall decreases of 10% to 17% under SSP1-2.6 and 13% to 20% under SSP5-8.5. These areas are likely to face severe drought conditions in the mid and late 21st century. These findings are essential for guiding water resource management, enabling adaptive strategies, and informing policies to mitigate drought impacts in the region. Full article

Land use patterns are a critical driver of changes in carbon emissions, making it essential to elucidate the relationship between regional carbon emissions and land use types. As a nationally designated economic strategic zone, the Yangtze River Basin encompasses megacities, rapidly developing medium-sized cities, and relatively underdeveloped regions. However, the mechanism underlying the interaction between landscape patterns and carbon emissions across such gradients remains inadequately understood. This study utilizes nighttime light, land use and carbon emissions datasets, employing XGBoost, CatBoost, LightGBM and a stacking ensemble model to analyze the impacts and driving factors of land use changes on carbon emissions in the Yangtze River Basin from 2002 to 2022. The results showed: (1) The stacking ensemble learning model demonstrated the best predictive performance, with a coefficient of determination (R²) of 0.80, a residual prediction deviation (RPD) of 2.22, and a root mean square error (RMSE) of 4.46. Compared with the next-best models, these performance metrics represent improvements of 19.40% in R² and 28.32% in RPD, and a 22.16% reduction in RMSE. (2) Based on SHAP feature importance and Pearson correlation analysis, the primary drivers influencing CO₂ net emissions in the Yangtze River Basin are GDP per capita (GDPpc), population density (POD), Tertiary industry share (TI), land use degree comprehensive index (LUI), dynamic degree of water-body land use (K_water), Largest patch index (LPI), and number of patches (NP). These findings indicate that changes in regional landscape patterns exert a significant effect on carbon emissions in strategic economic regions, and that stacked ensemble models can effectively simulate and interpret this relationship with high predictive accuracy, thereby providing decision support for regional low-carbon development planning. Full article

Poor-quality milk and the use of chemicals in it can lead to serious health problems, including various diseases and, in some cases, even death for those who consume it. In our society, using such products or contaminated milk that contains chemicals or is of bad quality, often with water or other adulterants, is very common. Based on previous research and existing models, we have improved the process to better and more accurately predict milk quality by using a voting system. This system uses four different algorithms: KNN (K Nearest Neighbour), Naïve Bayes, Random Forest, and Decision Tree. We applied these models to a dataset with almost 1000 samples. To enhance performance, we used brute-force feature selection and a voting process to make accurate decisions. All these procedures were implemented in RapidMiner Studio, resulting in an overall accuracy of 99.69%. Full article

Heavy rainfall events in the southern Anhui region are the main meteorological disasters, often leading to floods and secondary disasters. This article explores the mechanisms supporting extreme precipitation by studying the spatiotemporal characteristics of heavy rainfall events during 2022–2024 and their related atmospheric circulation patterns. Using high-resolution precipitation data, ERA5 and GDAS reanalysis datasets, and the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model analysis, the main sources and transport pathways of water that cause heavy rainfall in the region were determined. The results indicate that large-scale circulation systems, including the East Asian monsoon (EAM), the Western Pacific subtropical high (WPSH), the South Asian high (SAH), and the Tibetan Plateau monsoon (PM), play a decisive role in regulating water vapor flux and convergence in southern Anhui. Southeast Asia, the South China Sea, the western Pacific, and inland China are the main sources of water vapor, with multi-level and multi-channel transport. The uplift effect of mountainous terrain further enhances local precipitation. The Indian Ocean basin mode (IOBM) and zonal index are also closely related to the spatiotemporal changes in rainfall and disaster occurrence. The rainstorm disaster risk assessment based on principal component analysis, the information entropy weight method, and multiple regression shows that the power index model fitted by multiple linear regression is the best for the assessment of disaster-causing rainstorm events. The research results provide a scientific basis for enhancing early warning and disaster prevention capabilities in the context of climate change. Full article

Maintaining functional rural community water supply is a persistent challenge across Sub-Saharan Africa, particularly in Liberia. This study examined the determinants of hand pump functionality in Liberia using a comprehensive dataset from the Liberian Government. We analyzed 11,065 Afridev hand pumps using regression and Bayesian network models. Water points managed by local and institutional entities had substantially higher odds of being functional than those with no management (adjusted OR 3.73 and 2.89), while WASH committees showed a smaller increase (OR 2.43). Pump part damage significantly reduced functionality (undamaged vs. damaged, OR: 10.46. Faster repair was an important determinant, with odds of functionality up to 6.37 times higher. The availability of a trained mechanic with a modest toolkit modestly improved odds (OR 1.25), and proximity to spare parts suppliers played a role (second quartile vs. farthest quartile, OR 1.57). We quantified the impact of service delivery: posterior odds that a water point is functional under the most ideal conditions were four times those under the least ideal conditions. These findings underscore the importance of effective management and prompt repairs to maintain the functionality of water infrastructure. These insights can guide improvements in Liberia and other regions facing similar challenges. Full article

This study investigates groundwater salinization in a section of a coastal aquifer in Rio Grande do Norte, Brazil, using frequency-domain electromagnetic (FDEM) measurements. With the global expansion of shrimp farming in ecologically sensitive coastal regions, there is an urgent need to assess associated risks and promote sustainable management practices. A key concern is the prolonged flooding of shrimp ponds, which accelerates saltwater infiltration into surrounding areas. To better delineate salinization plumes, we analyzed direct groundwater salinity measurements from 14 wells combined with 315 subsurface apparent conductivity measurements obtained using the FDEM method. Correlating these datasets improved the accuracy of salinity mapping, as evidenced by reduced variance in kriging interpolation. By integrating hydrogeological, hydrogeochemical, and geophysical approaches, this study provides a comprehensive characterization of groundwater salinity in the study area. Hydrogeological investigations delineated aquifer properties and flow dynamics; hydrogeochemical analyses identified salinity levels and water quality indicators; and geophysical surveys provided spatially extensive conductivity measurements essential for detecting and mapping saline intrusions. The combined insights from these methodologies enable a more precise assessment of salinity sources and support the development of more effective groundwater management strategies. Our findings demonstrate the effectiveness of integrating geophysical surveys with hydrogeological and hydrogeochemical data, confirming that shrimp farm ponds are a significant source of groundwater contamination. This combined methodology offers a low-impact, cost-effective approach that can be applied to other coastal regions facing similar environmental challenges. Full article

Seasonal fires in northern Thailand are a persistent environmental and public health concern, yet existing fire probability mapping approaches in Thailand rely heavily on subjective multi-criteria analysis (MCA) methods and temporally static data aggregation methods. To address these limitations, we present a flexible, replicable, and operationally viable seasonal fire probability mapping methodology using a Random Forest (RF) machine learning model in the Google Earth Engine (GEE) platform. We trained the model on historical fire occurrence and fire predictor layers from 2016–2023 and applied it to 2024 conditions to generate a probabilistic fire prediction. Our novel approach improves upon existing operational methods and scientific literature in several ways. It uses a more representative sample design which is agnostic to the burn history of fire presences and absences, pairs fire and fire predictor data from each year to account for interannual variation in conditions, empirically refines the most influential fire predictors from a comprehensive set of predictors, and provides a reproducible and accessible framework using GEE. Predictor variables include both socioeconomic and environmental drivers of fire, such as topography, fuels, potential fire behavior, forest type, vegetation characteristics, climate, water availability, crop type, recent burn history, and human influence and accessibility. The model achieves an Area Under the Curve (AUC) of 0.841 when applied to 2016–2023 data and 0.848 when applied to 2024 data, indicating strong discriminatory power despite the additional spatial and temporal variability introduced by our sample design. The highest fire probabilities emerge in forested and agricultural areas at mid elevations and near human settlements and roads, which aligns well with the known anthropogenic drivers of fire in Thailand. Distinct areas of model uncertainty are also apparent in cropland and forests which are only burned intermittently, highlighting the importance of accounting for localized burning cycles. Variable importance analysis using the Gini Impurity Index identifies both natural and anthropogenic predictors as key and nearly equally important predictors of fire, including certain forest and crop types, vegetation characteristics, topography, climate, human influence and accessibility, water availability, and recent burn history. Our findings demonstrate the heavy influence of data preprocessing and model design choices on model results. The model outputs are provided as interpretable probability maps and the methods can be adapted to future years or augmented with local datasets. Our methodology presents a scalable advancement in wildfire probability mapping with machine learning and open-source tools, particularly for data-constrained landscapes. It will support Thailand’s fire managers in proactive fire response and planning and also inform broader regional fire risk assessment efforts. Full article

Protection of coastal cultural heritage is among the most urgent global priorities, as these sites face increasing threats from climate change, sea level rise, and human activity. This study emphasises the value of innovative geospatial tools and data ecosystems for timely risk assessment. The role of land administration systems, geospatial documentation of coastal cultural heritage sites, and the adoption of innovative techniques that combine various methodologies is crucial for timely action. The coastal management infrastructure in Greece is presented, outlining the key public authorities and national legislation, as well as the land administration and geospatial ecosystems and the various available geospatial ecosystems. We profile the Hellenic Cadastre and the Hellenic Archaeological Cadastre along with open geospatial resources, and introduce TRIQUETRA Decision Support System (DSS), produced through the EU’s Horizon project, and a Digital Twin methodology for hazard identification, quantification, and mitigation. Particular emphasis is given to the role of Digital Twin technology, which acts as a continuously updated virtual replica of coastal cultural heritage sites, integrating heterogeneous geospatial datasets such as cadastral information, photogrammetric 3D models, climate projections, and hazard simulations, allowing for stakeholders to test future scenarios of sea level rise, flooding, and erosion, offering an advanced tool for resilience planning. The approach is validated at the coastal archaeological site of Aegina Kolona, where a UAV-based SfM-MVS survey produced using high-resolution photogrammetric outputs, including a dense point cloud exceeding 60 million points, a 5 cm resolution Digital Surface Model, high-resolution orthomosaics with a ground sampling distance of 1 cm and 2.5 cm, and a textured 3D model using more than 6000 nadir and oblique images. These products provided a geospatial infrastructure for flood risk assessment under extreme rainfall events, following a multi-scale hydrologic–hydraulic modelling framework. Island-scale simulations using a 5 m Digital Elevation Model (DEM) were coupled with site-scale modelling based on the high-resolution UAV-derived DEM, allowing for the nested evaluation of water flow, inundation extents, and velocity patterns. This approach revealed spatially variable flood impacts on individual structures, highlighted the sensitivity of the results to watershed delineation and model resolution, and identified critical intervention windows for temporary protection measures. We conclude that integrating land administration systems, open geospatial data, and Digital Twin technology provides a practical pathway to proactive and efficient management, increasing resilience for coastal heritage against climate change threats. Full article