Search Results (222)

In wetland ecological monitoring, accurate acquisition of water bodies is particularly crucial, especially for hydrological monitoring and eutrophication control. Water bodies can be clearly delineated by using optical remote sensors. Optical sensors can clearly delineate water boundaries and features when extracting water bodies via remote sensing. Meanwhile, synthetic aperture radar (SAR), with its unique microwave capabilities, can easily penetrate vegetation and operate regardless of weather conditions, enabling all-weather monitoring. Each sensor type exhibits distinct advantages in water body monitoring and research. This study focuses on Caohai Wetland in Guizhou Province, utilizing data from the optical satellite Zhuhai-1 (launched by China in 2017) and the radar satellite RadarSat-2 (launched by Canada) at identical resolutions during the same period. Five supervised classification methods were applied to extract water bodies using optical imagery within the wetland area, with results evaluated against SAR data. Results indicate that the optimal water body extraction methods based on optical and SAR data are Random Forest Classification and Support Vector Machine classification, respectively, achieving an overall accuracy of 0.896 and 0.940, with Kappa coefficients of 0.791 and 0.879. The water area extracted using SAR was significantly larger than that based on optical data, thereby identifying areas within Caohai Wetland that were not fully submerged in vegetation during this period. This study holds significant implications for accurate water body extraction and analysis benefited an improved monitoring and conserving the wetland environment. Full article

Smart cities require effective disaster management (like flooding, solar storms, sandstorms, or hurricanes), as it directly impacts people’s lives. The key challenges of disaster management are timely detection and effective notification during the crisis. This research presents a smart multi-layer framework for notification classification and management before and during flooding disasters. The framework includes an early detection module as the main phase in the alerting process. This step depends on an Ensemble Learning (EL) model based on a triad of the three best selected models (Deep Learning (DL), Random Forest (RF), and K-nearest Neighbor (KNN)) to analyze data collected continuously from the Internet of Things (IoT) layer. In the boosting phase, the framework utilizes Large Language Models (LLMs) with DL to analyze social textual crowdsourcing data. The results will enable the framework to identify the most affected areas during a flood. The framework adds a fog computing layer alongside a cloud layer to enable instantaneous processing of user responses and generate specialized alerts based on contextual factors such as location, time, risk level, alert type, and user characteristics. Through testing and implementation, the proposed algorithms demonstrated an accuracy rate of over 98% in detecting threats using a dataset of real, collected weather and flooding data. Additionally, the framework proposes a centralized control panel and a design of a smartphone application that offers essential services and facilitates communication among managed civil defense teams, citizens, and volunteers. Full article

Due to its fast start and stop, purity, and reliability, hydropower is becoming more important in the overall power dispatch strategy in grids with a high proportion of wind and solar power generation. Therefore, we propose an interval type-2 fuzzy logic-based rainfall classification and fuzzy neural network model to build a 48 h reservoir inflow forecasting system, addressing the challenges of renewable energy instability and extreme weather in hydropower operations. Full article

In order to address the challenges of limited accuracy in identifying non-meteorological clutter and the spatial overlap between meteorological and non-meteorological echoes in cloud radar observations under complex weather conditions, in this study, we propose a quality-control method for cloud-radar data, which integrates machine learning with neighborhood filtering, This quality-control method first uses the Light Gradient Boosting Machine (LightGBM) to initially identify clutter, then employs a customized neighborhood filtering module to optimize and eliminate residual isolated clutter. This two-stage framework combines the strengths of accurate machine-learning-based classification and physically motivated filtering optimization, enabling reliable discrimination between meteorological and non-meteorological echoes. Based on multi-region, long-term and multi-model radar baseline observations, which cover typical complex weather types such as snow, fog, rain, low clouds and dust, the refined manual labeling of meteorological and non-meteorological echoes is carried out, combined with multi-source ground observation data such as surface observations, temperature and humidity. Based on this, a feature training dataset for machine learning is constructed, which contains over 20 million samples. A multi-index evaluation system—including echo classification accuracy and non-meteorological clutter rejection rate—is used to quantitatively assess the quality-control performance of the method in different weather scenarios. The results indicate that the proposed method demonstrates stable performance in typical complex weather scenarios, with comprehensive scores of 90.73 (snow), 94.23 (rain), 96.49 (low clouds), 91.10 (fog) and 95.79 (dust) on a 100-point scale. Through typical case studies and statistical data analysis, the proposed algorithm achieves better quality-control scores in comparison with the Random Forest and single LightGBM algorithms. It provides a new technical approach for cloud-radar data quality control and also offers a theoretical basis for the feature selection of machine-learning-based quality-control models, further enhancing the application value of cloud-radar data in refined meteorological observations. Full article

Monitoring agricultural production in developing nations is essential for assessing food security. Nevertheless, persistent cloud cover in tropical regions severely limits optical satellite observations, and ground-truth data for classification validation are typically unavailable. This study developed a remote sensing methodology to classify agricultural land in southern Uasin Gishu County, Kenya, using weather-independent Synthetic Aperture Radar (SAR) imagery without requiring in situ training data. We processed 29 Sentinel-1 C-band VH-polarized scenes through the Alaska Satellite Facility’s Radiometric Terrain Correction pipeline. We computed the Coefficient of Variation (CV) across the 2017 time series to quantify temporal backscatter variance. VH polarization was selected over VV because a preliminary analysis showed that VV sensitivity to water surface dynamics confounded the CV algorithm. Preprocessing masks excluded water bodies, urban areas, and edge pixels to reduce classification errors from non-agricultural sources of temporal variability. Unsupervised ISO Cluster classification partitioned the CV raster into land-cover classes, and a Python-based statistical analysis determined optimal threshold values. Active agriculture pixels (n = 581,807) exhibited a mean CV of 0.469 (SD = 0.087), while non-agricultural pixels (n = 623,484) showed a mean CV of 0.274 (SD = 0.049). The optimal classification threshold of 0.357, determined by the intersection of fitted normal distributions, achieved an overall accuracy of 87.5% (Kappa = 0.73) when validated against Sentinel-2 reference imagery. User’s accuracy for agriculture was 96.6%, indicating that pixels classified as agricultural were highly reliable, while omission errors reducing producer’s accuracy to 84.6% were primarily attributable to edge pixels and land cover types where preprocessing masks or threshold placement excluded pixels exhibiting intermediate temporal dynamics. The classification identified approximately 810 km² of actively cultivated land (54% of the southern study area), corresponding to an estimated 69,500 to 162,200 metric tonnes (assuming 30–70% maize fraction) of potential maize production based on FAO yield data. The methodology provides a replicable, cost-effective tool for food security monitoring in cloud-prone regions where ground-truth data are unavailable. Full article

The evolution of next-generation urban infrastructure necessitates the deployment of intelligent pavement systems capable of real-time data acquisition, adaptive response, and predictive analytics. This article presents the design, implementation, and performance evaluation of the smart pavement system incorporating multimodal embedded sensors for traffic density analysis, structural health monitoring, and environmental surveillance. SPS integrates piezoelectric transducers, micro-electro-mechanical system accelerometers, inductive loop coils, fiber Bragg grating (FBG) sensors, and capacitive moisture and temperature sensors within the asphalt and sub-base layers, forming a distributed sensor network that interfaces with an edge-AI-enabled data acquisition and control module. Each sensor node performs localized pre-processing using low-power microcontrollers and transmits spatiotemporal data to a centralized IoT gateway over an adaptive mesh topology via long-range wide-area network or 5G-Vehicle-to-Everything protocols. Data fusion algorithms employing Kalman filters, sensor drift compensation models, and deep convolutional recurrent neural networks enable accurate classification of vehicular loads, traffic, and anomaly detection. Additionally, the system supports real-time air pollutant detection (e.g., NO₂, CO, and PM2.5) using embedded electrochemical and optical gas sensors linked to mobile roadside units. Field deployments on a 1.2 km highway testbed demonstrate the system’s capability to achieve 95.7% classification accuracy for vehicle type recognition, ±1.5 mm resolution in rut depth measurement, and ±0.2 °C thermal sensitivity across dynamic weather conditions. Predictive analytics driven by long short-term memory networks yield a 21.4% improvement in maintenance planning accuracy, significantly reducing unplanned downtimes and repair costs. The architecture also supports vehicle-to-infrastructure feedback loops for adaptive traffic signal control and incident response. The proposed SPS architecture demonstrates a scalable and resilient framework for cyber-physical infrastructure, paving the way for smart cities that are responsive, efficient, and sustainable. Full article

Unmanned aerial vehicles (UAVs) increasingly share low-altitude airspace with birds, making early distinguishing between drones and biological targets critical for safety and security. This work addresses long-range scenarios where objects occupy only a few pixels and appearance-based recognition becomes unreliable. We develop a model-driven simulation pipeline that generates synthetic data with a controlled camera model, atmospheric background and realistic motion of three aerial target types: multicopter, fixed-wing UAV and bird. From these sequences, each track is encoded as a time series of image-plane coordinates and apparent size, and a bidirectional long short-term memory (LSTM) network is trained to classify trajectories as drone-like or bird-like. The model learns characteristic differences in smoothness, turning behavior and velocity fluctuations, and to achieve reliable separation between drone and bird motion patterns on synthetic test data. Motion-trajectory cues alone can support early distinguishing of drones from birds when visual details are scarce, providing a complementary signal to conventional image-based detection. The proposed synthetic data and sequence classification pipeline forms a reproducible testbed that can be extended with real trajectories from radar or video tracking systems and used to prototype and benchmark trajectory-based recognizers for integrated surveillance solutions. The proposed method is designed to generalize naturally to real surveillance systems, as it relies on trajectory-level motion patterns rather than appearance-based features that are sensitive to sensor quality, illumination, or weather conditions. Full article

As a complex and destructive natural disaster, the characteristics of typhoons are closely related to human activities, and their accurate categorization is of vital significance for improving disaster warning and management capabilities. This study highlights the key role of typhoon clustering in analyzing typhoon behaviors, aiming to provide reliable support for disaster prevention and control. Based on the NOAA meteorological dataset from 2003 to 2024, this study firstly adopts the K-means clustering algorithm to classify typhoons into seven categories and then utilizes eight machine learning models to train and validate the classification results, and introduces the Shapley’s additive interpretation (SHAP) algorithm to enhance the interpretability of the models. The study data covers a variety of features such as air temperature, wind speed, atmospheric pressure, and weather station observations, etc. After a systematic preprocessing process, a feature matrix containing key variables such as typhoon intensity and moving speed is constructed. The results show that the XGBoost model outperforms others across multiple evaluation metrics (Accuracy: 0.992, Precision: 0.989, Recall: 0.992, F1.5 Score: 0.990), highlighting its exceptional capability in managing complex weather classification tasks. The seven categories of typhoon types classified by K-means exhibit different feature patterns, while the SHAP analysis further reveals the effects of each feature on the classification and its potential interactions. This study not only verifies the effectiveness of K-means combined with machine learning in typhoon classification but also lays a solid scientific foundation for accurate prediction, risk assessment and optimization of management strategies for typhoon disasters through the in-depth analysis of feature impacts. Full article

Heavy precipitation, and its dependence on atmospheric circulation, is one of the most important weather features in Central Europe. The Polish–Czech Sudetes Mountains and their northern foreland are one of the regions where such precipitation, under certain circulation conditions, often results in floods. The main goal of this paper is to examine multiannual changes in seasonal heavy precipitation between 1961–2020 and to assess their relationship with atmospheric circulation. The data were derived from the Polish and Czech meteorological stations, representing various altitudes and geographical regions. For the purposes of the study, several indices were used, including 1-, 3-, and 5-day maximum precipitation, as well as two indices based on the 90th and 95th percentile thresholds. In the analysis concerning atmospheric circulation, the Lityński classification was considered. The results show that the changes in heavy precipitation usually do not indicate homogeneous directions and are strongly affected by applied indices, seasons, and various geographic factors. Those include the northern/southern slope exposition, which significantly determines heavy precipitation under circulation conditions typical for individual seasons. This particularly concerns heavy precipitation for the north and northeast types, which contribute to higher rates of the considered index, especially in the northern part of the mountains. Full article

This study investigates the coupled relationship between groundwater chemistry, lithology, and structural features in the dry zone of Netiyagama, Sri Lanka, within a fractured crystalline basement. Groundwater chemistry fundamentally reflects geological conditions determined by rock-water interactions, we hypothesized that the specific spatial patterns of groundwater chemistry in heterogeneous fractured systems are distinctly controlled by integrated effects of lithological variations, structurally driven flow pathways, aquifer stratification, and geochemical processes, including cation exchange and mineral-specific weathering. To test this, we integrated hydrogeochemical signatures with mapped hydrogeological data and applied multi-stage multivariate analyses, including Piper diagrams, Hierarchical Cluster Analysis (HCA), and Principal Component Analysis (PCA), and various bivariate plots. Piper diagrams identified five distinct hydrochemical facies, but these did not correlate directly with specific rock types, highlighting the limitations of traditional methods in heterogeneous settings. Employing a multi-stage multivariate analysis, we identified seven clusters (C1–C7) that exhibited unique spatial distributions across different rock types and provided a more refined classification of groundwater chemistries. These clusters align with a three-unit aquifer framework (shallow weathered zone, intermittent fracture zone at ~80–100 m MSL, and deeper persistent fractures) controlled by a regional syncline and lineaments. Further analysis through bivariate diagrams revealed insights into dominant weathering processes, cation-exchange mechanisms, and groundwater residence times across the identified clusters. Recharge-type clusters (C1, C2, C5) reflect plagioclase-dominated weathering and short flow paths; transitional clusters (C3, C7) show mixed sources and increasing exchange; evolved clusters (C4, C6) exhibit higher mineralization and longer residence. Overall, the integrated workflow (facies plots + PCA/HCA + bivariate/process diagrams) constrains aquifer dynamics, recharge pathways, and flow-path evolution without additional drilling, and provides practical guidance for well siting and treatment. Full article

The growing global demand for food and energy requires land-use strategies that support agricultural production and renewable energy generation. Agrivoltaic (APV) systems allow farmland to be used for both agriculture and solar power generation. The aim of this study is to critically synthesize the interactions between the key dimensions of APV implementation—technical, agronomic, legal, and economic—in order to create a multidimensional framework for designing an APV optimization model. The analysis covers APV system topologies, appropriate types of photovoltaic modules, installation geometry, shading conditions, and micro-environmental impacts. The paper categorizes quantitative indicators and critical thresholds that define trade-offs between energy production and crop yields, including a discussion of shade-tolerant crops (such as lettuce, clover, grapevines, and hops) that are most compatible with APV. Quantitative aspects were integrated in detail through a review of mathematical approaches used to predict yields (including exponential-linear, logistic, Gompertz, and GENECROP models). These models are key to quantitatively assessing the impact of photovoltaic modules on the light balance, thus enabling the simultaneous estimation of energy efficiency and yields. Technical solutions that enhance synthesis, such as dynamic tracking systems, which can increase energy production by up to 25–30% while optimizing light availability for crops, are also discussed. Additionally, the study examines regional legal frameworks and the economic factors influencing APV deployment, highlighting key challenges such as land use classification, grid connection limitations, investment costs and the absence of harmonised APV policies in many countries. It has been shown that APV systems can increase water retention, mitigate wind erosion, strengthen crop resilience to extreme weather conditions, and reduce the levelized cost of electricity (LCOE) compared to small rooftop PV systems. A key contribution of the work is the creation of a coherent analytical design framework that integrates technical, agronomic, legal and economic requirements as the most important input parameters for the APV system optimization model. This indicates that wider implementation of APV requires clear regulatory definitions, standardized design criteria, and dedicated support mechanisms. Full article

Monitoring civil infrastructure is increasingly critical due to aging assets, urban expansion, and the need for early detection of structural instabilities. Interferometric Synthetic Aperture Radar (InSAR) offers high-resolution, all-weather surface deformation monitoring capabilities, which are being enhanced by recent advances in Deep Learning (DL). Despite growing interest, the existing literature lacks a comprehensive synthesis of how DL models are applied specifically to infrastructure monitoring using InSAR data. This review addresses this gap by systematically analyzing 67 peer-reviewed articles published between 2020 and February 2025. We examine the DL architectures employed, ranging from LSTMs and CNNs to Transformer-based and hybrid models, and assess their integration within various stages of the InSAR monitoring pipeline, including pre-processing, temporal analysis, segmentation, prediction, and risk classification. Our findings reveal a predominance of LSTM and CNN-based approaches, limited exploration of pre-processing tasks, and a focus on urban and linear infrastructures. We identify methodological challenges such as data sparsity, low coherence, and lack of standard benchmarks, and we highlight emerging trends including hybrid architectures, attention mechanisms, end-to-end pipelines, and data fusion with exogenous sources. The review concludes by outlining key research opportunities, such as enhancing model explainability, expanding applications to underexplored infrastructure types, and integrating DL-InSAR workflows into operational structural health monitoring systems. Full article

Buildings account for approximately one-third of global energy usage and associated carbon emissions, making energy benchmarking a crucial tool for advancing decarbonization. Current benchmarking studies have often been limited to mainly the annual scale, relied heavily on simulation-based approaches, or employed regression methods that fail to capture the complexity of diverse building stock. These limitations hinder the interpretability, generalizability, and actionable value of existing models. This study introduces a hybrid AI framework for building energy benchmarking across two time scales—annual and monthly. The framework integrates supervised learning models, including white- and gray-box models, to predict annual and monthly energy consumption, combined with unsupervised learning through neural network-based Self-Organizing Maps (SOM), to classify heterogeneous building stocks. The supervised models provide interpretable and accurate predictions at both aggregated annual and fine-grained monthly levels. The model is trained using a six-year dataset from Washington, D.C., incorporating multiple building attributes and high-resolution weather data. Additionally, the generalizability and robustness have been validated via the real-world dataset from a different climate zone in Pittsburgh, PA. Followed by unsupervised learning models, the SOM clustering preserves topological relationships in high-dimensional data, enabling more nuanced classification compared to centroid-based methods. Results demonstrate that the hybrid approach significantly improves predictive accuracy compared to conventional regression methods, with the proposed model achieving over 80% $R^2$ at the annual scale and robust performance across seasonal monthly predictions. White-box sensitivity highlights that building type and energy use patterns are the most influential variables, while the gray-box analysis using SHAP values further reveals that Energy Star^® rating, Natural Gas (%), and Electricity Use (%) are the three most influential predictors, contributing mean SHAP values of 8.69, 8.46, and 6.47, respectively. SOM results reveal that categorized buildings within the same cluster often share similar energy-use patterns—underscoring the value of data-driven classification. The proposed hybrid framework provides policymakers, building managers, and designers with a scalable, transparent, and transferable tool for identifying energy-saving opportunities, prioritizing retrofit strategies, and accelerating progress toward net-zero carbon buildings. Full article

Climate change is reshaping weather patterns and atmospheric circulation globally, particularly in monsoon-dominated tropical environments. To examine how these changes are unfolding in Bangladesh, we extend the Spatial Synoptic Classification (SSC) using ERA5 reanalysis (1960–2024) at three representative stations (Chittagong, Khulna, and Sylhet) to assess long-term changes in the SSC weather types and their internal meteorological properties. The SSC calendars were constructed and analyzed for seasonal distribution, interannual trends, and decadal anomalies of temperature and dew point. Results reveal that Bangladesh’s climatology is dominated by Moist Tropical (MT), Moist Moderate (MM), and Dry Moderate (DM) weather types with a coherent seasonal cycle. Interannually, MT increased strongly across all stations, while MM and DM declined significantly. Decadal anomalies show consistent warming and moistening since the 2000s, which are most pronounced for Dry Tropical (DT) and MT. These findings indicate that climate change in Bangladesh is expressed not only through shifting frequencies but also through evolving thermodynamic characteristics of daily weather types, underscoring the SSC framework’s value in tropical monsoon regions for generating actionable climate information to support heat-stress planning and climate-health services. Full article

Circulation type classification (CTC) is an important method in atmospheric sciences, which reveals the relationship between atmospheric circulation and regional weather and climate. Accurate circulation classification helps to improve weather forecasting accuracy and supports climate change research. China has complex topography and significant spatiotemporal variability in its circulation patterns, making the study of circulation type classification in this region highly significant. This study aims to evaluate the applicability of several mainstream objective CTC methods in the China region. We applied methods including T-mode principal component analysis (PCT), Ward linkage, K-means, and Self-Organizing Maps (SOM) to classify the sea-level pressure daily mean fields from 1993 to 2023 in the study area, and compared the classification results in terms of internal metrics, continuity, seasonal variation, separability of related meteorological variables (e.g., temperature, precipitation), and stability to spatiotemporal resolution. The results show that each method has its advantages in different contexts, with the K-means method showing the best overall performance. Additionally, an optimized approach combining PCT and K-means is proposed. Full article