Search Results (1,220)

Sustainable forest management requires current, territory-wide data, which is difficult to obtain in vast regions like Quebec, Canada. To complement ground inventories and photo-interpretation, the province developed an airborne laser scanning (ALS)-based model that performs well in coniferous stands, but its accuracy in hardwood stands remains untested. This study aims to evaluate the accuracy of the ALS-based prediction of stand basal area and then test new approaches to increase its performance. Airborne LiDAR data from 2011 to 2020 and 12,506 validation plots from sample plots were used. The ALS model accuracy was initially compared across the stand types, revealing lower accuracy in shade-tolerant deciduous stands. Three inputs were found to increase prediction accuracy: proportion of each species basal area in the stand, geographical coordinates, and meteorological data associated with location. Parametric and auto machine learning (AutoML) methods were employed using those inputs to improve accuracy, with AutoML achieving the highest improvement with initial R² of 0.27, 0.47 and 0.54 and after correction R² of 0.31, 0.56 and 0.67, respectively, for shade-tolerant deciduous, shade-intolerant deciduous, and coniferous stand. Even with the advancements made, further improvements will be necessary to consider using an ALS-based model for shade-tolerant deciduous species. Full article

This paper proposes a replicable, city-oriented workflow to support the preliminary screening of photovoltaic (PV) opportunities in Bogotá, Colombia, by integrating (i) georeferenced spatial inventories (roofs/land), (ii) solar-resource modeling based on local meteorological stations and radiation models, and (iii) an optional 3D module (LiDAR/DSM) to refine shading and orientation losses when higher-resolution data are available. Rather than claiming a complete citywide quantification from exhaustive building-level inputs, the workflow is demonstrated through two institutional case studies (public schools) selected to represent contrasting urban morphologies. The results show how the approach consistently transforms spatial constraints and solar estimates into comparable technical and economic indicators for decision-making at the site level. Finally, a practical scale-up pathway is described to extend the same logic from pilots to citywide portfolios through batch processing of urban footprints and the progressive enrichment of inputs—from 2D GIS screening to targeted 3D refinement—while preserving transparency and traceability of assumptions. For the two case study sites, the workflow yielded preliminary PV capacities of 72.6 and 95.0 kWp, with year-1 generation of 90.2 and 115.0 MWh, respectively. The IRR values achieved were between 18.9 and 19.5%, the simple payback period was approximately five years, and the LCOE was between 0.051 and 0.053 USD/kWh. It should be noted that the generation was reported as a central estimate with ±25% tolerance to reflect interannual solar resource variability. Full article

Spring frosts pose a major threat to tea production, causing severe damage to tender spring buds and substantial economic losses. To support timely frost protection measures, this study develops an interpretable machine learning framework for next-day frost forecasting in a tea plantation in Danyang, eastern China. Leveraging nine years (2008–2016) of multi-source data—including high-resolution on-site meteorological observations and daily records from surrounding regional stations—we engineered a comprehensive set of predictive features capturing local microclimatic, regional synoptic, and short-term temporal dynamics. A two-stage feature selection approach, combining Spearman correlation screening with SHAP-based importance ranking, identified an optimal subset of 14 robust predictors. Among eight benchmarked models, XGBoost achieved the best performance on a chronologically held-out test set, yielding a CSI of 0.736, accuracy of 91.0%, F1-Score of 0.848 and AUC-ROC of 0.968. Ablation experiments demonstrated the added value of data integration: model performance improved from a CSI of 0.617 (using only local data) to 0.736 (with full multi-source inputs). SHAP interpretability analysis further revealed that the model’s predictions align with established frost formation physics, highlighting key drivers such as nocturnal cooling rate and regional humidity. This work demonstrates that integrating multi-scale meteorological data with interpretable machine learning offers a reliable, transparent, and operationally viable tool for frost risk management—providing actionable insights to enhance resilience in precision horticulture for perennial crops like tea. Full article

Photovoltaic (PV) power forecasting is strongly influenced by volatility, randomness, and changing meteorological conditions, while conventional point forecasting provides limited uncertainty information for engineering use. This study proposes a hybrid interval forecasting framework for PV prediction. Similar-day clustering first segments weather data into distinct scenarios (sunny, cloudy and overcast) to reduce noise and redundant information within sequences, enhancing stability and thereby providing a more refined feature space for deep learning. A BiLSTM–Transformer model is then used as the core forecaster, taking multiple meteorological variables as multi-feature time-series inputs. BiLSTM captures bidirectional temporal dependencies, and the Transformer enhances long-range feature extraction via attention. To improve robustness and stability, the Alpha Evolution (AE) algorithm is applied for hyperparameter optimization, balancing global exploration and local refinement. For probabilistic forecasting, Adaptive Bandwidth Kernel Density Estimation (ABKDE) is employed to construct prediction intervals, where the local bandwidth is determined by minimizing a local error function to adapt to data density and error distribution. Case studies utilizing a full-year, 5 min high-resolution dataset from the DKASC station demonstrate that the proposed AE-BiLSTM–Transformer achieves highly accurate point forecasts across diverse weather conditions, reducing the RMSE by 81.85%, 76.99%, and 72.26% under sunny, cloudy, and overcast scenarios, respectively, compared to the baseline LSTM. ABKDE further produces reliable and compact intervals; at the 90% confidence level on sunny days, it achieves PICP = 0.921 with PINAW = 0.0378, reducing PINAW by 75.16% relative to conventional KDE while maintaining comparable coverage. Full article

Solar-induced chlorophyll fluorescence (SIF) is an emerging predictor in the crop gross primary production (GPP) estimation for its close relationships with vegetation photosynthesis. Conventional crop GPP are estimated by data-driven models upscaled from eddy covariance flux observations, light-use efficiency (LUE) models, and process-based models, which are constrained by the limited availability of in-site experimental and simulated data. By using vegetation remote sensing data and meteorological data to simulate the combined impacts of changes in vegetation physiological factors and environmental factors on GPP estimation, we proposed a new method to estimate GPP for winter wheat over the North China Plain (NCP) based on the SIF-based mechanistic light response (MLR) model with bias correction. Results showed that (1) vegetation and meteorological factors could be used to fit the bias caused by the static input parameters of the MLR model for winter wheat GPP estimation, which solved the unavailability of the input parameters in the MLR models; (2) the MLR model with bias correction could quickly achieve large-scale crop GPP estimation at the regional scale during the vigorous period of winter wheat, whose performance was superior to that of a traditional statistical regression model with an increased R² of 6.4%. Full article

Groundwater is a critical natural and strategic economic resource, and the accurate prediction of groundwater depth dynamics is essential for the rational development and utilization of water resources. However, under the combined influence of climate variability, human activities, and complex hydrogeological conditions, groundwater level time series exhibit strong nonlinear and non-stationary characteristics, posing great challenges to the accurate prediction of groundwater level dynamics. Most existing prediction models rely on sufficient hydro-meteorological and exploitation data that are difficult to obtain in water-scarce regions, or fail to effectively decouple the multi-scale features of non-stationary groundwater level signals, resulting in limited prediction accuracy and insufficient generalization ability. To address these research gaps, this study takes Zhengzhou, a typical water-deficient city in the Yellow River Basin, as the study area, and proposes a hybrid deep learning framework combining Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM) neural network for predicting shallow and intermediate-deep groundwater level changes. Kolmogorov–Arnold Networks (KANs) and Gated Recurrent Units (GRUs) are selected as benchmark models to verify the superior performance of the proposed framework. In this framework, the non-stationary groundwater level signal is adaptively decomposed into Intrinsic Mode Functions (IMFs) with distinct frequency characteristics via VMD. An independent LSTM model is constructed for each IMF to capture its unique temporal variation pattern, and the final groundwater level prediction is obtained by linearly reconstructing the predicted results of all IMFs. The results show that the coefficient of determination (R²) of the VMD-LSTM model exceeds 0.90 for all monitoring datasets, with low Mean Absolute Error (MAE) and Mean Squared Error (MSE). It significantly outperforms the benchmark models in handling nonlinear and non-stationary time series features. Using only historical groundwater level data as input, the proposed framework effectively overcomes the limitation of insufficient driving variables in data-scarce regions and fully explores the multi-scale evolution of groundwater dynamics through the synergistic effect of multi-scale decomposition and deep learning. The method presented in this study provides a novel and reliable technical approach for groundwater level prediction in water-deficient and data-limited areas, and also offers scientific support for the rational management and sustainable utilization of regional groundwater resources. Future research will incorporate driving factors such as meteorology and exploitation to further improve the model’s ability to capture abrupt changes in groundwater level dynamics. Full article

Cellular automata-based wildfire simulation models are widely used to support fire management, risk assessment, and operational decision-making, due to their efficiency and computational advantages. However, the accuracy of these models heavily depends on the quality of input data provided by the user, including the composition and geospatial extend of forest fuels, current meteorological conditions and terrain information. This publication examines how quantitative and spatial input data uncertainties affect the estimates of the impacted areas. Using a series of simulation experiments, inaccurate data are introduced to specific input variables (such as the vegetation type and the fuel moisture content) to reflect realistic levels of uncertainty commonly observed in operational scenarios, where users with different cognitive backgrounds fail to properly identify key characteristics of a fire. Model outputs are then compared using spatial and temporal performance metrics, including the rate of spread and burned area extent. The results demonstrate that uncertainties in fuel models and meteorological inputs exert a dominant influence on simulated fire behavior. Our findings highlight the sensitivity of wildfire simulations to compounded input uncertainties and stress the need for improved in-field data acquisition strategies. Full article

Decarbonizing the maritime sector requires not only adopting alternative fuels and propulsion technologies but also quantitatively assessing their impacts on coastal and urban air quality. This study develops a stochastic, time-resolved air-quality modelling framework to evaluate ship-related pollutant dispersion in port environments. The approach integrates Automatic Identification System (AIS) trajectories, vessel-specific emission factors, and meteorological inputs within a moving-source Gaussian dispersion model to simulate the spatio-temporal evolution of pollutant concentrations. A 24 h case study for the Ports of Los Angeles and Long Beach demonstrates highly intermittent emission behaviour, with peak aggregated emission rates reaching approximately 1.2 kg/s for CO₂ and 3.8 g/s for SO₂. Temporally integrated concentration fields reveal maximum cumulative dosages of 0.145 g·s/m³ for NO_x, 0.023 g·s/m³ for SO₂, 0.014 g·s/m³ for total PM, and 7.5 g·s/m³ for CO₂ in near-port traffic corridors. Sensitivity analysis indicates that effective emission height variations alter cumulative exposure by up to 17%, whereas temporal resolution changes produce deviations below 7%, confirming numerical stability. Monte Carlo uncertainty propagation demonstrates bounded but non-negligible variability in exposure estimates under realistic emission and wind uncertainties. Results show that cumulative exposure patterns differ substantially from short-term concentration peaks, highlighting the importance of time-integrated and receptor-based metrics for port air quality assessment. The proposed AIS-driven stochastic framework provides a reproducible and computationally efficient tool for evaluating operational mitigation strategies and supporting evidence-based maritime decarbonization pathways. Full article

Deep learning-based flow field prediction for microclimate pollutant dispersion represents an emerging and promising methodology, where effectively integrating meteorological, spatial, and temporal information remains a critical challenge. To address this, we propose a novel non-intrusive reduced-order model (ROM) that synergizes a Dilated Convolutional Autoencoder (DCAE) with pre-trained large language models (LLMs). The DCAE, leveraging nonlinear mapping, was employed for extracting low-dimensional spatiotemporal flow field features. These features were then combined with textual prototypes via text embedding to enable few-shot inference using the LLM-based flow field prediction method. To optimize the utilization of pre-trained LLMs, we designed a specialized textual description template tailored for pollutant dispersion data, which enhances the contextual input of meteorological conditions to guide model predictions. Experimental validation through three-dimensional urban canyon simulations conclusively demonstrated the efficacy of the convolutional autoencoder and LLM-based framework in predicting pollutant dispersion flow fields. The proposed method exhibits remarkable transfer learning capabilities across varying street canyon geometries and meteorological conditions while significantly representing a 9.85× acceleration in prediction compared to Computational Fluid Dynamics (CFD). Full article

To enhance the accuracy of short-term photovoltaic (PV) power forecasting, this study proposes a novel hybrid model that integrates Random Forest (RF), Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Sample Entropy (SE), the Random Walk with Compulsory Evolution (RWCE) algorithm, and the Gated Recurrent Unit (GRU) network. Initially, RF is applied to select relevant meteorological features, minimizing redundancy and improving both training efficiency and predictive robustness under complex operating conditions. ICEEMDAN is then employed to decompose the PV power series into multiple quasi-stationary components, mitigating the adverse effects of non-stationarity on forecasting accuracy. Following this, SE is applied to quantify the complexity of each component and reconstruct the decomposed signals into high-, mid-, and low-frequency bands, simplifying the inputs to the forecasting model. To further improve performance, the RWCE algorithm optimizes GRU network hyperparameters through global exploration, individual evolution, and enforced evolution strategies. The optimized GRU network then predicts each reconstructed component, and the component-wise forecasts are aggregated to yield the final PV power output. Simulation results from several representative months indicate that the proposed approach reduces RMSE by an average of 9.02% compared to comparison model and by 43.41% relative to the baseline model, demonstrating its superior forecasting capability. Additionally, the model demonstrated scalability across varying climate conditions, confirming its applicability in real-world scenarios. Full article

Urban environments face increasing thermal stress from climate change and the Urban Heat Island effect, with significant implications for livability, public health, and energy sustainability. Outdoor thermal comfort is defined as the state in which conditions are perceived as acceptable, depends on interactions among meteorological, morphological, physiological, and behavioral factors. This review synthesizes the application of machine learning (ML) to outdoor thermal comfort assessment into a practice-oriented taxonomy. Research spans diverse climates and urban forms, using inputs across environmental and human domains. Supervised learning dominates. Regression approaches (linear regression, support vector regression, random forest, gradient boosting) and classification algorithms (decision trees, support vector machines, K-nearest neighbors, Naïve Bayes, random forest classifiers) are widely used to predict thermal indices such as the Physiological Equivalent Temperature and Universal Thermal Climate Index, or to classify subjective responses including thermal sensation, comfort, and acceptability. Unsupervised learning (clustering, principal component analysis) supports identification of microclimatic zones and perceptual clusters, while deep learning (multilayer perceptrons, convolutional and recurrent neural networks, generative adversarial networks) achieves superior accuracy for complex, high-dimensional, and spatiotemporal data. Algorithms such as random forests, support vector machines, and gradient boosting consistently show strong performance for both indices and subjective responses when integrating multi-domain inputs. Semi-supervised and reinforcement learning remain underexplored but offer promise for leveraging large-scale sensor data and enabling adaptive, real-time comfort management. The review concludes with a roadmap emphasizing explainable artificial intelligence, scalable surrogate modeling, and integration with simulation-based optimization and parametric design tools. Full article

Tropical cyclones are among the most destructive meteorological systems on Earth. Accurate track forecasting of tropical cyclones remains a core challenge in atmospheric science, and it is of great significance for disaster prevention and mitigation. This study targets the critical limitations of existing tropical cyclone track forecasting models: the insufficient ability to extract non-linear spatiotemporal features from 3D atmospheric circulation fields and the long-standing bottlenecks in multi-source heterogeneous meteorological data fusion. To address these issues, we propose a Dual-Stream Gated Fusion Network (DGF-Net), a high-precision track forecasting method tailored to the Northwest Pacific basin. The proposed framework takes the Best Track dataset and ERA5 Reanalysis Dataset as primary inputs: a Bidirectional Gated Recurrent Unit (Bi-GRU) is adopted to capture the temporal evolution characteristics of 2D tropical cyclone trajectory sequences, and a SpatioTemporal Convolutional Gated Recurrent Unit (STConvGRU) is used to extract complex non-linear features from 3D atmospheric environmental fields. Then, a multimodal fusion module integrating gating and attention mechanism is constructed to achieve deep fusion of cross-dimensional features, which effectively mines the intrinsic physical correlations between tropical cyclone track evolution and environmental driving factors. Comparative experiments based on historical observational datasets of the Northwest Pacific show that DGF-Net achieves superior forecasting performance, with the 6 h, 12 h, and 24 h Great Circle Distance (GCD) errors of 35.62 km, 43.53 km, and 135.49 km, respectively. The results significantly outperform mainstream baseline models, which validates the effectiveness of DGF-Net in feature extraction and multimodal fusion and provides solid technical support for tropical cyclone disaster prevention and operational decision-making. Full article

Accurate modeling of tropospheric effects on GNSS signals is essential for achieving high-precision positioning, as the troposphere can delay pseudorange signals by up to 30 m in Standard Point Positioning applications. While empirical models, such as the Saastamoinen model, are commonly used to simulate tropospheric delay by separating it into the hydrostatic (ZHD) and wet (ZWD) components, these models often lack the realism needed to model the highly variable ZWD accurately. To address this limitation, Safran Electronics & Defense has developed an advanced machine learning-based model to enhance the realism of the unpredicted ZWD simulation within the Skydel-powered GNSS simulators. The model incorporates a feedforward neural network with two hidden layers, integrated with empirical methods for ZHD computation, resulting in a robust hybrid framework. The model is trained on a comprehensive 20-year dataset (2004–2024) collected from 221 GNSS stations worldwide and further refined using meteorological data from Open Meteo to ensure accurate input parameters. This innovative hybrid approach significantly enhances the realism of tropospheric delay modeling for Safran’s Skydel GNSS simulation software (version 24.4). Performance evaluations show a significant reduction in simulation errors across all tested stations, especially under complex and dynamic weather conditions. The paper details the new model’s design, training, and optimization processes, emphasizing the seamless integration of machine learning techniques within the Skydel simulator architecture. By delivering more realistic simulations, this methodology enhances the fidelity of GNSS signal modeling and establishes a new benchmark for the integration of machine learning into reliable GNSS simulators. Full article

Accurate prediction of PM_2.5 concentration is crucial for air quality management and public health protection. However, existing methods often struggle to capture and interpret the nonlinear relationships among multiple atmospheric variables. This study proposes GT-iFormer, a novel interpretable deep learning model that integrates graph convolutional networks (GCNs), Temporal Convolutional Networks (TCNs), and inverted Transformer (iTransformer) for PM_2.5 concentration prediction. The model features a GTCN-Block that encapsulates GCN and TCN with residual-style fusion, preserving feature-level dependencies alongside temporal patterns to prevent information degradation. The Pearson correlation coefficients and KNN algorithm are innovatively integrated to build a data-driven graph structure, which allows GCNs to flexibly model the nonlinear relationships between pollutants and meteorological factors based on observed data. TCNs obtain multi-scale temporal patterns via causal dilated convolutions. Subsequently, the concatenated representations of GTCN-Block are input into iTransformer to model global inter-variable interactions using attention mechanisms along the axis of the variable. We incorporated SHAP (SHapley Additive exPlanations) analysis to expose feature importance and nonlinear relationships with PM_2.5 predictions. Results on the hour-level data of Beijing (2020–2021) and Shenzhen (2021) show that our proposed GT-iFormer surpasses all baseline models, with an RMSE of 8.781 μg/m³ and R² of 0.978 for Beijing, and an RMSE of 3.871 μg/m³ and R² of 0.957 for Shenzhen on single-step prediction, equating to RMSE reductions of 15.75% and 17.92%, respectively, over the best baseline model. The SHAP analysis shows clearly distinct regional patterns, with combustion sources dominant in Beijing (represented by CO at 28.231%), and traffic emissions dominant in Shenzhen (represented by NO₂ at 25.908%). Crucial threshold effects are established for all variables, with significant cross-city differences that can serve as general forecasts and guidance for city-specific air quality management policies. Full article

Lightning is a major natural ignition source of wildfires across forest, grassland, and cropland ecosystems. Accurate prediction of lightning-ignited fire occurrence remains challenging due to uncertainties in spatiotemporal alignment caused by vegetation-dependent smoldering delays and the difficulty of representing heterogeneous fuel conditions in mixed-vegetation regions. This study proposes a semi-automated lightning–fire alignment framework that integrates land cover information and historical fire records to improve spatiotemporal matching across different vegetation types and to reduce misclassification from human-induced fires in agricultural areas. To better characterize fuel conditions, two feature-level vegetation fusion parameters—total vegetation cover and leaf area index weight—are introduced and combined with hourly meteorological variables and lightning characteristics to develop a tuned random forest prediction model. The framework is applied at a regional scale in the Greater Khingan Mountains and southwestern forest regions of China, with predictions conducted at an event-based temporal scale using hourly inputs. The vegetation-fused model achieves an AUC of 0.93, outperforming models without vegetation fusion. Analysis of model outputs indicates that hourly maximum temperature, leaf area index weight, precipitation, and wind speed are key factors influencing lightning-ignited fire occurrence. This study demonstrates the value of semi-automated alignment and vegetation feature fusion for improving lightning-ignited fire prediction in heterogeneous landscapes, supporting regional wildfire risk assessment and potential early-warning applications. Full article