Search Results (621)

In the context of retrofitting existing buildings into nearly zero-energy buildings (NZEBs), in situ assessment methods have proven reliable for evaluating the performance of building components, including glazing systems. However, these methods are often time-consuming, intrusive to occupants, and disruptive to building operations. This study investigates the potential of a machine learning approach—multiple linear regression (MLR)—to predict the dynamic performance of an office building’s glazing system by analyzing surface temperature variations and their impact on nearby thermal comfort. The models were trained using in situ data collected over just two weeks—one in September and one in December—but were applied to predict the glazing performance on multiple other dates with diverse weather conditions. Results show that MLR predictions closely matched nighttime measurements, while some discrepancies occurred during the daytime. Nevertheless, the machine learning model achieved a daytime prediction accuracy of approximately 1.5 °C in terms of root mean square error (RMSE), which is lower than the values reported in previous studies. For thermal comfort evaluation, the MLR model identified the periods with thermal discomfort with an overall accuracy of approximately 92%. However, during periods when the difference between predicted and measured operative temperatures exceeded 1 °C, the thermal comfort predictions showed greater deviation from actual measurements. The study concludes by acknowledging its limitations and recommending a future approach that integrates machine learning with laboratory-based techniques (e.g., hot-box setups and solar simulators) and in situ measurements, together with a broader variety of glazing samples, to more effectively evaluate and enhance prediction accuracy, robustness, and generalizability. Full article

Phenology prediction is critical for optimizing the timing of rice crop management operations such as fertilization and irrigation, particularly in the face of increasing climate variability. This study aimed to estimate three key developmental stages in the temperate irrigated rice systems of Australia: panicle initiation (PI), flowering, and harvest maturity. Extensive and diverse field observations ($n\approx 302$) were collected over four consecutive seasons (2022–2025) from the rice-growing regions of the Murrumbidgee and Murray Valleys in southern New South Wales, encompassing six varieties and three sowing methods. The extent of data available allowed a number of traditional and emerging machine learning (ML) models to be directly compared to determine the most robust strategies to predict Australian rice crop phenology. Among all models, Tabular Prior-data Fitted Network (TabPFN), a pre-trained transformer model trained on large synthetic datasets, achieved the highest precision for PI and flowering predictions, with root mean square errors (RMSEs) of 4.9 and 6.5 days, respectively. Meanwhile, long short-term memory (LSTM) excelled in predicting harvest maturity with an RMSE of 5.9 days. Notably, TabPFN achieved strong results without the need for hyperparameter tuning, consistently outperforming other ML approaches. Across all stages, models that integrated remote sensing (RS) and weather variables consistently outperformed those relying on single-source input. These findings underscore the value of hybrid data fusion and modern time series modeling techniques for accurate and scalable phenology prediction, ultimately enabling more informed and adaptive agronomic decision-making. Full article

Driven by real-world demands of processing massive high-frequency data and achieving longer forecasting horizons in time series forecasting scenarios, a variety of deep learning architectures designed for time series forecasting have emerged at a rapid pace. However, this rapid development actually leads to a sharp increase in parameter size, and the introduction of numerous redundant modules typically offers only limited contribution to improving prediction performance. Although prediction models have shown a trend towards simplification over a period, significantly improving prediction performance, they remain weak in capturing dynamic relationships. Moreover, the predictive accuracy depends on the quality and extent of data preprocessing, making them unsuitable for handling complex real-world data. To address these challenges, we introduced Treeformer, an innovative model that treats the traditional tree-based machine learning model as an encoder and integrates it with a Transformer-based forecasting model, while also adopting the idea of time–feature two-dimensional information extraction by channel independence and cross-channel modeling strategy. It fully utilizes the rich information across variables to improve the ability of time series forecasting. It improves the accuracy of prediction on the basis of the original deep model while maintaining a low computational cost and exhibits better applicability to real-world datasets. We conducted experiments on multiple publicly available datasets across five domains—electricity, weather, traffic, the forex market, healthcare. The results demonstrate improved accuracy, and provide a better hybrid approach for enhancing predictive performance in Long-term Sequence Forecasting (LSTF) problems. Full article

As extreme weather events become more frequent, the icing of transmission lines in winter has become more common, causing significant economic losses to power systems and drawing increasing attention. However, owing to the complexity of the conductor icing process, establishing high-precision ice thickness prediction models is vital for ensuring the safe and stable operation of power grids. Therefore, this paper proposes a hybrid model combining an improved snake optimization (ISO) algorithm, deep extreme learning machine (DELM), and hybrid kernel extreme learning machine (HKELM). Firstly, based on the analysis of the factors that influence the icing, the temperature, the humidity, the wind velocity, the wind direction, and the precipitation are selected as the weather parameters for the prediction model of the transmission line icing. Secondly, the HKELM is introduced into the regression layer of DELM to obtain the deep hybrid kernel extreme learning machine (DHKELM) model for ice thickness prediction. The SO algorithm is then augmented by incorporating the Latin hypercube sampling technique, t-distribution mutation strategy, and Cauchy mutation, enhancing its convergence. Finally, the ISO-DHKELM model is applied to the icing data of transmission lines in Sichuan Province for experiments. The simulation results indicate that this model not only performs well, but also enhances the accuracy of ice thickness predictions. Full article

Flood impacts are intensifying due to the increasing frequency and severity of factors such as severe weather events, climate change, and unplanned urbanization. This study focuses on Briar Creek in Charlotte, North Carolina, an area historically affected by flooding. Three machine learning algorithms —bagging (random forest), extreme gradient boosting (XGBoost), and logistic regression—were used to develop a flood susceptibility model that incorporates topographical, hydrological, and meteorological variables. Key predictors included slope, aspect, curvature, flow velocity, flow concentration, discharge, and 8 years of rainfall data. A flood inventory of 750 data points was compiled from historic flood records. The dataset was divided into training (70%) and testing (30%) subsets, and model performance was evaluated using accuracy metrics, confusion matrices, and classification reports. The results indicate that logistic regression outperformed both XGBoost and bagging in terms of predictive accuracy. According to the logistic regression model, the study area was classified into five flood risk zones: 5.55% as very high risk, 8.66% as high risk, 12.04% as moderate risk, 21.56% as low risk, and 52.20% as very low risk. The resulting flood susceptibility map constitutes a valuable tool for emergency preparedness and infrastructure planning in high-risk zones. Full article

Wildfires are increasing in frequency and severity, with Australia’s 2019–2020 Black Summer burning over 18 million hectares. Accurate prediction of wildfire behavior is essential for effective risk assessment and emergency response. This study presents a machine learning framework for predicting wildfire dynamics across Australia’s seven regions using the IBM wildfire dataset. Various Machine Learning (ML) models were evaluated to forecast three key indicators: Fire Area (km²), Fire Brightness Temperature (K), and Fire Radiative Power (MW). Lasso Regression consistently outperformed the other models, achieving an average RMSE of 0.04201 and R² of 0.29355. Performance varied across regions, with stronger results in areas like New South Wales and Queensland, likely influenced by differences in topography, microclimate, and vegetation. However, limitations include the exclusion of ignition sources such as lightning and human activity, which are critical for capturing the environment accurately and improving predictive accuracy. Future work will integrate these factors alongside more detailed weather and vegetation data. Practical implementation may face challenges related to real-time data availability, system integration, and response coordination, but this approach offers promising potential for operational wildfire decision support. Full article

Accurate identification of microplastic polymers in marine environments is essential for tracing pollution sources, understanding ecological impacts, and guiding mitigation strategies. This study presents a comprehensive, explainable-AI framework that uses Raman spectroscopy to classify pristine and weathered microplastics versus biological materials. Using a curated spectral library of 78 polymer specimens—including pristine, weathered, and biological materials—we benchmark seven supervised machine learning models (Decision Trees, Random Forest, k-Nearest Neighbours, Neural Networks, LightGBM, XGBoost and Support Vector Machines) without and with Principal Component Analysis for binary classification. Although k-Nearest Neighbours and Support Vector Machines achieved the highest single metric accuracy (82.5%), k NN also recorded the highest recall both with and without PCA, thereby offering the most balanced overall performance. To enhance interpretability, we employed SHapley Additive exPlanations, which revealed chemically meaningful spectral regions (notably near 700 cm⁻¹ and 1080 cm⁻¹) as critical to model predictions. Notably, models trained without Principal Component Analysis provided clearer feature attributions, suggesting improved interpretability in raw spectral space. This pipeline surpasses traditional spectral matching techniques and also delivers transparent insights into classification logic. Our findings can support scalable, real-time deployment of AI-based tools for oceanic microplastic monitoring and environmental policy development. Full article

Lightning nowcasting is crucial for ensuring safety and operational continuity in weather-exposed industries such as mining. This study evaluates three machine learning (ML)-based approaches for predicting lightning using dual-polarimetric weather radar data collected in the eastern Amazon, Brazil. The strategies propose advances in literature in three ways by involving (i) grouping radar variables by temperature layers, (ii) statistical summaries at key altitudes, and (iii) analyzing all the 18 levels of reflectivity data combined with Principal Component Analysis (PCA) dimensionality reduction and ensemble models. For each approach, models such as Random Forest, Support Vector Machines, and XGBoost were trained and tested using data from 2021–2022 with class balancing and feature engineering techniques. Among the approaches, the PCA-based ensemble achieved the best generalization (recall = 0.89, F1 = 0.77), while the layer-based method had the highest recall (0.97), and the altitude-based strategy offered a computationally efficient alternative with competitive results. These findings confirm the predictive value of radar-derived features and emphasize the role of feature representation in model performance. Additionally, the best model was integrated into the operational LEWAIS alert system, and four integration strategies were tested. The strategy that combined alerts from both ML and LEWAIS systems reduced the failure-to-warn rate to 0.0531 and increased the lead time to 10.18 min, making it ideal for safety-critical applications. Overall, the results show that ML models based solely on radar inputs can achieve robust lightning nowcasting, supporting both scientific advancement and industrial risk mitigation. Full article

The precise forecasting of renewable energy production and usage is essential for the stability, efficiency, and sustainability of contemporary power systems. This requirement is especially urgent in South Africa, a nation currently grappling with considerable energy issues, such as recurrent load shedding, outdated coal-fired power plants, and an increasing electricity demand. As the country moves towards a more renewable-focused energy portfolio, the capacity to anticipate future energy requirements is crucial for effective planning, operational stability, and grid resilience. This study introduces a hybrid approach that combines deep learning and machine learning techniques, specifically integrating long short-term memory (LSTM) neural networks with extreme gradient boosting (XGBoost) to provide more accurate and detailed forecasts of energy demand. LSTM networks are particularly effective in capturing long-term temporal dependencies in sequential data, such as patterns of energy usage. At the same time, XGBoost delivers high-performance gradient-boosted decision trees that can manage non-linear relationships and noise present in extensive datasets. The proposed hybrid LSTM-XGBoost model was trained and assessed using high-resolution data on energy consumption and weather conditions gathered from a coastal municipality in KwaZulu-Natal, South Africa, a country that exemplifies the convergence of renewable energy potential and challenges related to energy reliability. The preprocessing steps, including normalization, feature selection, and sequence modeling, were implemented to enhance the input data for both models. The performance of the model was thoroughly evaluated using standard statistical metrics, specifically the mean absolute error (MAE), the root mean squared error (RMSE), and the coefficient of determination (R²). The hybrid model achieved an MAE of merely 192.59 kWh and an R² of approximately 0.71, significantly surpassing the performance of the individual LSTM and XGBoost models. These findings highlight the enhanced predictive capabilities of the hybrid model in capturing both temporal trends and feature interactions in energy consumption behavior. Full article

Photovoltaic (PV) modules undergo comprehensive testing to validate their electrical and thermal properties prior to market entry. These evaluations consist of durability and efficiency tests performed under realistic outdoor conditions with natural climatic influences, as well as in controlled laboratory settings. The overall performance of PV cells is affected by several factors, including solar irradiance, operating temperature, installation site parameters, prevailing weather, and shading effects. In the presented study, three distinct PV modules were analyzed using a sophisticated large-scale steady-state solar simulator. The current–voltage (I-V) characteristics of each module were precisely measured and subsequently scrutinized. To augment the analysis, a three-layer artificial neural network, specifically the multilayer perceptron (MLP), was developed. The experimental measurements, along with the outputs derived from the MLP model, served as the foundation for a comprehensive global sensitivity analysis (GSA). The experimental results revealed variances between the manufacturer’s declared values and those recorded during testing. The first module achieved a maximum power point that exceeded the manufacturer’s specification. Conversely, the second and third modules delivered power values corresponding to only 85–87% and 95–98% of their stated capacities, respectively. The global sensitivity analysis further indicated that while certain parameters, such as efficiency and the ratio of V_oc/V, played a dominant role in influencing the power-voltage relationship, another parameter, U, exhibited a comparatively minor effect. These results highlight the significant potential of integrating machine learning techniques into the performance evaluation and predictive analysis of photovoltaic modules. Full article

Climate change poses significant challenges for viticulture, particularly in regions known for producing high-quality wines. Wine quality results from a complex interaction between climatic factors, regional characteristics, and viticultural practices. Methods: This study integrates statistical analysis, machine learning (ML) algorithms, and systems thinking to assess the extent to which wine quality can be predicted using monthly weather data and regional classification. The dataset includes average wine scores, monthly temperatures and precipitation, and categorical region data for Slovenia between 2011 and 2021. Predictive models tested include Random Forest, Support Vector Machine, Decision Tree, and linear regression. In addition, Causal Loop Diagrams (CLDs) were constructed to explore feedback mechanisms and systemic dynamics. Results: The Random Forest model showed the highest prediction accuracy (R² = 0.779). Regional classification emerged as the most influential variable, followed by temperatures in September and April. Precipitation did not have a statistically significant effect on wine ratings. CLD models revealed time delays in the effects of adaptation measures and highlighted the role of perceptual lags in growers’ responses to climate signals. Conclusions: The combined use of ML, statistical methods, and CLDs enhances understanding of how climate variability influences wine quality. This integrated approach offers practical insights for winegrowers, policymakers, and regional planners aiming to develop climate-resilient viticultural strategies. Future research should include phenological phase modeling and dynamic simulation to further improve predictive accuracy and system-level understanding. Full article

Accurately predicting the ionosphere’s Total Electron Content (TEC) is significant for ensuring the regular operation of satellite navigation and communication systems and space weather prediction. To further improve the accuracy of TEC prediction, this paper proposes a TEC prediction model based on the grid-optimized Gate Recurrent Unit (GRU). This model has the following main features: (1) it uses statistical learning methods to interpolate the missing data of TEC observations; (2) it constructs a sliding time window by using the multi-dimensional time series features of two types of solar activity indices to support modeling; (3) It adopts grid search combined with optimization of network depth, time step length, and other hyperparameters to significantly enhance the model’s ability to extract the characteristics of the ionospheric 11-year cycle and seasonal variations. Taking the EGLIN station as an example, the proposed model is verified. The experimental results show that the root mean square error of the GRU model during the period from 2019 to 2020 was 0.78 TECU, which was significantly lower than those of the CCIR, URSI, and statistical machine learning models. Compared with the other three models, the RMSE error of the GRU model was reduced by 72.73%, 72.64%, and 57.38%, respectively. The above research verifies the advantages of the proposed model in predicting TEC and provides a new idea for ionospheric modeling. Full article

The increasing penetration of photovoltaic solar energy has intensified the need for accurate production forecasting to ensure efficient grid operation. This study presents a critical comparison of traditional statistical methods and machine learning approaches for forecasting solar irradiance using the benchmark Folsom PLC dataset. Two primary research questions are addressed: whether machine learning models outperform traditional techniques, and whether time series modelling improves prediction accuracy. The analysis includes an evaluation of a range of models, including statistical regressions (OLS, LASSO, ridge), regression trees, neural networks, LSTM, and random forests, which are applied to physical modelling and time series approaches. The results reveal that although machine learning methods can outperform statistical models, particularly with the inclusion of exogenous weather features, they are not universally superior across all forecasting horizons. Furthermore, pure time series approach models yield lower performance. However, a hybrid approach in which physical models are integrated with machine learning demonstrates significantly improved accuracy. These findings highlight the value of hybrid models for photovoltaic forecasting and suggest strategic directions for operational implementation. Full article

Accurate prediction of airport delays under convective weather conditions is essential for effective traffic coordination and improving overall airport efficiency. Traditional methods mainly rely on numerical weather and traffic indicators, but they often fail to capture the spatial distribution of traffic flows within the terminal area. To address this limitation, we propose a novel image-based representation named Multi-Channel Traffic and Weather Scene Image (MTWSI), which maps both meteorological and traffic information onto a two-dimensional airspace grid, thereby preserving spatial relationships. Based on the MTWSI, we develop a delay prediction model named ADLCNN. This model first uses a convolutional neural network to extract deep spatial features from the scene images and then classifies each sample into a delay level. Using real operational data from Guangzhou Baiyun Airport, this paper shows that ADLCNN achieves significantly higher prediction accuracy compared to traditional machine learning methods. The results confirm that MTWSI provides a more accurate representation of real traffic conditions under convective weather. Full article

Oil spill simulation models are essential for predicting the oil spill behavior and movement in marine environments. In this study, we comprehensively reviewed a large and diverse body of peer-reviewed literature obtained from Scopus and Web of Science. Our initial analysis phase focused on examining trends in scientific publications, utilizing the complete dataset derived after systematic screening and database integration. In the second phase, we applied elements of a systematic review to identify and evaluate the most influential contributions in the scientific field of oil spill simulations. Our analysis revealed a steady and accelerating growth of research activity over the past five decades, with a particularly notable expansion in the last two. The field has also experienced a marked increase in collaborative practices, including a rise in international co-authorship and multi-authored contributions, reflecting a more global and interdisciplinary research landscape. We cataloged the key modeling frameworks that have shaped the field from established systems such as OSCAR, OIL-MAP/SIMAP, and GNOME to emerging hybrid and Lagrangian approaches. Hydrodynamic models were consistently central, often integrated with biogeochemical, wave, atmospheric, and oil-spill-specific modules. Environmental variables such as wind, ocean currents, and temperature were frequently used to drive model behavior. Geographically, research has concentrated on ecologically and economically sensitive coastal and marine regions. We conclude that future progress will rely on the real-time integration of high-resolution environmental data streams, the development of machine-learning-based surrogate models to accelerate computations, and the incorporation of advanced biodegradation and weathering mechanisms supported by experimental data. These advancements are expected to enhance the accuracy, responsiveness, and operational value of oil spill modeling tools, supporting environmental monitoring and emergency response. Full article