Search Results (844)

Wildfires are a major ecological disturbance in Mediterranean ecosystems, affecting vegetation dynamics and landscape resilience. However, the relative importance of environmental factors controlling post-fire vegetation recovery remains insufficiently quantified at regional scales. This study investigates the drivers of vegetation regeneration following 45 large wildfires (>1000 ha) that occurred across Greece between 2017 and 2023. Vegetation recovery was assessed using Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series, while environmental predictors included burn severity metrics, soil moisture at four depth layers derived from the European Centre for Medium-Range Weather Forecasts Reanalysis 5-Land (ERA5-Land) climate reanalysis dataset, terrain characteristics (slope and aspect), land cover, and time since fire. All variables were harmonized at the fire-perimeter scale and analyzed using two complementary modeling approaches: multiple linear regression and artificial neural network (ANN) modeling. The linear regression model explained approximately 38% of the variability in vegetation recovery (R² = 0.38), while the ANN showed improved predictive performance, indicating the presence of complex relationships among predictors. Across the applied modeling approaches, burn severity, topographic conditions, and soil moisture emerged as important drivers of post-fire vegetation recovery. In particular, Soil Moisture Layer 1 (SM₁) showed the strongest positive association with NDVI recovery, followed by Soil Moisture Layer 4 (SM₄), highlighting the importance of water availability for vegetation regeneration under post-fire conditions. Overall, the results confirm that vegetation recovery is strongly controlled by environmental conditions rather than time alone. The findings contribute to a better understanding of post-fire ecosystem dynamics in Mediterranean landscapes and provide a useful framework for supporting wildfire management and restoration planning. Full article

Accurate solar irradiance forecasting is important for efficient planning of solar energy systems, renewable energy integration, and data-driven energy management in smart cities. This becomes more essential in regions with limited measured data availability and varying climatic conditions, where reliable forecasting can support urban energy planning and smart grid operation. Pakistan faces a scarcity of available solar data and has varying climatic conditions, which makes it ideal for such a study. This study utilizes nine geographically diverse stations to develop a benchmark framework for direct one-step-ahead hourly solar irradiance forecasting. The dataset was subjected to data preprocessing, feature engineering, and multi-model evaluation. A staged approach was adopted for feature selection, starting from a base model comprising three input variables: extraterrestrial radiation, solar zenith angle, and relative humidity. Features were added in an incremental order, which resulted in an optimized four-variable input set through the addition of a lagged clearness index to the base model. The forecasting models evaluated in this study, using these input variables, were ANN, NAR, NARX, LSTM, GRU, SARIMA, and Prophet. Deep learning models outperformed the other considered approaches, with LSTM showing the best overall benchmark performance with an average RMSE of 92.93 W/m², MAE of 66.56 W/m², and R-Squared of 0.872. The performance trends were broadly consistent across the evaluated stations, indicating stable behaviour within the adopted dataset and experimental setup. The study shows that a compact and physically interpretable input feature set, used with recurrent deep learning models, provides an effective solution for hourly solar irradiance forecasting, especially in locations with varying climatic conditions. The proposed benchmark can support smart city applications related to distributed solar generation, energy-aware urban planning, and intelligent operation of renewable-rich power systems. Full article

Ground improvement for foundations supported on soft soils is traditionally problematic because of low bearing capacity and a large magnitude of settlement. One sustainable method for mitigating these problems is the use of stone columns (SCs), particularly geosynthetic-encased stone columns (GESCs), to improve load transfer, confinement, and consolidation. This review critically synthesizes recent advances in the analysis and design of SC systems using experimental investigations, numerical simulations, and machine learning (ML)-based methodologies. The article indicates that GESCs, when integrated with modern data-driven techniques, especially hybrid metaheuristic ML models, represent a reliable and sustainable solution for soft soil stabilization. Traditional analytical and empirical methods remain useful; however, they are often inadequate for very soft soils (Undrained shear strength (c_u) < 15 kPa), where excessive bulging and large deformations dominate system behavior. Consequently, intelligent hybrid modeling approaches are emerging as the next generation of optimized, data-driven design tools in geotechnical engineering. Different failure mechanisms of SCs, including bulging, punching shear, and general shear failure, are critically discussed along with the governing design parameters. Previous studies consistently indicate that spacing ratios within the range of s/D = 2–3 can improve the bearing capacity ratio (BCR) by approximately 50–100%. Numerical and experimental studies further demonstrate that SC systems can transfer nearly 60–80% of the applied load through stress concentration and soil arching mechanisms. Furthermore, the application of geosynthetic encasement enhances the performance of SCs in very soft soils by increasing confinement, reducing lateral deformation, and enhancing bearing capacity by nearly 3–6 times compared with ordinary SCs. The review also evaluates the growing role of artificial intelligence techniques in forecasting settlement and bearing capacity behavior. ML techniques such as artificial neural networks (ANN), support vector regression (SVR), random forest (RF), XGBoost, and hybrid metaheuristic–ML models have shown high predictive capability, often achieving prediction errors below 5%. Despite these advancements, many existing ML studies still suffer from limited datasets, a lack of generalization, and insufficient incorporation of physical mechanisms. Full article

Photovoltaic power forecasting is important because solar generation varies with weather conditions. Accurate forecasts help improve grid operation, reduce costs, enhance system stability, and support battery management. This paper presents a hybrid methodology that combines statistical analysis and machine learning to forecast photovoltaic power generation. First, the data are cleaned and preprocessed. Then, the input vector is selected using two criteria: collinearity analysis to remove redundant variables, and Granger causality to identify variables with predictive value in a nonlinear autoregressive with exogenous inputs artificial neural network (NARX-ANN) framework. Next, an experimental design is used to evaluate two training algorithms and activation functions for the hidden layer available in Matlab^® version 26.1.0.3276743 (R2026a Update 3, MathWorks Inc., Natick, MA, USA). The methodology is validated by comparing hundreds of input-variable combinations generated through binomial coefficients. A case study using data from Sonora, Mexico, shows that the best model is the Collinearity–Causality (CC)-NARX-4 model, which uses four input variables, a radial basis function in the hidden layer, and Bayesian regularization backpropagation. This model achieves a root-mean-square error (RMSE) of approximately 132 watts (W) for the forecasting stage/forecasting horizon. The results are also compared with a nonlinear autoregressive (NAR) model to assess the predictive benefit of including exogenous inputs. The final outcome is a robust methodology for improving multivariable neural networks through (i) optimized input-vector selection using collinearity and causality tests, validated by an exhaustive combinatorial algorithm; and (ii) a systematic procedure for configuring the hidden-layer transfer function and the neural network training function. Full article

As the demand for sustainable construction materials grows, wood ash and nanosilica have emerged as promising components for eco-friendly mortars, whose optimization requires advanced analytical techniques capable of capturing their complex linear and nonlinear interactions, making frameworks such as response surface methodology and artificial neural networks essential for effective mix design. This study examines the mechanical performance of eco-friendly mortar incorporating wood ash (WA) as a partial cement replacement and nanosilica solution (NSS) as a strength-enhancing additive, with the aim of optimizing compressive and flexural behaviour. Wood ash was substituted at levels of 5–25%, while NS (0.265 moL⁻¹) was substituted at levels of 0–1.7%. Twenty-one mortar samples were produced and tested at multiple curing ages. Two modelling techniques, response surface methodology (RSM) and artificial neural networks (ANNs), were employed to evaluate the individual and interactive effects of WA and NSS on strength development at curing ages of 28 and 180 days. While RSM provided insight into factor significance and linear interactions, ANN more effectively captured nonlinear behaviour, achieving superior predictive accuracy (R² = 1.000 for 28-day strength). Experimental results revealed that nanosilica substantially enhanced strength up to an optimal dosage of approximately 2.5 g, beyond which performance declined due to particle agglomeration or matrix over-refinement. In contrast, higher WA contents produced strength reductions attributable to dilution effects. Optimization showed that mixtures containing low WA (≤30 g) combined with moderate NSS (2.0–2.5 g) exhibited the highest mechanical performance. Collectively, the findings confirm that ANN-based models outperform RSM and multilinear regression, underscoring their effectiveness for mix design optimization and performance forecasting in sustainable cementitious systems. Full article

Reducing carbon dioxide (CO₂) emissions from buildings is essential for climate change mitigation, with universities representing major energy consumers. This study develops a hybrid data-driven framework combining machine learning and simplified emission factor rescaling to predict campus-wide CO₂ emissions. Nine machine learning models were comparatively evaluated under both cross-sectional and temporal validation settings. Among all evaluated models, the Artificial Neural Network (ANN) demonstrated the most reliable predictive performance, achieving the best balance between prediction accuracy and generalization capability. Although the proposed physics-informed LSBoost_PI framework aimed to integrate physical priors with machine learning through residual correction, it did not improve predictive generalization under the limited sample conditions of the dataset. Time-series cross-validation further confirmed the ANN model’s temporal forecasting capability (RMSE = 2.13 ton/year, R² = 0.985). To support trustworthy and interpretable machine learning, feature importance analysis identified CO₂ intensity indicators (CO₂/kWh and CO₂/TEP) as the dominant drivers of emissions. The study also conducted an emission reduction assessment, revealing that a limited number of high-energy buildings dominate overall campus emissions. These findings provide actionable insights for campus-scale energy management, supporting targeted energy efficiency improvements and renewable energy integration strategies in high-emission buildings. Full article

Accurate long-horizon drought forecasting is essential for water resource management and early warning systems in semi-arid regions. This study evaluates five state-of-the-art Transformer architectures—Vanilla Transformer, Informer, Autoformer, Temporal Fusion Transformer (TFT), and PatchTST—for 24-month forecasting of the Standardized Precipitation Index (SPI-12) across four climatically homogeneous regions of Zacatecas, Mexico (Semi-arid, Highlands, Mountains, and Canyons). Models were trained on monthly precipitation data from 1965–2022 and evaluated on an independent test period (2023–2024) using MAE, RMSE, Pearson correlation, and the Diebold–Mariano test. The results show that PatchTST achieved the best overall performance in three of the four regions, significantly outperforming the other models in most cases. The Vanilla Transformer performed best in the less variable Highlands region. These findings demonstrate that the model’s suitability is strongly dependent on regional climatic characteristics. PatchTST’s patch-based approach proved particularly effective for capturing complex temporal dependencies in highly variable semi-arid environments. This study highlights the potential of Transformer architectures, especially PatchTST, to improve long-horizon SPI forecasting and strengthen operational drought monitoring systems in water-scarce regions. Full article

Decomposition of nonlinear, nonstationary multicomponent signals remains challenging for existing decomposition strategies, including frequency-based, data-driven, and subspace methods, which can suffer from mode mixing, leakage across components, and unreliable isolation of transients. Motivated by this gap, this study proposes Orthogonal Self-Similarity Decomposition (OSSD), which exploits a self-similarity structure in delay-embedded orbit geometry so that temporal organization, rather than spectrum alone, guides component construction. OSSD-Basic introduces three algorithmic novelties within a single pipeline: (1) an adaptive proxy-correlation band merging on the delay axis, (2) a dominant-component cascade that prevents energy-dominant carriers from masking weaker components, and (3) a double MGS + LS reprojection that collapses the inter-mode orthogonality index to numerical zero, regardless of merging and pruning operations. Synthetic experiments with known ground truth show that OSSD-Basic provides a parsimonious four-mode representation with exact inter-mode orthogonality (OI = 9.4 × 10⁻¹⁸), the highest reconstruction SNR among the evaluated baselines (27.14 dB), and the highest ground-truth diagonal correlation sum (3.038) among the tested methods, while using two fewer modes than EMD, VMD, and SSA. Daily streamflow forecasting on a U.S. Geological Survey discharge record further shows that augmenting OSSD-derived inputs with fractal descriptors and fractional-order differencing features yields progressive accuracy gains over the AR-ANN baseline, with R² improving from 0.855 to 0.915 at one-step-ahead and from 0.388 to 0.699 at four-step-ahead forecasting in the single-input setting, within a single-station case study on USGS 01554000. Overall, OSSD-Basic offers an interpretable multiscale decomposition with guaranteed inter-mode orthogonality and a structured feature pathway for oscillatory–transient mixtures. Full article

The present paper suggests a federated learning-based distributed solar forecasting model based on gated recurrent unit (GRU) networks (FL-GRU) to smart buildings in Muscat, Oman. The growing adoption of rooftop photovoltaic (PV) systems in urban settings needs precise, privatizing, and scalable forecasting models able to manage geographically dispersed and statistically heterogeneous data. The suggested solution will include federated learning and GRU networks to train a global forecasting model across several smart buildings and avoid the exchange of raw energy data to overcome these challenges. The local GRU models are trained on local PV generation data and only parameters of the model are relayed to a central aggregation server. This provides privacy of data without compromising the effectiveness of collaborative learning. The proposed framework is tested in a variety of realistic scenarios such as scalability analysis, non-identically distributed (non-IID) data, client dropout, communication constraints, seasonal variability, and privacy saving noise injection. Simulation outcomes show that the proposed FL-GRU model presents a final RMSE of 0.129, MAE of 0.100 and forecasting accuracy of 97%. When increasing the number of clients involved in the process, 2 to 10, RMSE decreases to 0.129, which supports the high scalability advantages. In non-IID scenarios, RMSE ranges between 0.129 and 0.167, and even with half of the clients dropping, the system is robust with an RMSE of 0.172. The proposed FL-GRU is better than the benchmark models, Local GRU, centralized GRU, FL-LSTM, and FL-ANN with a maximum improvement of 22.29% in RMSE reduction. Also, the best predictive consistency is found with correlation analysis with R² = 0.957. On the whole, the suggested approach can offer an efficient, privacy-aware, and scalable solution to distributed solar energy prediction in smart cities. Full article

The emergence of Artificial Neural Networks (ANNs) and their deep learning form called Artificial Intelligence (AI) opened a new path to improve energy efficiency and the indoor environment. A small collaborating network team is now extending the passive house approach, in a book entitled Retrofitting, the Energy and Environment of Buildings (Gruyter Publishers), and presenting generalized AI modeling in the following paper. This concept uses a long-term neural network with a short-term memory (LSTM) and three stages (training, validation, and test) for optimalization to hourly data collected for one full year. The non-residential buildings are less affected by the space occupants. This paper examines the feasibility of a uniform, climate modified technology, as our objective is to create a universal and affordable approach to buildings assisting in slowing the rate of climate change. Hence, the idea of creating a generalized neural network for predicting electricity consumption linked with weather conditions was born. This network is to forecast the electricity consumption for buildings linked to the local weather conditions, but different categories of buildings are put together in one set. While this will lower the large set precision, still our question is if such a network would work. If so, in the future we will create multi-variant, local residential systems with the capability of predicting energy use. Full article

A novel double-criterion early stopping pruning strategy is introduced in this study. The proposed approach enables the structural optimization of a hybrid filtering framework that integrates FeedForward Neural Networks with an adaptive Extended Kalman Filter, by simultaneously monitoring the validation error and the trace of the error covariance matrix. Unlike classical pruning methods, which are applied after the completion of the training process and aggressively remove network neurons, the proposed scheme exploits the learning procedure, achieving a more selective reduction of 2% to 13%, balancing effectively between strong generalization performance and computationally efficient training. The proposed framework is evaluated on wind speed forecasts obtained from a numerical weather prediction model, within a time-varying window scheme, demonstrating promising improvements. Key statistical indices, such as the Mean Absolute Error and the Root Mean Square Error, were significantly reduced, with reductions ranging from approximately 65% to 80% and 60% to 78%, respectively. These findings suggest that the proposed methodology offers a robust and accurate framework for time series forecasting in operational settings. Full article

Artificial intelligence (AI) has become a powerful tool for machine-learning-based forecasting from available data. This study evaluates several artificial neural network (ANN) architectures and the traditional multiple linear regression (MLR) method to predict the compressive strength of steel-fiber-reinforced concrete (SFRC). The input parameters considered in the models included electrical resistivity, concrete age, water-to-cement ratio (w/c), and cement content. Fifty-four concrete mixes were designed by varying the w/c ratio (0.45, 0.50 and 0.60), the nominal maximum size of the coarse aggregate (1″, 3/4″ and 1/2″) and the type of metallic fiber (Sika^® Fiber CHO 65/35 [F1] and Sika^® Fiber CHO 80/60 [F2]). Cylindrical specimens were cured in accordance with ASTM C31 and tested at 7, 14, and 28 days. Compressive strength was determined in accordance with ASTM C39. Electrical resistivity was measured at 7, 14 and 28 days using the Wenner method. Using this dataset, six ANN architectures were trained and the multiple linear regression (MLR) equation was calculated using Matlab R2018a software. The ANN models outperformed the MLR approach in predictive accuracy. Optimal performance was achieved with a three-layer ANN comprising 50 neurons in the first hidden layer, 20 in the second, and a single output neuron. The activation functions used were f(s) = tanh(s) for the first two layers and g(s) = s for the third layer. This ANN architecture achieved a correlation coefficient (R) of 0.98157 and the lowest error metrics, reported as percentages: mean absolute error (MAE), mean absolute percentage error (MAPE), mean squared error (MSE), and root mean squared error (RMSE) of 2.37%, 2.52%, 0.124%, and 3.52%, respectively. These findings demonstrate that ANN models can accurately predict the compressive strength of metal fiber reinforced concrete from electrical resistivity measurements and the variables mentioned above. Full article

Predictive modeling using artificial neural networks (ANN) has drawn a lot of interest as a quick, dependable, and non-destructive method for assessing food qualities. In order to forecast the thermal conductivity and thermal diffusivity of a beverage made from buttermilk sweetened with date syrup from the Khlass type, this study developed an ANN model. We looked into the effects of date syrup concentration (5, 10, and 15%), storage cooling temperature (0, 5, 10, 15 °C), and storage duration (0, 3, 6, 9, 12, and 15 days). The findings of the experiment showed that while higher date syrup concentrations and storage temperatures led to higher values of these attributes, longer storage times decreased both thermal conductivity and thermal diffusivity. Thermal diffusivity was between 1.317 × 10⁻⁷ and 2.247 × 10⁻⁷ m²/s, while thermal conductivity was between 0.533 and 0.632 W/m K. Using a trial-and-error method, the best ANN architecture was found to include three input neurons, one hidden layer with twenty neurons, and two output neurons. With mean absolute errors of 1.80 × 10⁻³ W/m K and 1.7 × 10⁻⁹ m²/s for thermal conductivity and thermal diffusivity, respectively, using the testing points, the model shows excellent forecast accuracy. According to sensitivity analysis, the most significant factor influencing both thermal properties was storage duration. Full article

Smart power management practices are needed for a sustainable EV charging infrastructure due to the fast use of renewable energy resources. An innovative power management structure for a small grid-connected solar PV system-based AC and DC charging station, combined with a backup purpose battery energy system (BESS), is demonstrated in this paper’s study. The sustainability transition is associated with integrating renewable energy resources with a battery storage system, providing a helpful solution for managing large power-demanding entities (EV, microgrid, etc.). In this study, a solar PV system takes 500 datasets (based on data availability or to prevent overfitting) of PV voltage, solar irradiance, and air temperature, and the performance of controlling for the maximum power point tracker by training these datasets using Levenberg–Marquardt (LM), which was implemented in the ANN toolbox and created this technique in MATLAB 2016 or Simulink. Also, using this technique for the estimation and forecasting of the datasets of solar PV systems and EVs obtains better results for achieving further targets. To enhance decision-making capability through optimized technique, we have to find it before forecasting PV power generation and EV datasets throughout the day (24 h). The optimized power flows among solar PV power generation, EV charging demand (including AC charging and DC fast charging), the BESS, and the utility/small grid under several priority operating scenarios. A famous technique for optimization, mixed-integer linear programming (MILP), is applied. In this technique, the objective function is used for the solution of problem formation and compliance with system constraints such as the power balancing equation, charging/discharging limits, SOC limits, and grid export/import exchange limits: basically, equality, inequality, and bounds limits. Optimized results show that the coordinated power flow operations are consented to by EV users, by prioritizing some key points, such as solar PV use at the maximum, reducing the grid power dependency, and the first power flow towards EV charging demand. The verified MILP-based solutions boost the maximum utilization of renewable energy resources, feasible EV charging demand, and scaling power flow among these entities. The key contribution of this study is suitable for different powered EV charging stations based on both AC and DC, with different ratings of EVs (including fast and slow charging). Most solar PV-based generation supports the EVCS and backup for ranking-wise BESS, and grid support for the EVCS. Also, the key contribution of hybrid techniques in this article is divided into two stages: in the first stage, an artificial neural network (ANN) is utilized for estimating the PV voltage at the maximum point and forecasting, while in the second stage, mixed-integer linear programming (MILP) employs optimal power management. Full article

Accurate forecasts of tropical cyclone (TC) track and intensity with a sufficient lead time are critical for disaster preparedness and risk mitigation. Traditional numerical weather prediction models, while fundamental to operational forecasting, often exhibit systematic errors due to limitations in observations, physical parameterizations, and model resolution. In recent years, machine learning (ML) and deep learning (DL) approaches have emerged as promising data-driven alternatives for improving TC forecasts. This study presents a comparative evaluation of six ML and DL models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN)—for forecasting TC track and intensity in the North Atlantic basin. The models are trained using the National Hurricane Center’s (NHC) HURDAT2 best-track dataset for storms from 1990 to 2019 and evaluated on an independent test set from the 2020 season. Model performance is compared across all models and benchmarked against the 2020 mean Decay-SHIFOR5 intensity error, CLIPER5 track errors, and the NHC official forecast (OFCL) errors. Forecast skill is assessed using mean absolute error (MAE) with 95% bootstrap confidence intervals and the coefficient of determination ($R^2$) across lead times of 6, 12, 18, 24, 48, and 72 h. The results show that: (1) several ML and DL models achieve intensity forecast performance that is broadly comparable in magnitude to the 2020 mean OFCL benchmarks, with an average error reduction of 5–11% at the 24 h lead time; (2) among the ML models, XGBoost and CatBoost slightly outperform LightGBM and RF in accuracy, while LightGBM demonstrates the highest computational efficiency; and (3) among the DL models, CNNs outperform ANNs in predictive accuracy and intensity forecasting efficiency, while ANNs exhibit lower computational cost for track forecast. Bootstrap confidence intervals indicate relatively low variability in model errors, supporting the statistical stability of the results within the 2020 season. However, these results reflect within-season variability and do not necessarily generalize across different years or climatological conditions. Overall, the findings demonstrate the potential of ML/DL-based approaches to complement existing operational forecast systems and enhance TC track and intensity forecasting in the North Atlantic basin. Full article