Search Results (36)

Aiming at the nonlinear and non-stationary characteristics of wind speed series, this study proposes a hybrid forecasting framework that integrates Variational Mode Decomposition (VMD), Sparrow Search Algorithm (SSA), and Long Short-Term Memory (LSTM) networks. First, VMD is employed to adaptively decompose the original wind speed series into multiple Intrinsic Mode Functions (IMFs) with distinct frequency features, thereby reducing the non-stationarity of the original sequence. Second, SSA is utilized to adaptively optimize key parameters of the LSTM network, including the number of hidden units, learning rate, and dropout rate, to enhance the model’s capability in capturing complex temporal patterns. Finally, the predictions from all modal components are integrated to generate the final wind speed forecast. Experimental results based on 10-min resolution measured data from a coastal wind farm in southeastern China in June 2020 show that the model achieves a Root Mean Square Error (RMSE) of 0.208, a Mean Absolute Error (MAE) of 0.161, and a Mean Absolute Percentage Error (MAPE) of 3.284% on the test set, with its comprehensive performance significantly surpassing benchmark models such as LSTM, VMD-LSTM, MLP, XGBoost, and ARIMA. The limitations of this study mainly include the use of only one month of data for validation, the lack of segmented performance analysis across different wind speed regimes, and a fixed prediction horizon of 10 min. The results indicate that the proposed hybrid forecasting framework provides an effective approach with practical engineering potential for ultra-short-term wind power prediction. Full article

Forest fires pose escalating threats to ecological security and public safety in Guangdong Province. This study presents a novel machine learning framework for fire occurrence prediction by synergistically integrating multi-source geospatial data. Utilizing Moderate-resolution Imaging Spectroradiometer (MODIS) active fire detections from 2014 to 2023, we quantified historical fire patterns and incorporated four categories of predisposing factors: meteorological variables, topographic attributes, vegetation characteristics, and anthropogenic activities. Spatiotemporal clustering dynamics were characterized via kernel density estimation and spatial autocorrelation analysis. An XGBoost classifier, hyperparameter-optimized through the Sparrow Search Algorithm (SSA), achieved a predictive accuracy of 90.4%, with performance evaluated through precision, recall, and $F_1$-score. Risk zoning maps generated from predicted probabilities were validated against independent fire records from 2019 to 2024. Results reveal pronounced spatial heterogeneity, with high-risk zones concentrated in northern and western mountainous areas, constituting 29% of the provincial territory. Critical driving factors include slope gradient, proximity to roads and rivers, temperature, population density, and elevation. This robust predictive framework furnishes a scientific foundation for spatially-explicit fire prevention strategies and optimized resource allocation in key high-risk jurisdictions, notably Qingyuan, Shaoguan, Zhanjiang, and Zhaoqing. Full article

Accurate prediction of the mechanical and rheological properties of self-compacting concrete (SCC) is critical for mixture design and engineering decision-making; however, conventional empirical approaches often struggle to capture the coupled nonlinear relationships among mixture variables. To address this challenge, this study develops an integrated and interpretable hybrid machine learning (ML) framework by coupling three ML models (RF, XGBoost, and SVR) with five metaheuristic optimizers (SSA, PSO, GWO, GA, and WOA), and by incorporating SHAP and partial dependence (PDP) analyses for explainability. Two SCC datasets with nine mixture parameters are used to predict 28-day compressive strength (CS) and slump flow (SF). The results show that SSA provides the most stable hyperparameter optimization, and the best-performing SSA–RF model achieves test R² values of 0.967 for CS and 0.958 for SF, with RMSE values of 2.295 and 23.068, respectively. Feature importance analysis indicates that the top five variables contribute more than 80% of the predictive information for both targets. Using only these dominant features, a simplified SSA–RF model reduces computation time from 7.3 s to 5.9 s and from 9.7 s to 6.1 s for the two datasets, respectively, while maintaining engineering-level prediction accuracy, and the SHAP and PDP analyses provide transparent feature-level explanations and verify that the learned relationships are physically consistent with SCC mixture-design principles, thereby increasing the reliability and practical applicability of the proposed framework. Overall, the proposed framework delivers accurate prediction, transparent interpretation, and practical guidance for SCC mixture optimization. Full article

Accurate estimation of flow discharge, Q, through hydraulic structures such as spillways and gates is of great importance in water resources engineering. Each hydraulic structure, due to its unique characteristics, requires a specific and comprehensive study. In this regard, the present study innovatively focuses on predicting Q through Rectangular Top-Hinged Gates (RTHGs) using advanced Gradient Boosting (GB) models. The GB models evaluated in this study include Categorical Boosting (CatBoost), Histogram-based Gradient Boosting (HistGBoost), Light Gradient Boosting Machine (LightGBoost), Natural Gradient Boosting (NGBoost), and Extreme Gradient Boosting (XGBoost). One of the essential factors in developing artificial intelligence models is the accurate and proper tuning of their hyperparameters. Therefore, four powerful metaheuristic algorithms—Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Sparrow Search Algorithm (SSA), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA)—were evaluated and compared for hyperparameter tuning, using LightGBoost as the baseline model. An assessment of error metrics, convergence speed, stability, and computational cost revealed that SSA achieved the best performance for the hyperparameter optimization of GB models. Consequently, hybrid models combining GB algorithms with SSA were developed to predict Q through RTHGs. Random split was used to divide the dataset into two sets, with 70% for training and 30% for testing. Prediction uncertainty was quantified via Confidence Intervals (CI) and the R-Factor index. CatBoost-SSA produced the most accurate prediction performance among the models (R² = 0.999 training, 0.984 testing), and NGBoost-SSA provided the lowest uncertainty (CI = 0.616, R-Factor = 3.596). The SHapley Additive exPlanations (SHAP) method identified h/B (upstream water depth to channel width ratio) and channel slope, S, as the most influential predictors. Overall, this study confirms the effectiveness of SSA-optimized boosting models for reliable and interpretable hydraulic modeling, offering a robust tool for the design and operation of gated flow control systems. Full article

During the construction of deep foundation pits, closely monitoring the deformation of the foundation pit retaining structure is of vital importance for ensuring the stability and safety of the foundation pit and reducing the risk of structural damage caused by foundation pit deformation. While theoretical and numerical methods exist for displacement prediction, their practical application is often hindered by the complex, non-linear nature of soil behavior and the numerous influencing parameters involved, making direct calculation methods challenging for real-time prediction and control. To address this, this study proposes a novel and interpretable machine learning framework for modeling both vertical and horizontal displacements in foundation pit engineering. Six widely used machine learning algorithms—Decision Tree (DT), Random Forest (RF), Extremely Randomized Trees (ET), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGBM)—were developed and compared. To improve model performance, the Sparrow Search Algorithm (SSA) was employed for hyperparameter optimization, leading to the creation of hybrid models such as SSA-XGB and SSA-LGBM. The SSA-optimized XGBoost (SSA-XGB) model achieved superior performance, with R² values of 0.988 and 0.990 for vertical and horizontal displacement prediction, respectively, alongside the lowest RMSE (0.785 and 5.684) and MAE (0.562 and 2.427). Notably, the study also found that hyperparameter tuning does not consistently enhance model performance; in some cases, simpler baseline models such as unoptimized ET performed better in noisy environments. Furthermore, SHAP-based interpretability analysis revealed a strong mutual dependency between vertical and horizontal displacements: horizontal displacement was the most influential feature in predicting vertical displacement, and vice versa. Overall, the proposed SSA-XGB model offers a reliable, cost-effective, and interpretable tool for excavation-induced displacement prediction. Full article

During the peak load period, there is a high level of imbalance between power supply and demand, which has become a critical challenge, leading to higher operational costs for power grids. To improve the accuracy of peak load forecasting, this study introduces a novel approach based on Extreme Gradient Boosting Trees (XGBoost) and Multiple Linear Regression (MLR) for daily peak load prediction. The proposed methodology first employs an improved version of the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) algorithm to decompose the raw load data, subsequently reconstructing each Intrinsic Mode Function (IMF) into high-frequency and stationary components. For the high-frequency components, XGBoost serves as the base predictor within a Bagging-based ensemble structure, while the Sparrow Search Algorithm (SSA) is employed to optimize hyperparameters automatically, ensuring efficient learning and accurate representation of complex peak load fluctuations. Meanwhile, the stationary components are modeled using MLR to provide fast and reliable estimations. The proposed framework was evaluated using actual daily peak load data from Western Inner Mongolia, China. The results indicate that the proposed method successfully captures the peak characteristics of the power grid, delivering both robust and precise predictions. When compared to the baseline model, the RMSE and MAPE are reduced by 54.4% and 87.3%, respectively, underscoring its significant potential for practical applications in power system operation and planning. Full article

To mitigate wind power intermittency effects on forecasting accuracy, this study proposes a novel ultra-short-term prediction method based on improved variational mode decomposition (IVMD) and multi-scale feature extraction. First, the maximum information coefficient identified meteorological features strongly correlated with wind power, such as wind speed and wind direction, thereby reducing model input dimensionality. Permutation entropy then served as the fitness function for the sparrow search algorithm (SSA), enabling adaptive IVMD parameter optimization for effective decomposition of non-stationary sequences. The resulting intrinsic mode functions and key meteorological features were input into a prediction model integrating a temporal convolutional network (TCN) and bidirectional gated recurrent unit (BiGRU) to capture global trends and local fluctuations. The SSA was reapplied to optimize TCN-BiGRU hyperparameters, enhancing adaptability. Simulations using operational data from a Xinjiang wind farm demonstrated that the proposed method achieved a coefficient of determination (R²) of 0.996, representing an absolute increase of 0.060 over the XGBoost benchmark (R² = 0.936). This confirms significant enhancement of ultra-short-term forecasting accuracy. Full article

Precise monitoring of the leaf area index (LAI) and soil–plant analysis development (SPAD, which represents chlorophyll content) at the field level is crucial for enhancing crop yield and formulating agricultural management strategies. Currently, most studies use multispectral sensors mounted on unmanned aerial vehicles (UAVs) to obtain images, whereby the spectral information is utilized to estimate rice growth parameters. Considering the cost of multispectral sensors and factors influencing rice growth parameters, this study integrated satellite remote sensing images with UAV visible-light images to obtain high-resolution multispectral images during key rice growth stages, thereby determining the rice LAI and SPAD on the same day. The vegetation indices and textural features most correlated with rice LAI and SPAD were selected using Pearson correlation analysis, and based on vegetation indices, textural features, and their combinations, regression models were established. The results indicate the following: (1) The fusion of satellite and UAV images, combined with spectral information and textural features, can significantly improve the estimation accuracy of LAI and SPAD compared to using only spectral information or textural features. (2) Sparrow search algorithm-optimized extreme gradient boosting (SSA-XGBoost) regression achieved the highest accuracy, with R² and RMSE of 0.904 and 0.183 in LAI estimation and 0.857 and 0.882 in SPAD estimation, respectively. This demonstrates that integrating satellite and UAV images, combined with vegetation indices and texture features, can effectively establish rice LAI and SPAD estimation models, using the SSA-optimized XGBoost method, as an effective and feasible solution for precise monitoring of rice growth parameters. Full article

Due to the rapid proliferation of social media and online reviews, the accurate identification and classification of controversial texts has emerged as a significant challenge in the field of natural language processing. However, traditional text-classification methodologies frequently encounter critical limitations, such as feature sensitivity and inadequate generalization capabilities. This results in a notably suboptimal performance when confronted with diverse controversial content. To address these substantial limitations, this paper proposes a novel controversial text-detection framework based on stacked ensemble learning to enhance the accuracy and robustness of text classification. Firstly, considering the multidimensional complexity of textual features, we integrate comprehensive feature engineering, i.e., encompassing word frequency, statistical metrics, sentiment analysis, and comment tree structure features, as well as advanced feature selection methodologies, particularly lassonet, i.e., a neural network with feature sparsity, to effectively address dimensionality challenges while enhancing model interpretability and computational efficiency. Secondly, we design a two-tier stacked ensemble architecture, which not only combines the strengths of multiple machine learning algorithms, e.g., gradient-boosted decision tree (GBDT), random forest (RF), and extreme gradient boosting (XGBoost), with deep learning models, e.g., gated recurrent unit (GRU) and long short-term memory (LSTM), but also implements the support vector machine (SVM) for efficient meta-learning. Furthermore, we systematically compare three hyperparameter optimization algorithms, including the sparrow search algorithm (SSA), particle swarm optimization (PSO), and Bayesian optimization (BO). The experimental results demonstrate that the SSA exhibits a superior performance in exploring high-dimensional parameter spaces. Extensive experimentation across diverse topics and domains also confirms that our proposed methodology significantly outperforms the state-of-the-art approaches. Full article

Air pollution presents significant risks to both human health and the environment. This study uses air pollution and meteorological data to develop an effective deep learning model for hourly PM_2.5 concentration predictions in Tehran, Iran. This study evaluates efficient metaheuristic algorithms for optimizing deep learning model hyperparameters to improve the accuracy of PM_2.5 concentration predictions. The optimal feature set was selected using the Variance Inflation Factor (VIF) and the Boruta-XGBoost methods, which indicated the elimination of NO, NO₂, and NO_x. Boruta-XGBoost highlighted PM₁₀ as the most important feature. Wavelet transform was then applied to extract 40 features to enhance prediction accuracy. Hyperparameters and weights matrices of the Echo State Network (ESN) model were determined using metaheuristic algorithms, with the Salp Swarm Algorithm (SSA) demonstrating superior performance. The evaluation of different criteria revealed that the ESN-SSA model outperformed other hybrids and the original ESN, LSTM, and GRU models. Full article

To address the issue of aliasing between weak signals and sea clutter, we have developed a weak signal detection method leveraging High-Frequency Energy Ratio (HFER) features. This feature detection approach significantly enhances the detection performance of weak signals against the backdrop of sea clutter. By thoroughly examining the echo characteristics that distinguish clutter range gates from target range gates, we transition the analysis from the observation domain to the feature domain, thereby achieving effective discrimination between the two. We analyze the distribution characteristics of high-frequency IMF energy ratios following CEEMD decomposition and construct a weak signal detection network using XGBoost, with the energy ratio as the key feature. The hyperparameters of the network are optimized using the Sparrow Search Algorithm (SSA). We conducted a comparative analysis using the BCD, RAA, TIE, SVM, and multi-feature fusion detection methods. The experimental results showed that the detection probability of the proposed method can reach over 95%, significantly improving the sea surface monitoring and target tracking capabilities of sea radar. Full article

Geological hazards in Southern Sichuan have become increasingly frequent, posing severe risks to local communities and infrastructure. This study aims to predict the spatial distribution of potential geological hazards using machine learning models and ArcGIS-based spatial analysis. A dataset comprising 2700 known geological hazard locations in Yibin City was analyzed to extract key environmental and topographic features influencing hazard susceptibility. Several machine learning models were evaluated, including random forest, XGBoost, and CatBoost, with model optimization performed using the Sparrow Search Algorithm (SSA) to enhance prediction accuracy. This study produced high-resolution susceptibility maps identifying high-risk zones, revealing a distinct spatial pattern characterized by a concentration of hazards in mountainous areas such as Pingshan County, Junlian County, and Gong County, while plains exhibited a relatively lower risk. Among different hazard types, landslides were found to be the most prevalent. The results further indicate a strong spatial overlap between predicted high-risk zones and existing rural settlements, highlighting the challenges of hazard resilience in these areas. This research provides a refined methodological framework for integrating machine learning and geospatial analysis in hazard prediction. The findings offer valuable insights for rural land use planning and hazard mitigation strategies, emphasizing the necessity of adopting a “small aggregations and multi-point placement” approach to settlement planning in Southern Sichuan’s mountainous regions. Full article

The Fourth Industrial Revolution (4IR) has significantly impacted healthcare, including sexually transmitted infection (STI) management in Sub-Saharan Africa (SSA), particularly among key populations (KPs) with limited access to health services. This review investigates 4IR technologies, including artificial intelligence (AI) and machine learning (ML), that assist in diagnosing, treating, and managing STIs across SSA. By leveraging affordable and accessible solutions, 4IR tools support KPs who are disproportionately affected by STIs. Following systematic review guidelines using Covidence, this study examined 20 relevant studies conducted across 20 SSA countries, with Ethiopia, South Africa, and Zimbabwe emerging as the most researched nations. All the studies reviewed used secondary data and favored supervised ML models, with random forest and XGBoost frequently demonstrating high performance. These tools assist in tracking access to services, predicting risks of STI/HIV, and developing models for community HIV clusters. While AI has enhanced the accuracy of diagnostics and the efficiency of management, several challenges persist, including ethical concerns, issues with data quality, and a lack of expertise in implementation. There are few real-world applications or pilot projects in SSA. Notably, most of the studies primarily focus on the development, validation, or technical evaluation of the ML methods rather than their practical application or implementation. As a result, the actual impact of these approaches on the point of care remains unclear. This review highlights the effectiveness of various AI and ML methods in managing HIV and STIs through detection, diagnosis, treatment, and monitoring. The study strengthens knowledge on the practical application of 4IR technologies in diagnosing, treating, and managing STIs across SSA. Understanding this has potential to improve sexual health outcomes, address gaps in STI diagnosis, and surpass the limitations of traditional syndromic management approaches. Full article

Potato is a major food crop in China. Its development and nutritional state can be inferred by the content of chlorophyll in its canopy. However, the existing study on applying feature extraction and optimization algorithms to determine the canopy SPAD (Soil–Plant Analytical Development) values of potatoes at various fertility stages is inadequate and not very reliable. Using the Pearson feature selection algorithm and the Competitive Adaptive Reweighted Sampling (CARS) method, the Vegetation Index (VI) with the highest correlation was selected as a training feature depended on multispectral orthophoto images from unmanned aerial vehicle (UAV) and measured SPAD values. At various potato fertility stages, Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) inversion models were constructed. The models’ parameters were then optimized using the Grey Wolf Optimizer (GWO) and Sparrow Search Algorithm (SSA). The findings demonstrated a higher correlation between the feature selected VI and SPAD values; additionally, the optimization algorithm enhanced the models’ prediction accuracy; finally, the addition of the fertility stage feature considerably increased the accuracy of the full fertility stage in comparison to the single fertility stage. The models with the highest inversion accuracy were the CARS-SSA-RF, CARS-SSA-XGBoost, and Pearson-SSA-XGBoost models. For the single-fertility and full-fertility phases, respectively, the optimal coefficients of determination (R²s) were 0.60, 0.66, and 0.87, the root-mean-square errors (RMSEs) were 2.63, 3.23, and 2.39, and the mean absolute errors (MAEs) were 2.00, 2.75, and 1.99. Full article

Predicting the height of the water-conducting fracture zone (WCFZ) is crucial for preventing water inrush and ensuring safe underground mining operations. In this study, we propose a novel model combining CatBoost, XGBoost, and AdaBoost with SSA, HHO, and LEA. Key stratum data (DK, TK) and dip mining intensity data were integrated into the existing parameters for WCFZ height prediction. The main influence angle tangent, derived from the probability integral method, replaces the hard rock ratio coefficient. A total of 104 field datasets with eight input parameters were used, with WCFZ height as the dependent variable. The model was validated using five-fold cross-validation and evaluated with root mean square error ($RMSE$), mean absolute error ($MAE$), $R^2$, and mean relative error ($MRE$). The Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) was applied to rank the models. The CAT-HHO model demonstrated the best performance. Using this model, predictions of WCFZ height under varying dip mining intensities showed an approximately linear relationship. SHAP analysis identified mining thickness as the most influential factor. Removing key stratum data from models significantly reduced prediction accuracy. The results highlight the model’s ability to improve WCFZ height prediction, offering insights for water inrush prevention in coal mining operations and providing guidance for applying machine learning to similar challenges. Full article