Optimal Model Parameters Research Articles

Research on National Short-Term Precipitation Forecast Method Based on Machine Learning

Abstract. Climate change is closely related to human lives. With the development of information technology, meteorological data gradually show the characteristics of big data. The purpose of this study is to compare the application effects of different machine learning models in short-term precipitation prediction, to improve the accuracy and efficiency of prediction. Based on the annual average precipitation data of prefecture-level cities in China from 1990 to 2022, this paper uses Random Forest, eXtreme Gradient Boosting and Neural Networks to construct prediction models, and comprehensively evaluate them. In this study, the data is preprocessed to ensure the data quality of the input model. Next, the data is fed into three machine learning algorithm models, Random Forest, eXtreme Gradient Boosting and Neural Networks. Finally, the prediction performance of each model is evaluated by various indexes. In this paper, it is found that the Random Forest model has the best performance, and its prediction accuracy is higher than the other two models, which has great application potential in the field of short-term precipitation prediction. This study shows that a reasonable selection of machine learning methods and optimization of model parameters can effectively improve the accuracy of short-term precipitation prediction. This paper provides some empirical evidence for precipitation prediction, which will help to make more effective decisions in dealing with extreme weather events and climate change challenges in the future.

Key phase diagram experiments and thermodynamic modeling of the ZnO‒B2O3, ZnO‒SiO2, and ZnO‒B2O3‒SiO2 systems

AbstractThermodynamic modeling of binary ZnO‒B2O3 and ZnO‒SiO2 systems and ternary ZnO‒B2O3‒SiO2 system were carried out based on the critical evaluation and optimization of all available phase diagram and thermodynamic property data using the CALculation of PHAse Diagram (CALPHAD) method. To resolve the inconsistency among the available phase diagram data in ternary ZnO‒B2O3‒SiO2 system, key phase diagram experiment was carried out using classical quenching method followed by electron probe micro‐analysis and X‐ray diffraction phase determination. The liquid oxide solution was described using the modified quasichemical model accounting for a short‐range ordering behavior in molten oxide solution. Thermodynamic models with optimized model parameters were used to predict the phase diagram and thermodynamic properties of the ternary systems.

Algorithm for simulating calligrapher’s stroke features using neural networks

In the field of calligraphy art, simulating the stroke features of calligraphers has always been a challenging task. Traditional methods often rely on manually designed rules or feature extraction algorithms, which are difficult to accurately capture calligraphy details and time-consuming. This article aimed to explore a more effective method to simulate the stroke features of calligraphers using neural network technology. This article mainly explored the simulation algorithm of calligrapher stroke features based on neural networks, including calligrapher stroke feature statistics, calligrapher stroke feature abstraction, and calligrapher stroke feature extraction. By using Convolutional Neural Network (CNN) to learn and analyze calligraphy works, a better performance neural network model was established to achieve automatic recognition and classification of calligrapher stroke features. By continuously optimizing model parameters, the accuracy of calligrapher stroke feature simulation can be improved. The average similarity rates for imitating stroke features of five calligraphers (regular script, cursive script, clerical script, seal script, and running script) using Artificial Neural Network (ANN) were 0.72, 0.62, 0.40, 0.33, and 0.53, respectively. The average similarity rates for imitating stroke features of 5 calligraphers (regular script, cursive script, clerical script, seal script, and running script) using CNN were 0.93, 0.78, 0.87, 0.67, and 0.80, respectively. The research results of this article promoted the inheritance and innovation of calligraphy art, and expanded the expression forms and application fields of calligraphy art.

Enhancing Lung Cancer Screening with Bidirectional LSTM and GRU Models

Abstract. This study aims to enhance lung cancer patient screening by developing and evaluating bidirectional Long Short-Term Memory (LSTM) and bidirectional Gated Recurrent Unit (GRU) models using the Lung Cancer dataset from Kaggle. The dataset includes 16 features related to patient symptoms and lung cancer status, providing a broad spectrum of symptoms to improve model accuracy. The research advances Artificial Intelligence (AI)-driven healthcare by integrating these sophisticated machine learning techniques into diagnostic processes. The methodology involves four main steps: preprocessing the dataset for model compatibility, defining the model architecture with bidirectional LSTM and GRU layers and evaluating its performance. The results show an overall accuracy of 52.17%, with accuracy, recall, and F1 scores for both cancerous and non-cancerous categories around 50%. Despite the hybrid model's average performance, it establishes a basis for future enhancements. Optimizing model parameters and exploring additional other techniques to improve prediction accuracy and clinical applicability will be done in the future.

Distribution of Pb, Zn, Fe, As, Sn, Sb, Bi, and Ni Between Oxide Liquid and Metal in the ‘CuO0.5’-CaO-AlO1.5 System in Equilibrium with Cu Metal at 1400 °C

AbstractThe increasing complexity of ore resources and recycled materials in the feed of pyrometallurgical processes present a technical challenge to the metallurgical engineers working on maximizing the recovery of the valuable elements and minimizing the environmental impact of the processes. To address this challenge, the availability of computational tools that can predict the mass and energy balance in complex systems is required. Then, the accurate description of phase equilibria in the complex multicomponent systems describing the chemistry of the pyrometallurgical processes becomes critical for the correct implementation of the indicated tools and facing the outlined industrial challenges. In the present study, the distribution of selected elements (Pb, Zn, Fe, As, Sn, Sb, Bi, and Ni) between oxide liquid and metal in the ‘CuO0.5’–CaO–AlO1.5 system in equilibrium with Cu metal at 1400 °C (liquidus of CaAl2O4) was experimentally studied using the equilibration and quenching technique followed by the electron probe X-ray microanalysis of the resulted samples. The study covered a wide range of effective p(O2) over the system from 10−11 to 10−3.5 (corresponding to formation of immiscible CuO0.5-rich slag). To avoid loss of volatile elements (Pb, Zn, As, Sn, Sb, and Bi), a correlation between ‘CuO0.5’ in oxide liquid and p(O2) in open system was obtained first, followed by studying the volatile elements distribution in closed conditions (Al2O3 crucible sealed in SiO2 ampoule), where ‘CuO0.5’ concentration was used as a marker to evaluate the effective p(O2) over the system. The experimental results were then used for the optimization of the thermodynamic model parameters of the system as part of the integrated experimental and self-consisting thermodynamic modeling research program of phase equilibria in the Cu–Pb–Zn–Fe–Ca–Si–Al–Mg–O–S–(As, Sn, Sb, Bi, Ag, Au, Ni, Cr, Co, and Na) gas/oxide liquid/matte/speiss/metal/solids system. Graphical Abstract

Enhanced predictability and interpretability of COVID-19 severity based on SARS-CoV-2 genomic diversity: a comprehensive study encompassing four years of data.

Despite the end of the global Coronavirus Disease 2019 (COVID-19) pandemic, the risk factors for COVID-19 severity continue to be a pivotal area of research. Specifically, studying the impact of the genomic diversity of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) on COVID-19 severity is crucial for predicting severe outcomes. Therefore, this study aimed to investigate the impact of the SARS-CoV-2 genome sequence, genotype, patient age, gender, and vaccination status on the severity of COVID-19, and to develop accurate and robust prediction models. The training set (n = 12,038), primary testing set (n = 4,006), and secondary testing set (n = 2,845) consist of SARS-CoV-2 genome sequences with patient information, which were obtained from Global Initiative on Sharing all Individual Data (GISAID) spanning over four years. Four machine learning methods were employed to construct prediction models. By extracting SARS-CoV-2 genomic features, optimizing model parameters, and integrating models, this study improved the prediction accuracy. Furthermore, Shapley Additive exPlanes (SHAP) was applied to analyze the interpretability of the model and to identify risk factors, providing insights for the management of severe cases. The proposed ensemble model achieved an F-score of 88.842% and an Area Under the Curve (AUC) of 0.956 on the global testing dataset. In addition to factors such as patient age, gender, and vaccination status, over 40 amino acid site mutation characteristics were identified to have a significant impact on the severity of COVID-19. This work has the potential to facilitate the early identification of COVID-19 patients with high risks of severe illness, thus effectively reducing the rates of severe cases and mortality.

Functional connectotomy of a whole-brain model reveals tumor-induced alterations to neuronal dynamics in glioma patients

Abstract Brain tumors can induce pathological changes in neuronal dynamics that are reflected in functional connectivity measures. Here, we use a whole-brain modeling approach to investigate pathological alterations to neuronal activity in glioma patients. By fitting a Hopf whole-brain model to empirical functional connectivity, we investigate glioma-induced changes in optimal model parameters. We observe considerable differences in neuronal dynamics between glioma patients and healthy controls, both on an individual and population-based level. In particular, model parameter estimation suggests that local tumor pathology causes changes in brain dynamics by increasing the influence of interregional interactions on global neuronal activity. Our approach demonstrates that whole-brain models provide valuable insights for understanding glioma-associated alterations in functional connectivity.

Enhancing Photovoltaic Solar Model Parameter Optimization: WSO-MTBO Hybrid Approach based on Newton-Raphson Method

Enhancing Photovoltaic Solar Model Parameter Optimization: WSO-MTBO Hybrid Approach based on Newton-Raphson Method

Characterizing and Understanding HGNN Training on GPUs

Owing to their remarkable representation capabilities for heterogeneous graph data, Heterogeneous Graph Neural Networks (HGNNs) have been widely adopted in many critical real-world domains such as recommendation systems and medical analysis. Prior to their practical application, identifying the optimal HGNN model parameters tailored to specific tasks through extensive training is a time-consuming and costly process. To enhance the efficiency of HGNN training, it is essential to characterize and analyze the execution semantics and patterns within the training process to identify performance bottlenecks. In this study, we conduct a comprehensive quantification and in-depth analysis of two mainstream HGNN training scenarios, including single-GPU and multi-GPU distributed training. Based on the characterization results, we reveal the performance bottlenecks and their underlying causes in different HGNN training scenarios and propose optimization guidelines from both software and hardware perspectives.

Predicting fungal infection sensitivity of sepals in harvested tomatoes using imaging spectroscopy and partial least squares discriminant analysis

Tomatoes ( Solanum lycopersicum L.) are a widely grown and globally traded vegetable, essential for both local consumption and international trade. However, approximately 30% of harvested tomato yields are lost due to fungal decay during postharvest handling. Timely disease identification is crucial to prevent such losses, but certain tomato varieties exhibit higher susceptibility to fungal infections than others. Additionally, there are variations in susceptibility among individual sepals, with unknown underlying causes. Traditional methods for assessing fungal presence in plants have limitations, such as sample destruction and a focus on symptom detection rather than evaluating susceptibility to fungal infection. Hence, there is a demand need for an accurate, non-destructive method capable of predicting susceptibility to fungal infection. The use of hyperspectral imaging (HSI) with chemometrics presents a pioneering approach to address this need. In this study, three tomato cultivars (‘Brioso,’ ‘Cappricia,’ and ‘Provine’) were studied. Hyperspectral images were captured on day-1 of harvest, followed by controlled fungal growth conditions. Ground truth assessments were conducted by three experts on day-3 and day-4, averaging severity scores assigned per sepal. The methodology involved extracting spectra from HSI images and calibrating and validating models using partial least squares discriminant analysis (PLSDA), aiming to optimize model parameters for accurate predictions. The models were categorized into those developed using data from a single variety (intravariety) and those utilizing data from multiple varieties combined (global models). The best-performing intravariety model was established using the Cappricia variety, achieving a balanced accuracy of 0.84. Conversely, a global model combining Cappricia and Provine varieties achieved a balanced accuracy of 0.70. Overall, the results suggest that distinguishing between more and less susceptible sepals is feasible under controlled conditions.

Stability discussion and application study of pseudo-corner models

Accurate plastic flow modelling under complex working conditions is crucial for metal deformation simulations. Recently, some advanced pseudo-corner models have been developed to describe corner effects and analyze strain localization problems. The present work consists of three parts. The first part discusses the intrinsic stability of the pseudo-corner model class, which forms the premise of application analysis. The second part applies the pseudo-corner models and the associated flow rule (AFR) to buckling onset estimation, plastic post-buckling analysis and shear band analysis. The experimental conditions are strictly reproduced and the optimal model parameters are determined. The results reveal that the pseudo-corner models and AFR are indistinguishable in the buckling onset estimation. AFR overestimates the post-buckling strength of circular tubes under axial compression, and cannot reproduce the shear band development during sheet bending; while the pseudo-corner models have better prediction performance in both scenarios. The results also suggest that the parameter values of pseudo-corner models are apparently inconsistent in the above two types of problems. Then in the third part, two representative influencing factors including strain gradient plasticity and initial imperfections are discussed, and this inconsistency is finally attributed to the shortwave surface defect which however is usually neglected by previous studies.

Design and optimization of a planar anti-buckling compliant rotational joint with a remote center of motion

Compliant mechanisms (CMs) exhibit some excellent mechanical properties and provide numerous innovative solutions for many existing mechanical applications. Among them, planar compliant rotational joints have made substantial contributions to precision engineering. To achieve high-precision positioning with a compliant rotational joint, it is essential to study characteristics such as anti-buckling and constant stiffness. The remote center of motion (RCM) mechanism, with its compact structure and ease of precise control, is expected to enhance overall rigidity and reduce parasitic displacements. Here, we propose a planar anti-buckling compliant rotational joint with a RCM. Static modeling and model validation are conducted for different geometric configurations, including single and combined structures. A distributed configuration with bidirectional anti-buckling properties is selected for optimization. Using a global parameter optimization model, single-objective optimization studies are conducted for three distinct characteristics. Subsequently, a multi-objective optimization model for constant stiffness and high precision is established. Based on the optimization results, a compliant rotational joint with bidirectional anti-buckling, constant rotational stiffness, and high precision is designed. Finally, the effectiveness of the optimization method is validated through physical experiments.

Measurement and Modeling of Self-Directed Channel (SDC) Memristors: An Extensive Study

This study systematically addresses the challenge of accurately modeling memristors, focusing on four distinct types doped with tungsten, tin, chromium, and carbon, fabricated by Knowm Inc. A comprehensive characterization is performed by subjecting the devices to sinusoidal excitations with varying frequencies and amplitudes, followed by data averaging and high-frequency filtering. The resulting measurements are fitted using three prominent memristor models: VTEAM, MMS, and Yakopcic. Additional bespoke modifications are assessed. These models, typically formulated as coupled algebraic differential equations integrating electrical quantities (voltage and current) with internal state variables governing device dynamics, are optimized using two robust approaches: (1) interior-point optimization with gradient-based search, and (2) Nelder–Mead gradient-free optimization, both with box constraints applied. A thorough comparison and discussion of the optimized model parameters ensue, accompanied by an examination of the sensitivity to diverse frequency and amplitude ranges. The findings inform conclusions and provide a foundation for future refinements, underscoring the importance of multi-model evaluation and advanced optimization strategies in precise memristor modeling. The presented methodology offers a valuable framework for elucidating optimal modeling paradigms tailored to specific memristor architectures and operating regimes, ultimately enhancing their integration in emerging neuromorphic and computational applications.

Construction of an inflammatory, nutritional, and metabolic prognostic risk score in patients with heart failure with preserved ejection fraction: a machine learning-based approach

Abstract Background Chronic inflammation, malnutrition, and metabolic syndrome contribute to the underlying mechanisms of heart failure with preserved ejection fraction (HFpEF). Inflammatory, nutritional, and metabolic biomarkers often interact with each other and affect the prognosis of patients. Purpose We aimed to identify the most predictive indicators from a pool of 21 biomarkers encompassing inflammation, nutritional status, as well as lipid, glucose, thyroid, and uric acid metabolism. Subsequently, predictive biomarkers were used to develop a risk score to enhance risk prediction for patients with HFpEF. Methods We included patients diagnosed with HFpEF and without infective or systemic disease. The primary outcome was all-cause mortality. We employed the Least Absolute Shrinkage and Selection Operator (LASSO) regression to screen the biomarkers. A machine learning-based approach was utilized to fine-tune the optimal model parameters. Additionally, we generated 1000 bootstrapping datasets to identify biomarkers recurring above a 95% frequency in repetitions, which were then employed to formulate the biomarker risk score. The discrimination of incorporating the risk score into the basic model was evaluated using 100 iterations of 5-fold cross-validation to assess the predictive efficacy. Results Among the 1311 included patients (with a median age of 64 years and 42.6% female), 363 patients (27.7%) experienced mortality over a median follow-up period of 2.7 years. The final risk score model was derived from 5 biomarkers—albumin (ALB), red blood cell distribution width-standard deviation (RDW-SD), lymphocytes, triiodothyronine (T3), and uric acid—each selected in over 95% of 1000 bootstrapping iterations. Integration of this risk score into the basic model notably enhanced discrimination (∆C-index=0.015, 95% CI 0.005-0.023) and reclassification (IDI: 3.3%, 95% CI 1.7%-5.8%; NRI: 15.2%, 95% CI 6.5%-22.5%) for risk stratification. In time-dependent receiver operating characteristic (ROC) analysis, the mean area under the curve (AUC) of the risk score was 0.688, 0.710, and 0.738 at 1, 3, and 5 years post-discharge, respectively, in the cross-validation sets. Incorporating the risk score into the basic model significantly improved the AUC at 1, 3, and 5 years (DeLong test P-value<0.05). Furthermore, in multivariable Cox regression, the risk score demonstrated an independent association with all-cause mortality (HR: 1.81, 95% CI 1.51-2.16, P<0.001 per 1 score increase). Conclusions A risk score derived from 5 commonly used inflammatory, nutritional, thyroid and uric acid metabolic biomarkers can effectively identify high-risk patients with HFpEF. This approach supports the development of potential individualized management strategies for patients with HFpEF.Study population and model constructionPredictive performance of the risk score

Toxicokinetic model of the pyrethroid pesticide lambda-cyhalothrin, main exposure route and dose reconstruction predictions in agricultural workers.

A toxicokinetic model of the pyrethroid insecticide lambda-cyhalothrin (LCT) was developed to relate absorbed doses to urinary cis-3-(2-chloro-3,3,3-trifluoroprop-1-en-1-yl)-2,2-dimethylcyclopropanecarboxylic acid (CFMP) metabolite levels used as a biomarker of exposure. The model then served to reconstruct absorbed doses in agricultural workers and their probability of exceeding the EFSA Acceptable occupational Exposure Level (AOEL). The toxicokinetic model was able to reproduce the temporal profiles of CFMP in the urine of operators spraying pesticides using the optimized model parameters (adjusted to human volunteer data). Modeling also showed that simulation of an inadvertent oral exposure mainly was the exposure scenario giving the best fit to CFMP urinary time-course data in applicators. With the dermal model parameters optimized from data in volunteers, simulation of a dermal exposure in applicators did not allow to reproduce the observed peak excretions and urinary metabolite levels; extremely high applied dermal doses would be required but still simulated dermal penetration rate would remain too slow. Simulation of an inhalation exposure allowed to reproduce the observed time-courses, but with unrealistic air concentrations. For applicators with the highest urinary concentrations, there was a probability of exceeding the AOEL at some points during the biomonitoring period [>50% probability of exceeding for 27% of 24-h samples]; for non-applicator workers the probability of exceeding the AOEL value was very low [corresponding value of 5%]. Furthermore, the median [95% CI] estimates of 10 000 Monte Carlo simulations led to a biological reference value corresponding to the AOEL of 116 [113-119] ng/kg bw/d and 7.5 [7.3-7.7] μg/L. Overall, 7% of applicators and 1% of workers performing weeding and strawberry picking had a probability of exceeding this biological reference value. As a next step, it would be interesting to apply these methods to multiple exposure to various contaminants.

An Adaptive Parameter Optimization Deep Learning Model for Energetic Liquid Vision Recognition Based on Feedback Mechanism.

The precise detection of liquid flow and viscosity is a crucial challenge in industrial processes and environmental monitoring due to the variety of liquid samples and the complex reflective properties of energetic liquids. Traditional methods often struggle to maintain accuracy under such conditions. This study addresses the complexity arising from sample diversity and the reflective properties of energetic liquids by introducing a novel model based on computer vision and deep learning. We propose the DBN-AGS-FLSS, an integrated deep learning model for high-precision, real-time liquid surface pointer detection. The model combines Deep Belief Networks (DBN), Feedback Least-Squares SVM classifiers (FLSS), and Adaptive Genetic Selectors (AGS). Enhanced by bilateral filtering and adaptive contrast enhancement algorithms, the model significantly improves image clarity and detection accuracy. The use of a feedback mechanism for reverse judgment dynamically optimizes model parameters, enhancing system accuracy and robustness. The model achieved an accuracy, precision, F1 score, and recall of 99.37%, 99.36%, 99.16%, and 99.36%, respectively, with an inference speed of only 1.5 ms/frame. Experimental results demonstrate the model's superior performance across various complex detection scenarios, validating its practicality and reliability. This study opens new avenues for industrial applications, especially in real-time monitoring and automated systems, and provides valuable reference for future advancements in computer vision-based detection technologies.

Modelling the evolving critical state of crushable soils and parameter optimization with an improved genetic algorithm (AIS-RCGA)

The critical state line (CSL) plays an essential role in the constitutive modelling of granular soils. It serves as a reference line for the measurement of the state parameter. The critical state of crushable soils cannot accurately be determined from experimental data because of the ever-changing soil properties due to particle breakage. In this study, two new parameters eB and eb are introduced, which account for the final position and the evolution of CSL of crushable soils during shearing, respectively. To identify the optimal CSL-related parameters from experimental data, a hybrid genetic algorithm combining artificial immune system (AIS) and real-coded genetic algorithm (RCGA), namely AIS-RCGA is adopted. The fitness is defined to minimize the prediction error of both void ratio (or, pore water pressure) and deviatoric stress. We have refined the tuning methods for several hyperparameters of AIS-RCGA and proposed a novel method to assess the similarity of individuals within AIS-RCGA. Results show that the new method is more efficient in finding the global optimal for our problem. With optimized model parameters, the new constitutive model can accurately predict the response of crushable soil, outperforming other constitutive model reported in the literature.

Structural topology optimization based on deep learning

In the mechanical design of structures, traditional topology optimization methods involve numerous finite element iterative analyses, leading to a significant expenditure of computational resources. Therefore, the improved multi-scale gradient generative adversarial networks topology optimization technique is proposed. The topology optimization condition parameters are compressed into a low-dimensional latent space feature representation using the encoder, allowing the model to better extract features from these parameters. To speed up model training, the generator and discriminator networks use lightweight residual convolutional blocks. The hybrid attention mechanism extracts prominent region features from the topology optimization structure map. The model training process is guided by a multi-dimensional fusion loss function to enhance the quality of generated model samples. Finally, transferring the parameters of the low-resolution topology optimization model to the high-resolution model enables complete training on a limited amount of high-resolution topology optimization datasets. The experimental data on the low- and high-resolution topology optimization datasets demonstrate that, when compared to alternative methods, this method produces better-quality topology optimization structure maps. Additionally, it can generate high-resolution topology optimization structure maps in minimal time, enabling real-time topology optimization.

Joint torque estimation method of industrial robot by combining error compensation model based on LSTM and iterative weighted parameter identification

It is essential for human-robot interaction to accurate joint torque value estimation of industrial robots without force/torque (F/T) sensors. The most common method to estimate the value of robot joint torque is based on dynamic model parameter identification. However, no matter how elaborate the modeling methods are used, there are always uncertainty errors when robot’s joint rotation direction changes. In this paper, an identification framework is proposed to estimate optimal model parameters, and a deep learning network is proposed to compensate for the uncertainty errors caused by the unmodeled dynamics part. At first, the dynamic parameters are estimated during the Least Squares (LS) identification process considering the noise influence and data outliers, and the nonlinear friction model is unified into the identification framework. Secondly, based on Long-Short Term Memory (LSTM) network, an error compensation model (ECM) is proposed to establish the mapping relationship between the joint motion and the identification errors. Finally, a 6-DOF robot is used for parameter identification and ECM validation. Experimental results show that the proposed identification method is better than the LS method. Compared with the torque value estimated by the identification method, the proposed ECM can compensate for the uncertainty errors and improve the torque estimation accuracy.

Traffic Flow Prediction in 5G-Enabled Intelligent Transportation Systems Using Parameter Optimization and Adaptive Model Selection.

This study proposes a novel hybrid method, FVMD-WOA-GA, for enhancing traffic flow prediction in 5G-enabled intelligent transportation systems. The method integrates fast variational mode decomposition (FVMD) with optimization techniques, namely, the whale optimization algorithm (WOA) and genetic algorithm (GA), to improve the accuracy of overall traffic flow based on models tailored for each decomposed sub-sequence. The selected predictive models-long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU)-were considered to capture diverse temporal dependencies in traffic data. This research explored a multi-stage approach, where the decomposition, optimization, and selection of models are performed systematically to improve prediction performance. Experimental validation on two real-world traffic datasets further underscores the method's efficacy, achieving root mean squared errors (RMSEs) of 152.43 and 7.91 on the respective datasets, which marks improvements of 3.44% and 12.87% compared to the existing methods. These results highlight the ability of the FVMD-WOA-GA approach to improve prediction accuracy significantly, reduce inference time, enhance system adaptability, and contribute to more efficient traffic management.