Hybrid Model Research Articles

Advancing source apportionment of soil potentially toxic elements using a hybrid model: a case study in urban parks, Beijing, China.

Identifying the source-specific health risks of potentially toxic elements (PTE) in urban park soils is essential for human health protection. However, previous studies have mostly focused on the deterministic source-specific health risks, ignoring the health risk assessment from a probabilistic perspective. To fill this gap, we developed a hybrid model that incorporated machine learning (ML) interpretability into positive matrix factorization (PMF) and probability health risk assessment (PHRA) based on the Monte Carlo simulation. The results indicated that concentrations of soil PTEs except for Mn and Sb were significantly higher than their corresponding background values. Random forest (RF) was regarded as the best ML model to identify key drivers for As, Cd, Cr, Cu, Ni, Pb, and Zn, with R2 > 0.60, but was less effective for other soil PTEs (R2 < 0.49). Specifically, the contributions of the four potential pollutionsources were mixed sources, traffic emission, fuel combustion, and building materials, with contribution rate of 24.88%, 30.56%, 28.99%, and 15.56%, respectively. Fuel combustion contributed the most to non-carcinogenic for children (39.45%), male (43.84%), and female (43.76%), and the non-carcinogenic risk could be considered negligiblefor human. However, building materials was the major contributor to carcinogenic risk for children (36.1%), male (44.9%), and female (43.2%). The integration of the RF model with PMF and PHRA improved the accuracy of the results by identifying and quantifying the specific sources of each soil PTE using the relative importance analysis from the RF model. The results of this study assisted in providing efficient strategies for risk management and control of soil PTEs in Beijing parks.

Integrated Hybrid Modelling and Surrogate Model-Based Operation Optimization of Fluid Catalytic Cracking Process

Fluid Catalytic Cracking (FCC) is one of the most important conversion processes in oil refineries, widely used to convert high-boiling, high-molecular-weight hydrocarbon components from crude oil into more valuable products like gasoline and diesel. Advanced simulation and optimization technologies are critical for improving the operational efficiency and economic performance of the FCC process. First-principles-based simulators rely on parameter estimation and are computationally intensive, making them unsuitable for online optimization. In recent years, with the development of deep learning, data-driven models have made significant progress in FCC modeling. However, due to their black-box nature and difficulty with extrapolation, they are rarely used for optimization. To bridge this gap, we propose an integrated framework that combines hybrid modeling and surrogate model-based optimization. This approach combines plant and simulation data to train a multi-task learning prediction model, which then serves as a surrogate for operational optimization. Validated on a large-scale FCC unit in southern China, the model predicts product yields with an error margin of under 4.84% for all products. Following optimization, yields of LNG, gasoline, and diesel rose by an average of 0.10 wt%, 1.58 wt%, and 1.05 wt%, respectively, resulting in a 3.67% increase in product revenues. This highlights the substantial potential of this framework for industrial applications.

Deformation prediction of overlying strata in multi-seam mining based on the influence function method-key stratum hybrid model

The extraction of coal seams from subterranean strata has the potential to induce displacement and deformation within the overlying strata. This leads to fractures in the strata and surface subsidence. The repetitive mining of coal seams, especially in scenarios involving multiple coal seams, can reactivate previously stabilized rock strata, causing them to subside and deform again. This exacerbates deformation and compromises the structural integrity of the strata. Consequently, there is an imperative need to thoroughly investigate the patterns of movement exhibited by overlying rock layers as a result of the recurrent extraction of coal seams. In recent years, significant progress in this field has been the effective utilization of the Influence Function Method-Key Stratum (IFM-KS) hybrid model. This model, distinguished by its amalgamation of mechanical and geometric methodologies, has proven to be efficacious in scrutinizing the movement dynamics of overlying strata in the context of single-layer mining operations. Based on that, this paper introduces a novel model for predicting rock layer movement and deformation in multi-seam mining. The calculation results derived from this model reveal the dynamics of rock layers' movement and deformation following secondary disturbances during repetitive mining. The accuracy and reliability of this newly proposed model in predicting rock movement during dual-layer coal mining are validated through both theoretical calculations and UDEC numerical simulations. The results demonstrate a high degree of consistency between the numerically simulated rock displacement and mathematical calculations and the rationality of the model. Based on the rock strata movement calculation results, the corresponding porosity distribution is also derived. Compared to single-layer coal mining, multi-seam mining could cause greater changes in porosity in the goaf area and extensive damage to rock strata. More fracture pathways between coal seams occur and a volume of air-leakage quantity is expected. This computational model provides invaluable insights for predicting rock strata deformation and porosity distribution in repetitive mining scenarios of a similar nature.

Hubert-LSTM: A Hybrid Model for Artificial Intelligence and Human Speech

Hubert-LSTM: A Hybrid Model for Artificial Intelligence and Human Speech

How Online Learning Has Changed the Accessibility of Higher Education

It has become more accessible, flexible, and affordable than ever before as it completely does away with geographical and time barriers as have persisted in traditional education. This aspect accelerated not just in presence but also with identifying strengths and weaknesses in online learning during the pandemic. An advantage of online education has been low cost, based on reduced physical infrastructure. Another is flexibility in learning that learners enjoy as it balances personal responsibilities with their educational needs. Additionally, access for disabled students exists through assistive technologies. Many institutions have embraced this hybrid model where both the traditional learning experience and the online learning experience can give personalized educational experiences. On the other hand, it has posed such problems as the digital divide: students from lower-income backgrounds and those residing in rural areas often cannot access the internet and its other technological tools, which remain inaccessible to a high number of students with disabilities. This contributes to the isolation and disengagement of the online learners, who feel much more at home within the face-to-face learning setting. A further drawback relates to the issue of academic integrity in an online virtual learning environment, where there are no guarantees that students would be self-regulatory.The study examines how these challenges affect the accessibility of higher education and explores gaps in current research, such as the digital divide, inclusion of marginalized students, and the emotional and social impact of online learning. Online education has broadened access to higher education, but there is a need to ensure that all barriers are addressed so that equitable, high-quality learning opportunities are offered to all students.

Multi-Timescale Energy Consumption Management in Smart Buildings Using Hybrid Deep Artificial Neural Networks

Demand side management is a critical issue in the energy sector. Recent events such as the global energy crisis, costs, the necessity to reduce greenhouse emissions, and extreme weather conditions have increased the need for energy efficiency. Thus, accurately predicting energy consumption is one of the key steps in addressing inefficiency in energy consumption and its optimization. In this regard, accurate predictions on a daily, hourly, and minute-by-minute basis would not only minimize wastage but would also help to save costs. In this article, we propose intelligent models using ensembles of convolutional neural network (CNN), long-short-term memory (LSTM), bi-directional LSTM and gated recurrent units (GRUs) neural network models for daily, hourly, and minute-by-minute predictions of energy consumptions in smart buildings. The proposed models outperform state-of-the-art deep neural network models for predicting minute-by-minute energy consumption, with a mean square error of 0.109. The evaluated hybrid models also capture more latent trends in the data than traditional single models. The results highlight the potential of using hybrid deep learning models for improved energy efficiency management in smart buildings.

Effect of ultrasonic vibration on fatigue life of Inconel 718 machined by high-speed milling: Physics-enhanced machine learning approach

Ultrasonic assisted high-speed machining (UAHSM) can be served as a thermomechanical surface sever plastic deformation (SSPD), because of the high-frequency impact load exerting to the sample together with thermomechanical loads due to shearing and plowing. Despite existing of few works which studied the impact of ultrasonic vibration on fatigue life assessment of difficult-to-cut material by experimental approach, they couldn’t provide an in-depth analysis to identify the underlying mechanisms of fatigue due time-consuming and costly fatigue life tests. Hence, elucidating the role of ultrasonic vibration in UAHSM on variation of fatigue life needs further studies. In order to do so, in the present work, a hybrid predictive approach based using ANFIS-based machine learning model and micromechanical Navaro-Rios (NR) fatigue crack propagation model has been introduced to directly correlates the UAHSM’s parameters to fatigue life. Here the former correlates feed rate, cutting velocity and vibration amplitude as process inputs, to surface integrity aspects (SIA) viz residual stress, roughness and grain size as output. Then, the modeled SIA are correlated to fatigue life using the former. The introduced hybrid model was then verified through series of UAHSM by examining the fatigue lives of milled Inconel 718 using four-point bending fatigue tests. Upon confirmation of the developed model, a comprehensive study was carried out to find how the process factors impact variation of SIA and subsequently fatigue. It was found from the results of developed models and confirmatory experiments that the role of ultrasonic vibration on improved fatigue life is mainly due to inducing compressive residual stress and more refined microstructure than the roughness.

CT-based radiomics model for predicting perineural invasion status in gastric cancer.

Perineural invasion (PNI) is an independent risk factor for poor prognosis in gastric cancer (GC) patients. This study aimed to develop and validate predictive models based on CT imaging and clinical features to predict PNI status in GC patients. This retrospective study included 291 GC patients (229 in the training cohort and 62 in the validation cohort) who underwent gastrectomy between January 2020 and August 2022. The clinical data and preoperative abdominal contrast-enhanced computed tomography (CECT) images were collected. Radiomics features were extracted from the venous phase of CECT images. The intraclass correlation coefficient (ICC), Pearson correlation coefficient, and t-test were applied for radiomics feature selection. The random forest algorithm was used to construct a radiomics signature and calculate the radiomics feature score (Rad-score). A hybrid model was built by aggregating the Rad-score and clinical predictors. The area under the receiver operating characteristic curve (ROC) and decision curve analysis (DCA) were used to evaluate the prediction performance of the radiomics, clinical, and hybrid models. A total of 994 radiomics features were extracted from the venous phase images of each patient. Finally, 5 radiomics features were selected and used to construct a radiomics signature. The hybrid model demonstrated strong predictive ability for PNI, with AUCs of 0.833 (95% CI: 0.779-0.887) and 0.806 (95% CI: 0.628-0.983) in the training and validation cohorts, respectively. The DCA showed that the hybrid model had good clinical utility. We established three models, and the hybrid model that combined the Rad-score and clinical predictors had a high potential for predicting PNI in GC patients.

A Survey of AI-Powered Proactive Threat-Hunting Techniques: Challenges and Future Directions

The study narrates the dimensions of AI-powered threat hunting techniques that keep changing and draws on their importance in cybersecurity measures while dealing with advanced cyber threats. While the cyber adversary is continuously adapting and innovating, the integration of advanced AI algorithms, including reinforcement learning and hybrid models, has become one of the key strategies for real-time threat detection and response. The research investigates the transformative potential of these algorithms to facilitate adaptive systems that can quickly recognize and act on new attack vectors. It also discusses the integration of AI with emergent technologies like blockchain, quantum computing, and IoT in the dimension of their potential to leverage threat intelligence and response mechanisms. This would naturally lead to increased efficiency in threat hunting by leveraging the strengths brought into this endeavor by both the AI systems and human analysts. The study also advocates for the development of strong policy and governance frameworks to drive home some ethical concerns in deploying AI in cybersecurity. Such a framework is very important for addressing challenges on data privacy, accountability, and transparency. The findings bring into sharp relief that, in this continuing evolution of the cybersecurity landscape, what will work best is a proactive stance-a proactive stance that incorporates innovative technologies with human ingenuity and ethics. This white paper broadly documents the considerable promise of AI in threat hunting and strategic advances needed to harden organizational defenses against a dynamic threat environment.

Real-Time Evaluation of the Improved Eagle Strategy Model in the Internet of Things

With the rapid expansion of cloud computing and the pervasive growth of IoT across industries and educational sectors, the need for efficient remote data management and service orchestration has become paramount. Web services, facilitated by APIs, offer a modular approach to integrating and streamlining complex business processes. However, real-time monitoring and optimal service selection within large-scale, cloud-based repositories remain significant challenges. This study introduces the novel Improved Eagle Strategy (IES) hybrid model, which uniquely integrates bio-inspired optimization with clustering techniques to drastically reduce computation time while ensuring highly accurate service selection tailored to specific user requirements. Through comprehensive NetLogo simulations, the IES model demonstrates superior efficiency in service selection compared to existing methodologies. Additionally, the IES model’s application through a web dashboard system highlights its capability to manage both functional and non-functional service attributes effectively. When deployed on real-time IoT devices, the IES model not only enhances computation speed but also ensures a more responsive and user-centric service environment. This research underscores the transformative potential of the IES model, marking a significant advancement in optimizing cloud computing processes, particularly within the IoT ecosystem.

A hybrid method for discovering interferon-gamma inducing peptides in human and mouse

Interferon-gamma (IFN-γ) is a versatile pleiotropic cytokine essential for both innate and adaptive immune responses. It exhibits both pro-inflammatory and anti-inflammatory properties, making it a promising therapeutic candidate for treating various infectious diseases and cancers. We present IFNepitope2, a host-specific technique to annotate IFN-γ inducing peptides, it is an updated version of IFNepitope introduced by Dhanda et al. In this study, dataset used for developing prediction method contain experimentally validated 25,492 and 7983 IFN-γ inducing peptides in human and mouse host, respectively. In initial phase, machine learning techniques have been exploited to develop classification model using wide range of peptide features. Further, to improve machine learning based models or alignment free models, we explore potential of similarity-based technique BLAST. Finally, a hybrid model has been developed that combine best machine learning based model with BLAST. In most of the case, models based on extra tree perform better than other machine learning techniques. In case of peptide features, compositional feature particularly dipeptide composition performs better than one-hot encoding or binary profile. Our best machine learning based models achieved AUROC 0.89 and 0.83 for human and mouse host, respectively. The hybrid model achieved the AUROC 0.90 and 0.85 for human and mouse host, respectively. All models have been evaluated on an independent/validation dataset not used for training or testing these models. Newly developed method performs better than existing method on independent dataset. The major objective of this study is to predict, design and scan IFN-γ inducing peptides, thus server/software have been developed (https://webs.iiitd.edu.in/raghava/ifnepitope2/). This method is also available as standalone at https://github.com/raghavagps/ifnepitope2 and python package index at https://pypi.org/project/ifnepitope2/.

Economic perspectives on redefining public service media in the digital era: broadcasting to media platform

AbstractThis paper examines the currently debated platform models of public service media (PSM) in Germany. As global platforms such as Meta, Netflix, and YouTube dominate the media landscape, PSM is shifting from linear broadcasting to digital platform strategies. This study analyzes 21 platform proposals and categorizes them into five archetypes using a taxonomy-based method and hierarchical clustering. The findings reveal a range of approaches, from pure public media platforms to hybrid models that include both public and private media organizations. However, despite the innovative character of these proposals, significant challenges remain, particularly in funding, governance, and aligning platform economics with public service media objectives. This research highlights the need to integrate a platform perspective further into the development of PSM strategies and address operational policy and market issues to ensure the successful transformation of PSM in the digital era.

CrackNet: A Hybrid Model for Crack Segmentation with Dynamic Loss Function

Cracks are a common form of damage in infrastructure, posing significant risks to both personal safety and property. Along with the development of deep learning, visual-based crack automatic detection has been widely studied. However, this task is still challenging due to complex crack topology, noisy backgrounds, unbalanced categories, etc. To address these challenges, this research proposes a novel hybrid network, named CrackNet, which leverages the strengths of both CNN and transformer. On the encoder side, CNNs are employed to extract multi-level local features, while transformers are used to model global dependencies. Additionally, a strip pooling module is introduced to suppress irrelevant regions and enhance the network’s ability to segment narrow and elongated cracks. On the decoder side, an attention-based skip connection strategy and a mixed up-sampling module are implemented to restore detailed information. Furthermore, a joint learning loss combining Dice and cross-entropy with dynamic weighting is proposed to mitigate the effects of severe class imbalance. CrackNet is trained and evaluated on three public crack datasets, and experimental results show that the proposed model outperforms several well-known deep neural networks, with a particularly noticeable improvement in recall rate.

Deep learning hybrid model ECG classification using AlexNet and parallel dual branch fusion network model.

Cardiovascular diseases are a cause of death making it crucial to accurately diagnose them. Electrocardiography plays a role in detecting heart issues such as heart attacks, bundle branch blocks and irregular heart rhythms. Manual analysis of ECGs is prone to mistakes and time consuming, underscoring the importance of automated methods. This study uses AI models like AlexNet and a dual branch model for categorizing ECG signals from the PTB Diagnostic ECG Database. AlexNet achieved a validation accuracy of 98.64% and a test set accuracy of 99% while the dual branch fusion network model achieved a test set accuracy of 99%. Data preprocessing involved standardizing, balancing and reshaping ECG signals. These models exhibited precision, sensitivity and specificity. In comparison to state of the arts' models such as Hybrid AlexNet SVM and DCNN LSTM our proposed models displayed performance. The high accuracy rates of 99% underscore their potential for ECG classification. These results validate the advantages of incorporating learning models into setups for automated ECG analysis providing adaptable solutions for various healthcare settings including rural areas.

A Comprehensive Review of Remaining Useful Life Estimation Approaches for Rotating Machinery

This review paper comprehensively analyzes the prognosis of rotating machines (RMs), focusing on mechanical-flaw and remaining-useful-life (RUL) estimation in industrial and renewable energy applications. It introduces common mechanical faults in rotating machinery, their causes, and their potential impacts on RM performance and longevity, particularly in wind, wave, and tidal energy systems, where reliability is crucial. The study outlines the primary procedures for RUL estimation, including data acquisition, health indicator (HI) construction, failure threshold (FT) determination, RUL estimation approaches, and evaluation metrics, through a detailed review of published work from the past six years. A detailed investigation of HI design using mechanical-signal-based, model-based, and artificial intelligence (AI)-based techniques is presented, emphasizing their relevance to condition monitoring and fault detection in offshore and hybrid renewable energy systems. The paper thoroughly explores the use of physics-based, data-driven, and hybrid models for prognosis. Additionally, the review delves into the application of advanced methods such as transfer learning and physics-informed neural networks for RUL estimation. The advantages and disadvantages of each method are discussed in detail, providing a foundation for optimizing condition-monitoring strategies. Finally, the paper identifies open challenges in prognostics of RMs and concludes with critical suggestions for future research to enhance the reliability of these technologies.

Improved Bacterial Foraging Optimization Algorithm with Machine Learning-Driven Short-Term Electricity Load Forecasting: A Case Study in Peninsular Malaysia

Accurate electricity demand forecasting is crucial for ensuring the sustainability and reliability of power systems. Least square support vector machines (LSSVM) are well suited to handle complex non-linear power load series. However, the less optimal regularization parameter and the Gaussian kernel function in the LSSVM model have contributed to flawed forecasting accuracy and random generalization ability. Thus, these parameters of LSSVM need to be chosen appropriately using intelligent optimization algorithms. This study proposes a new hybrid model based on the LSSVM optimized by the improved bacterial foraging optimization algorithm (IBFOA) for forecasting the short-term daily electricity load in Peninsular Malaysia. The IBFOA based on the sine cosine equation addresses the limitations of fixed chemotaxis constants in the original bacterial foraging optimization algorithm (BFOA), enhancing its exploration and exploitation capabilities. Finally, the load forecasting model based on LSSVM-IBFOA is constructed using mean absolute percentage error (MAPE) as the objective function. The comparative analysis demonstrates the model, achieving the highest determination coefficient (R2) of 0.9880 and significantly reducing the average MAPE value by 28.36%, 27.72%, and 5.47% compared to the deep neural network (DNN), LSSVM, and LSSVM-BFOA, respectively. Additionally, IBFOA exhibits faster convergence times compared to BFOA, highlighting the practicality of LSSVM-IBFOA for short-term load forecasting.

Study of Sterically Crowded Alkanes: Assessment of Non-Empirical Density Functionals Including Double-Hybrid (Cost-Effective) Methods.

We theoretically study here the homolytic dissociation reactions of sterically crowded alkanes of increasing size, carrying three different (bulky) substituents such as tert-butyl, adamantyl, and [1.1.1]propellanyl, employing a family of parameter-free functionals ranging from semi-local, to hybrid and double-hybrid models. The study is complemented with the interaction between a pair of HC(CH3)3 molecules at repulsive and attractive regions, as an example of a system composed by a pair of weakly bound sterically crowded alkanes. We also assessed the effect of incorporating reliable dispersion corrections (i. e., D4 or NL) for all the functionals assessed, as well as the use of a tailored basis set (DH-SVPD) for non-covalent interactions, which provides the best trade-off between accuracy and computational cost for a seemingly extended applications to branched or crowded systems. Overall, the PBE-QIDH/DH-SVPD and r2SCAN-QIDH/DH-SVPD methods represent an excellent compromise providing relatively low, and thus very competitive, errors at a fraction of the cost of other quantum-chemical methods in use.

Impact of Digital Banking on Traditional Banking Services

Digital banking has quickly become a game-changer in the world of finance, altering the way banks interact with customers and run their operations. With tools like mobile banking apps, AI-driven recommendations, and blockchain technology, consumers now have more control and convenience in managing their finances than ever before. This shift has raised the bar on what people expect from their banks, prompting traditional banking institutions to adapt to stay relevant in a fast-evolving, tech-driven landscape. This paper explores how digital banking is impacting traditional banks in areas like cost savings, data insights, market reach, and consumer habits. While the digital shift opens doors to new efficiencies and easier access, it also brings unique challenges, especially around cybersecurity, privacy, and compliance with new regulations. Additionally, with fewer physical branches and less face-to-face contact, there’s a question of how this transition may affect the trust and service quality that many customers still value in their banking experience. Using real-world examples and industry trends, this paper looks at how traditional banks are responding—whether by creating hybrid models that blend digital and in-person services, forming partnerships with tech innovators, or ramping up cybersecurity. The goal is to explore whether digital banking can coexist with traditional models to enhance the banking experience, or if it’s setting the stage for a future where traditional practices may be left behind. The findings aim to shed light on how banks can strike a balance between adopting new technology and maintaining the personalized, reliable service that has long defined their role in the financial industry.

Exploring Sentiment Analysis for the Indonesian Presidential Election Through Online Reviews Using Multi-Label Classification with a Deep Learning Algorithm

Presidential elections are an important political event that often trigger intense debate. With more than 139 million users, YouTube serves as a significant platform for understanding public opinion through sentiment analysis. This study aimed to implement deep learning techniques for a multi-label sentiment analysis of comments on YouTube videos related to the 2024 Indonesian presidential election. Offering a fresh perspective compared to previous research that primarily employed traditional classification methods, this study classifies comments into eight emotional labels: anger, anticipation, disgust, joy, fear, sadness, surprise, and trust. By focusing on the emotional spectrum, this study provides a more nuanced understanding of public sentiment towards presidential candidates. The CRISP-DM method is applied, encompassing stages of business understanding, data understanding, data preparation, modeling, evaluation, and deployment, ensuring a systematic and comprehensive approach. This study employs a dataset comprising 32,000 comments, obtained via YouTube Data API, from the KPU and Najwa Shihab channels. The analysis is specifically centered on comments related to presidential candidate debates. Three deep learning models—Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (Bi-LSTM), and a hybrid model combining CNN and Bi-LSTM—are assessed using confusion matrix, Area Under the Curve (AUC), and Hamming loss metrics. The evaluation results demonstrate that the Bi-LSTM model achieved the highest accuracy with an AUC value of 0.91 and a Hamming loss of 0.08, indicating an excellent ability to classify sentiment with high precision and a low error rate. This innovative approach to multi-label sentiment analysis in the context of the 2024 Indonesian presidential election expands the insights into public sentiment towards candidates, offering valuable implications for political campaign strategies. Additionally, this research contributes to the fields of natural language processing and data mining by addressing the challenges associated with multi-label sentiment analysis.

Pressure and Temperature Prediction of Oil Pipeline Networks Based on a Mechanism-Data Hybrid Driven Method

To ensure the operational safety of oil transportation stations, it is crucial to predict the impact of pressure and temperature before crude oil enters the pipeline network. Accurate predictions enable the assessment of the pipeline’s load-bearing capacity and the prevention of potential safety incidents. Most existing studies primarily focus on describing and modeling the mechanisms of the oil flow process. However, monitoring data can be skewed by factors such as instrument aging and pipeline friction, leading to inaccurate predictions when relying solely on mechanistic or data-driven approaches. To address these limitations, this paper proposes a Temporal-Spatial Three-stream Temporal Convolutional Network (TS-TTCN) model that integrates mechanistic knowledge with data-driven methods. Building upon Temporal Convolutional Networks (TCN), the TS-TTCN model synthesizes mechanistic insights into the oil transport process to establish a hybrid driving mechanism. In the temporal dimension, it incorporates real-time operating parameters and applies temporal convolution techniques to capture the time-series characteristics of the oil transportation pipeline network. In the spatial dimension, it constructs a directed topological map based on the pipeline network’s node structure to characterize spatial features. Data analysis and experimental results show that the Three-stream Temporal Convolutional Network (TTCN) model, which uses a Tanh activation function, achieves an error rate below 5%. By analyzing and validating real-time data from the Dongying oil transportation station, the proposed hybrid model proves to be more stable, reliable, and accurate under varying operating conditions.