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support vector regression model to predict TEC for GNSS signals

ACTA GEOPHYSICA 2022年第6期70卷 2827-2836页

作者： Sivakrishna, Kondaveeti Ratnam, Devanaboyina Venkata Sivavaraprasad, Gampala Koneru Lakshmaiah Educ Fdn Dept Elect & Commun Engn Guntur Andhra Pradesh India Malla Reddy Univ Sch Engn Hyderabad 500100 Telangana India

Ionospheric Total Electron Content (TEC) predominantly affects the radio wave communication and navigation links of Global Navigation Satellite Systems (GNSS). The ionospheric TEC exhibits a complex spatial-temporal pattern over equatorial and low latitude regions, which are difficult to predict for providing early warning alerts to GNSS users. Machine Learning (ML) techniques are proven better for ionospheric space weather predictions due to their ability of processing and learning from the available datasets of solar-geophysical data. Hence, a supervised ML algorithm such as the support vector regression (SVR) model is proposed to predict TEC over northern equatorial and low latitudinal GNSS stations. The vertical TEC data estimated from GPS measurements for the entire 24th solar cycle period, 11 years (2009-2019), is considered over Bengaluru and Hyderabad International GNSS Service (IGS) stations. The performance of the proposed SVR model with kernel Gaussian or Radial Basis Function (RBF) is evaluated over the two selected testing periods during the High Solar Activity (HSA) year, 2014 and the Low Solar Activity (LSA) year, 2019. The proposed model performance is compared with Neural Networks (NN) model, and International Reference Ionosphere (IRI-2016) model during both LSA and HSA periods. It is noticed that the proposed SVR model has well predicted the VTEC values better than NN and IRI-2016 models. The experimental results of the SVR model evidenced that it could be an effective tool for predicting TEC over low-latitude and equatorial regions.

关键词： Global Navigation Satellite System Total Electron Content IRI-2016 model Artificial Neural Network model support vector regression model

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State of Health Estimation for Lithium-Ion Batteries Using Enhanced Whale Optimization Algorithm for Feature Selection and support vector regression model

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PROCESSES 2025年第1期13卷 158-158页

作者： Wang, Rui Xu, Xikang Zhou, Qi Zhang, Jingtao Wang, Jing Ye, Jilei Wu, Yuping Nanjing Tech Univ Sch Energy Sci & Engn Nanjing 211816 Peoples R China South East Univ Sch Energy & Environm Nanjing 211189 Peoples R China

Evaluating the state of health (SOH) of lithium-ion batteries (LIBs) is essential for their safe deployment and the advancement of electric vehicles (EVs). Existing machine learning methods face challenges in the automation and effectiveness of feature extraction, necessitating improved computational efficiency. To address this issue, we propose a collaborative approach integrating an enhanced whale optimization algorithm (EWOA) for feature selection and a lightweight support vector regression (SVR) model for SOH estimation. Key features are extracted from charging voltage, current, temperature, and incremental capacity (IC) curves. The EWOA selects features by initially assigning weights based on importance scores from a random forest model. Gaussian noise increases population diversity, while a dynamic threshold method optimizes the selection process, preventing local optima. The selected features construct the SVR model for SOH estimation. This method is validated using four aging datasets from the NASA database, conducting 50 prediction experiments per battery. The results indicate optimal average absolute error (MAE) and root mean square error (RMSE) within 0.41% and 0.71%, respectively, with average errors below 1% and 1.3%. This method enhances automation and accuracy in feature selection while ensuring efficient SOH estimation, providing valuable insights for practical LIB applications.

关键词： state of health health feature enhanced whale optimization algorithm feature selection support vector regression model

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A support vector regression model for the prediction of total polyaromatic hydrocarbons in soil: an artificial intelligent system for mapping environmental pollution

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NEURAL COMPUTING & APPLICATIONS 2020年第18期32卷 14899-14908页

作者： Akinpelu, Adeola A. Ali, Md. Eaqub Owolabi, Taoreed O. Johan, Mohd R. Saidur, R. Olatunji, Sunday O. Chowdbury, Zaira Univ Malaya Nanotechnol & Catalysis Res Ctr Kuala Lumpur 50603 Malaysia King Fahd Univ Petr & Minerals Ctr Environm & Water Dhahran Saudi Arabia Adekunle Ajasin Univ Phys & Elect Dept Akungba Akoko 342111 Ondo State Nigeria Sunway Univ Res Ctr Nanomat & Energy Technol RCNMET Sch Sci & Technol 5 Jalan UnivBandar Sunway Petaling Jaya 47500 Malaysia Imam Abdulrahman Bin Faisal Univ Coll Comp Sci & Informat Technol Dept Comp Sci Dammam Saudi Arabia

The significance of total polyaromatic hydrocarbons (TPAH) determination in assessing the carcinogenicity of environmental samples for measuring the level of environmental pollution cannot be overemphasized. Despite the environmental danger of TPAH, its laboratory quantification is laborious, which consumes appreciable time and other valuable resources. This research work develops a computational intelligence-based model for the first time, which directly estimates and quantifies the level of TPAH of any environmental solid samples using total petroleum hydrocarbons descriptor that can be easily determined experimentally. The hyperparameters of the developed support vector regression (SVR)-based model are optimized using manual search (MS) approach and genetic algorithm (GA) search approach with Gaussian and polynomial kernel functions. Experimental validation of the developed model was carried out using samples obtained from the marine sediments of Arabian Gulf Sea. The future generalization and predictive strength of the developed models were assessed using correlation coefficient (CC), root-mean-square error, mean absolute error and mean absolute percentage deviation (MAPD). GA-SVR-Gaussian performs better than MS-SVR and GA-SVR-poly with performance enhancement of 63.89% and 536.32%, respectively, on the basis of MAPD as a performance-measuring parameter, while MS-SVR model performs better than GA-SVR-poly with performance improvement of 288.25% using MAPD to evaluate the model performance. The estimation accuracy and generalization strength of the developed models indicate the potential of the models in measuring the level of environmental pollution of oil-spilled area without experimental stress, while experimental precision is preserved.

关键词： support vector regression model Total polyaromatic hydrocarbon (TPAH) and total petroleum hydrocarbons (TPH) Artificial intelligence Pollution mapping

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Prediction of land surface temperatures for surface urban heat island assessment over Chandigarh city using support vector regression model

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SOLAR ENERGY 2019年 186卷 404-415页

作者： Mathew, Aneesh Sreekumar, Sreenu Khandelwal, Sumit Kumar, Rajesh Madanapalle Inst Technol & Sci Dept Civil Engn Madanapalle 517325 Andhra Pradesh India Malaviya Natl Inst Technol Jaipur Dept Elect Engn Jaipur 302017 Rajasthan India Malaviya Natl Inst Technol Jaipur Dept Civil Engn Jaipur 302017 Rajasthan India

Rapid urbanization is one of the primary reasons for changing the local climate, and there is a high impact on the surrounding areas. Chandigarh is one of the fastest developing cities in India showing rapidly urbanizing agglomeration. Due to the rapid urbanization, natural land surfaces are being replaced by the anthropogenic materials which negatively impacts the ecosystem resulting in urban heat island (UHI) effect. Land surface temperature (LST) is the primary and vital step for the analysis of UHI effect. The present study has been conducted to predict the LSTs for the assessment of UHI effect of the area surrounding Chandigarh city. Remote sensing data from Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor and Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation model (ASTER GDEM) have been used for the prediction of LST. In the study, support vector regression (SVR) model has been developed from LST values of previous three years along with enhanced vegetation index (EVI), road density (RD) and elevation as input parameters to predict LST. The results of the SVR model have been validated using the data of the year 2014. A comparison of the model estimated LST and measured LST indicates that the range of mean absolute error (MAE) and mean absolute percentage error (MAPE) varies between 0.521 K and 0.525 K and 0.181-0.187%, respectively. Hence, SVR model can be used as a significant tool to predict LST for the assessment of heat island effect at any location. From the sensitivity analysis, it is observed that LST was ultimately the most sensitive to the RD compared to EVI and elevation. The SVR model has been compared with artificial neural networks (ANN) model to estimate the skill score factor of the SVR model (Forecasted) with reference to the ANN (Referred) model. Skill scores calculated for the periods show positive values which clearly depicts the efficacy of SVR model compared to ANN model for better LST pred

关键词： Urban heat island Land surface temperature support vector regression model Enhanced vegetation index Digital elevation model Road density

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Optimization of variable blank holder force in deep drawing based on support vector regression model and trust region

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2019年第10期105卷 4265-4278页

作者： Feng, Yixiong Hong, Zhaoxi Gao, Yicong Lu, Runjie Wang, Yushan Tan, Jianrong Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Zhejiang Peoples R China Hefei Metalforming Machine Tool Co Ltd Hefei 230001 Anhui Peoples R China

Blank holder force (BHF) is one of the important process parameters for successful sheet metal forming. Variable blank holder force (VBHF) that the BHF varies through the forming process is recognized as one of the advanced manufacturing technologies. Therefore, optimization of VBHF trajectory is a crucial issue in industries. One of the effective approaches to determine the VBHF trajectory is to use the surrogate modeling techniques. However, it is very inaccurate and time-consuming to determine the VBHF trajectory for successful sheet forming through surrogate-based optimization methods. Therefore, this paper proposes an improved surrogate-based optimization method by integration of support vector regression (SVR) and trust region strategy to optimize VBHF in deep drawing. First, a random sampling test of VBHF in deep drawing is designed and a SVR approximate model of VBHF under random sampling is developed. Then, a trust region algorithm is adopted to predict and control the accuracy of the SVR approximate model of VBHF. Response surface is repeatedly constructed and optimized that is adopted to identify the Pareto-frontier of VBHF. The validity of the proposed approach is examined through the comparison of numerical and experimental results. The results of this research provide a reliable reference for future efforts to optimize VBHF in deep drawing.

关键词： Deep drawing Variable blank holder force support vector regression model Trust region

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The strategy of investment in the stock market using modified support vector regression model

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SCIENTIA IRANICA 2018年第3期25卷 1629-1640页

作者： Huang, C-H Yang, F-H Lee, C-P Da Yeh Univ PhD Program Management 168 Univ Rd Dacun 51591 Changhua Taiwan Da Yeh Univ Dept Int Business Management 168 Univ Rd Dacun 51591 Changhua Taiwan Natl Kaohsiung Marine Univ Dept Maritime Informat & Technol 482 Zhongzhou 3rd Rd Kaohsiung 805 Taiwan

Stock indices forecasting has become a popular research issue in recent years. Although many statistical time series models have been applied to stock indices forecasting, they are limited to certain assumptions. Accordingly, the traditional statistical time series models might not be suitable for forecasting real-life stock indices data. Hence, this paper proposes a novel forecasting model to assist investors in determining a strategy for investments in the stock market. The proposed model is called the modified support, vector regression model, which is composed of the correlation coefficient method, sliding window algorithm, and support, vector regression model. The results show that the forecasting accuracy of the proposed model is more stable than those of the existing models in terms of average and standard deviation of the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Accordingly, the proposed model would be used to assist investors in determining a strategy for investing in stocks. (C) 2018 Sharif University of Technology. All rights reserved.

关键词： Correlation coefficient support vector regression model Hybrid model Time series data forecasting Stock indices

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Forecasting riverine total nitrogen loads using wavelet analysis and support vector regression combination model in an agricultural watershed

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2018年第26期25卷 26405-26422页

作者： Ji, Xiaoliang Lu, Jun Zhejiang Univ Coll Environm & Nat Resources Hangzhou 310058 Zhejiang Peoples R China Zhejiang Univ Key Lab Environm Remediat & Ecol Hlth China Minist Educ Hangzhou 310058 Zhejiang Peoples R China

In the context of non-point source pollution management and algal blooms control, the reliable nutrient forecasting is of critical importance. Considering the highly stochastic, non-linear, and non-stationary natures involved in riverine total nitrogen (TN) load time series data, some traditional statistical and artificial intelligence models are inherently unable to give accurate nutrient forecasts due to their mechanism and structure characteristics. In this study, based on the wavelet analysis (WA) and support vector regression (SVR), a promising combined WA-SVR model was proposed for forecasting riverine TN loads. The data pro-processing tool WA was employed to decompose the time series data of riverine TN load for revealing its dominator. Subsequently, all wavelet components were used as inputs to SVR for WA-SVR model. The continuous riverine TN loads during 2004-2012 in the ChangLe River watershed of eastern China were estimated by using a calibrated Load Estimator model. Performance criteria, namely, determination coefficient (R-2), Nash-Sutcliffe model efficiency (NS), and mean square error (MSE) were applied to assess the performance of the developed models. The effects of different mother wavelets on the efficiency of the conjunction model were investigated. The results demonstrated that the mother wavelet played a crucial role for the successful implementation of the WA-SVR model. Among the 23 selected mother wavelet functions, dmey wavelet performed best in forecasting the daily and monthly TN loads. Furthermore, the performance of the optimal WA-SVR model was compared with that of single SVR model without wavelet decomposition. The comparison indicated that the hybrid model provided better accuracy than that of single SVR model. For daily riverine TN loads, the R-2, NS, and MSE values of WA-SVR model during the test stage were 0.9699, 0.9658, and 0.4885x10(7)kg/day, respectively. For monthly riverine TN loads, the R-2, NS, and MSE values of the model du

关键词： Forecasting Riverine TN loads Non-point source pollution Wavelet analysis support vector regression model

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A Novel Approach to Active Compounds Identification Based on support vector regression model and Mean Impact Value

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CHEMICAL BIOLOGY & DRUG DESIGN 2013年第5期81卷 650-657页

作者： Jiang, Jian-Lan Su, Xin Zhang, Huan Zhang, Xiao-Hang Yuan, Ying-Jin Tianjin Univ Tianjin Key Lab Biol & Pharmaceut Engn Key Lab Syst BioengnMinist Educ Dept Pharmaceut EngnSch Chem Engn & Technol Tianjin 300072 Peoples R China

Traditionally, active compounds were discovered from natural product extracts by bioassay-guided fractionation, which was with high cost and low efficiency. A well-trained support vector regression model based on mean impact value was used to identify lead active compounds on inhibiting the proliferation of the HeLa cells in curcuminoids from Curcuma longa L. Eight constituents possessing the high absolute mean impact value were identified to have significant cytotoxicity, and the cytotoxic effect of these constituents was partly confirmed by subsequent MTT (3-(4, 5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assays and previous reports. In the dosage range of 0.2211.2, 0.1140.2, 0.2149.9m, 50% inhibiting concentrations (IC50) of curcumin, demethoxycurcumin, and bisdemethoxycurcumin were 26.99 +/- 1.11, 19.90 +/- 1.22, and 35.51 +/- 7.29m, respectively. It was demonstrated that our method could successfully identify lead active compounds in curcuminoids from Curcuma longa L. prior to bioassay-guided separation. The use of a support vector regression model combined with mean impact value analysis could provide an efficient and economical approach for drug discovery from natural products.

关键词： active compounds identification Curcuma longa L cytotoxicity mean impact value support vector regression model

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Hybrid gray wolf optimization method in support vector regression framework for highly precise prediction of remaining useful life of lithium-ion batteries

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IONICS 2023年第9期29卷 3597-3607页

作者： Zhang, Mengyun Wang, Shunli Xie, Yanxin Yang, Xiao Hao, Xueyi Fernandez, Carlos Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Peoples R China Robert Gordon Univ Sch Pharm & Life Sci Aberdeen AB10 7GJ Scotland

The prediction of remaining useful life (RUL) of lithium-ion batteries takes a critical effect in the battery management system, and precise prediction of RUL guarantees the secure and reliable functioning of batteries. For the difficult problem of selecting the parameter kernel of the training data set of the RUL prediction model constructed based on the support vector regression model, an intelligent gray wolf optimization algorithm is introduced for optimization, and owing to the premature stagnation and multiple susceptibility to local optimum problems of the gray wolf algorithm, a differential evolution strategy is introduced to propose a hybrid gray wolf optimization algorithm based on differential evolution to enhance the original gray wolf optimization. The variance and choice operators of differential evolution are designed to sustaining the diversity of stocks, and then their crossover operations and selection operators are made to carry out global search to enhance the prediction of the model and realize exact forecast of the remaining lifetime. Experiments on the NASA lithium-ion battery dataset demonstrate the effectiveness of the proposed RUL prediction method. Experimental results demonstrate that the maximum average absolute value error of the prediction of the fusion algorithm on the battery dataset is limited to within 1%, which reflects the high accuracy prediction capability and strong robustness.

关键词： Lithium-ion battery Remaining useful life prediction support vector regression model Hybrid gray wolf optimization Differential evolution strategy

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Intelligent Deployment Solution for Tabling Adapting Deep Learning

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IEEE ACCESS 2023年 11卷 22201-22208页

作者： Keshun, You Huizhong, Liu Jiangxi Univ Sci & Technol Dept Mech & Elect Engn Ganzhou 341000 Peoples R China Jiangxi Min & Met Mech & Elect Engn Technol Res Ct Ganzhou 341000 Peoples R China

This article presents an intelligent deployment solution for tabling that utilizes deep learning techniques. The solution involves adapting deep learning semantic segmentation algorithms with DeepLab V3+ to extract multi-dimensional image features, enabling the mapping of relationships between mineral ore belt characteristics and operating parameters using a multi-output support vector regression model optimized using a sparrow search algorithm (ssa-msvr). The proposed solution integrates image recognition software and data processing method, which significantly improves the efficiency and effectiveness of mineral processing, providing a promising avenue for further research and development in this field.

关键词： Belts Ores Deep learning Feature extraction Semantic segmentation Optimization Intelligent systems Mineral processing deep learning semantic segmentation Index Terms multi-dimensional image features support vector regression model

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