Ground-level O3 sensitivity analysis using support vector machine with radial basis function

Mehdipour, Vahid; Memarianfard, M.

doi:10.1007/s13762-018-1770-3

Cited by 14 publications

(2 citation statements)

References 39 publications

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“…Statistics models are generally utilized involving Multiple Linear Regression (MLR) (Awang et al, 2015), Principal Component Analysis (PCA) (Wuttichaikitcharoen & Babel, 2014). Nowadays, ML techniques are speedily improved in order to empower the standard environmental predictions performances because of their nonlinear mapping capability, like Support Vector Machine (SVM) (He et al, 2017;Mehdipour & Memarianfard, 2019;Mehdipour et al, 2018;Sumathi & Manivannan, 2020), Extreme Gradient Boosting (XGBoost) (Ma et al, 2020), Bayesian Network (Mehdipour et al, 2018), and Random Forest (Feng et al, 2019). The previously mentioned ML techniques deal with nonlinearity cases and showed the high capability to 69 captures temporal characteristics of renewable resources as well as air pollutants.…”

Section: Time Series Prediction Techniquesmentioning

confidence: 99%

Hybrid deep learning model for ozone concentration prediction: comprehensive evaluation and comparison with various machine and deep learning algorithms

Yafouz

Ahmed

Zaini

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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To accurately predict tropospheric ozone concentration(O 3 ), it is needed to investigate the variety of artificial intelligence techniques' performance, such as machine learning, deep learning and hybrid models. This research aims to effectively predict the hourly ozone trend via fewer input variables. This ozone prediction attempt is performed on diversity data of air pollutants (NO 2 , NO x , CO, SO 2 ) and meteorological parameters (wind-speed and humidity). The historical datasets are collected from 3 sites in Malaysia. The study's methodology progressed in two paths: standalone and hybrid models where hourly-averaged datasets are applied based on 5-time horizon analysis scenario, with different inputs' combinations. For evaluation, all models are tested throughout 5-performance indicator and illustrated on Modified Taylor diagram. Sensitivity analysis of input variables is quantified. Additionally, uncertainty analysis is conducted to assess their confidence level associated with Willmott Index. Based on R 2 , results indicated that XGBoost has higher accuracy compared to MLP and SVR; meanwhile, LSTM and CNN outweighs XGBoost. In terms of robustness and accuracy, the proposed hybrid model possesses superlative performance compared to all above-mentioned techniques. The proposed model achieved exceptional results as the highest R 2 , the highest 95% confidence degree, and narrower confidence interval width, are 93.48%, 98.16%, and 0.0014195, respectively.

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Section: Time Series Prediction Techniquesmentioning

confidence: 99%

Hybrid deep learning model for ozone concentration prediction: comprehensive evaluation and comparison with various machine and deep learning algorithms

Yafouz

Ahmed

Zaini

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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“…Normally, to solve the problem of linear inseparability in low-dimensional space, kernel functions are used to map the features from low-dimensional space to high-dimensional space, thus realizing the linear classification in higher-dimensional space. In the experiments, the radial basis function (RBF) [ 34 ] was used as the kernel function of SVM to classify the CF, the DLF, and the MF, respectively, and the classification results were evaluated by various evaluation indexes.…”

Section: Deep Learning Features (Dlf) Extraction and Svm-based Classificationmentioning

confidence: 99%

Multi‐Features‐Based Automated Breast Tumor Diagnosis Using Ultrasound Image and Support Vector Machine

Zhuang

Yang

Zhuang

et al. 2021

Computational Intelligence and Neuroscience

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Breast ultrasound examination is a routine, fast, and safe method for clinical diagnosis of breast tumors. In this paper, a classification method based on multi-features and support vector machines was proposed for breast tumor diagnosis. Multi-features are composed of characteristic features and deep learning features of breast tumor images. Initially, an improved level set algorithm was used to segment the lesion in breast ultrasound images, which provided an accurate calculation of characteristic features, such as orientation, edge indistinctness, characteristics of posterior shadowing region, and shape complexity. Simultaneously, we used transfer learning to construct a pretrained model as a feature extractor to extract the deep learning features of breast ultrasound images. Finally, the multi-features were fused and fed to support vector machine for the further classification of breast ultrasound images. The proposed model, when tested on unknown samples, provided a classification accuracy of 92.5% for cancerous and noncancerous tumors.

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Predicting Surface Ozone Levels in Eastern Croatia: Leveraging Recurrent Fuzzy Neural Networks with Grasshopper Optimization Algorithm

Braik,

Sheta,

Kovač-Andrić

et al. 2024

Water Air Soil Pollut

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Ground-level O3 sensitivity analysis using support vector machine with radial basis function

Cited by 14 publications

References 39 publications

Hybrid deep learning model for ozone concentration prediction: comprehensive evaluation and comparison with various machine and deep learning algorithms

Hybrid deep learning model for ozone concentration prediction: comprehensive evaluation and comparison with various machine and deep learning algorithms

Multi‐Features‐Based Automated Breast Tumor Diagnosis Using Ultrasound Image and Support Vector Machine

Predicting Surface Ozone Levels in Eastern Croatia: Leveraging Recurrent Fuzzy Neural Networks with Grasshopper Optimization Algorithm

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