A traffic flow forecasting method based on the GA-SVR

Zhan, Aiyun; Du, Fei; Chen, Zhaozheng; Yin, Guanxiang; Wang, Meng; Zhang, Yuejin

doi:10.3233/jhs-220682

Cited by 11 publications

(4 citation statements)

References 15 publications

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“…There are two key parameters c and g in SVR, c is called the regularization parameter and g is called the kernel parameter. The size of the c value determines the tolerance of the model to error, and the size of the g value determines the influence range of each training sample [20][21][22].…”

Section: Modelingmentioning

confidence: 99%

Small Sample Sound Quality Prediction Method of Hy-Vo Chain Transmission System Based on Fuzzy Generation

Li,

An,

Cheng

et al. 2024

Applied Sciences

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To improve the noise comfort of the whole machine, it is necessary to establish the sound quality prediction model of the Hy-Vo chain transmission system. Compared with the silent chain transmission system, the Hy-Vo chain transmission system normally operates at a lower speed and cannot have too much load at the limit speed. It is difficult to obtain a sufficient quantity of high-quality noise samples because there are few different working conditions. For small sample sound quality prediction, we use a sample enhancement method called fuzzy generation based on fuzzy mathematics. Firstly, audio samples of the Hy-Vo chain transmission system are collected through noise tests. Secondly, the processed samples are evaluated objectively and subjectively. After a correlation test of the subjective evaluation results, correct subjective evaluation scores of each noise sample are obtained. With the help of fuzzy generation, we can obtain a sufficient number of new samples. By mixing the original samples with the generated samples, a new dataset is created. Through using a general regression neural network (GRNN), support vector regression (SVR) model, and ridge regression (RR) method, the sound quality of the Hy-Vo chain transmission system can be predicted. Different from prediction results under the original dataset, using the fuzzy generation method can not only significantly reduce the prediction error of the model but also improve stability.

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Section: Modelingmentioning

confidence: 99%

Small Sample Sound Quality Prediction Method of Hy-Vo Chain Transmission System Based on Fuzzy Generation

Li,

An,

Cheng

et al. 2024

Applied Sciences

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“…Although SVR can better solve the small sample and nonlinear problems and has been used in many fields, the values of its parameters directly affect the accuracy of SVR model prediction [32]. However, there is no clear method to determine the values of parameters, and most studies have been manually selecting parameter values relying on experience, making it difficult to obtain optimal values of parameters [33].…”

Section: ) Genetic Algorithm To Optimize Support Vector Regressionmentioning

confidence: 99%

A Method Based on Improved Ant Colony Algorithm Feature Selection Combined With GA-SVR Model for Predicting Chlorophyll-a Concentration in Ulansuhai Lake

Wu,

Fu,

et al. 2023

IEEE Access

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Chlorophyll-a (Chl-a) is an important parameter of water bodies, but due to the complexity of optics in water bodies, it is currently difficult to accurately predict Chl-a concentration in water bodies by traditional methods. In this paper, Sentinel-2 remote sensing images is used as the data source combined with measured data, and Ulansuhai Lake is taken as the study area. An adaptive ant colony exhaustive optimization (A-ACEO) algorithm is proposed for feature selection and combined with a novel intelligent algorithm of optimizing support vector regression (SVR) by genetic algorithm (GA) for prediction of Chl-a concentration. The ant colony optimization (ACO) algorithm is improved to select remote sensing feature bands for Chl-a concentration by introducing relevant optimization strategies. The GA-SVR model is built by optimizing SVR using GA with the selected feature bands as input, and comparing with the traditional SVR model. The simulation results show that under the same conditions, using A-ACEO algorithm to select feature bands as inputs can effectively reduce the model complexity, and improve the model prediction performance, which provides a valuable reference for monitoring Chl-a concentration in lakes.

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“…Inappropriate parameter selection may lead to underfitting or overfitting, and different parameter settings could result in significant differences in performance [23][24][25]. Various optimization algorithms have been used to optimize the SVR and further improve the inversion accuracy of water quality parameters, e.g., Gray Wolf Optimizer Algorithm (GWO) [26], Genetic Algorithm (GA) [27], Particle Swarm Optimization Algorithm (PSO) [28], Ant Colony Optimization algorithm (ACO) [29], Firefly Algorithm (FA) [30], Sparrow Search Algorithm (SSA) [31], Hunter-Prey Algorithm (HPO) [32], etc. These optimization algorithms can search for the optimal solution in the parameter space and help to adjust the parameters of the SVR model, thus improving the inversion accuracy.…”

Section: Introductionmentioning

confidence: 99%

Water Quality Prediction of Small-Micro Water Body Based on the Intelligent-Algorithm-Optimized Support Vector Machine Regression Method and Unmanned Aerial Vehicles Multispectral Data

Yao,

Chen,

et al. 2024

Sustainability

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Accurate prediction of spatial variation in water quality in small microwaters remains a challenging task due to the complexity and inherent limitations of the optical properties of small microwaters. In this paper, based on unmanned aerial vehicles (UAV) multispectral images and a small amount of measured water quality data, the performance of seven intelligent algorithm-optimized SVR models in predicting the concentration of chlorophyll (Chla), total phosphorus (TP), ammonia nitrogen (NH3-N), and turbidity (TUB) in small and micro water bodies were compared and analyzed. The results show that the Gray Wolf optimized SVR model (GWO-SVR) has the highest comprehensive performance, with R2 of 0.915, 0.827, 0.838, and 0.800, respectively. In addition, even when dealing with limited training samples and different data in different periods, the GWO-SVR model also shows remarkable stability and portability. Finally, according to the forecast results, the influencing factors of water pollution were discussed. This method has practical significance in improving the intelligence level of small and micro water body monitoring.

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A traffic flow forecasting method based on the GA-SVR

Cited by 11 publications

References 15 publications

Small Sample Sound Quality Prediction Method of Hy-Vo Chain Transmission System Based on Fuzzy Generation

Small Sample Sound Quality Prediction Method of Hy-Vo Chain Transmission System Based on Fuzzy Generation

A Method Based on Improved Ant Colony Algorithm Feature Selection Combined With GA-SVR Model for Predicting Chlorophyll-a Concentration in Ulansuhai Lake

Water Quality Prediction of Small-Micro Water Body Based on the Intelligent-Algorithm-Optimized Support Vector Machine Regression Method and Unmanned Aerial Vehicles Multispectral Data

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