An urban short-term traffic flow prediction model based on wavelet neural network with improved whale optimization algorithm

Du, Wangdi; Zhang, Qingyong; Chen, Yuepeng; Ye, Ziliu

doi:10.1016/j.scs.2021.102858

Cited by 51 publications

(18 citation statements)

References 15 publications

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“…The research shows that the WNN model has a certain effect, but the effect is not as good as the SVMR-FDR model. Du et al [ 12 ] combined a whale optimization algorithm (IWA) with the WNN to build the IWA-WNN model to predict urban traffic flow. The research results show that the prediction speed and accuracy of the model are better than those of the traditional WOA-WNN model.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Analysis of Landscape Ecological Planning Based on the High‐Order Multiwavelet Neural Network Algorithm

Yu¹,

2021

Computational Intelligence and Neuroscience

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Landscape architecture has both natural and social properties, which is the embodiment of people protecting the natural environment. Since the industrial revolution, the modern industry has developed rapidly. It has increased the living standard of people and consumed a lot of natural resources such as forest and energy. The ecological environment has been greatly damaged, and the landscape of gardens has been affected. Therefore, it is of great significance to find a method to evaluate the landscape ecology and plan the landscape ecology. This paper proposes a new high-order wavelet neural network algorithm combining wavelet analysis and artificial neural network. A model of ecological evaluation of landscape based on high-order wavelet neural network algorithm is proposed to evaluate the landscape ecology and provide reference data for the ecological planning of the landscape. The results show that the training times of the wavelet neural network to achieve the target accuracy are 3600 times less than those of the BP neural network. The MSE and MAE of the WNN are 0.0639 and 0.1501, respectively. The average error of the model to the comprehensive evaluation index of the landscape ecology is 0.005. The accuracy of the model to evaluate the sustainability of landscape land resources is 98.67%. The above results show that the model based on the wavelet neural network can effectively and accurately complete the evaluation of landscape ecology and then provide a decision-making basis for landscape ecological planning, which is of high practicability.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Analysis of Landscape Ecological Planning Based on the High‐Order Multiwavelet Neural Network Algorithm

Yu¹,

2021

Computational Intelligence and Neuroscience

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“…The past confirmatory research has transitioned to the current practical stage. Although the scope of application and application conditions of the neural network is still explored, its broad development prospects can already be seen (Du et al, 2021 ).…”

Section: College Students' Entrepreneurial Psychologymentioning

confidence: 99%

The Effect of Law Students in Entrepreneurial Psychology Under the Artificial Intelligence Technology

Zhang

2021

Front. Psychol.

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With the increasingly serious employment situation in China, the government and schools encourage college students to start businesses to alleviate employment pressure. College student's successful entrepreneurship depends on national preferential policies, social support, and, most importantly, their healthy and solid psychological quality and entrepreneurial psychological quality. The purpose is to understand the entrepreneurial psychology of college students and study the entrepreneurial psychological effect. Firstly, the four aspects of entrepreneurial psychology are summarized, including entrepreneurial awareness, entrepreneurial volition, entrepreneurial ability, and entrepreneurial personality. Secondly, the research status of college students' entrepreneurial psychology is reviewed, and the existing problems are pointed out. Thirdly, the combined model of wavelet transform and Neural Network (NN) is proposed, and the feasibility of the proposed model is evaluated through the analysis of college students' entrepreneurial psychology. The wavelet NN is used in experimental design to predict college students' entrepreneurial psychology, and the predicted results are compared with the actual value. From the perspective of the prediction results of entrepreneurial psychology, the combination of wavelet algorithm and neural network is more accurate for entrepreneurial psychology prediction and evaluation results of law students. Overall, the difference between the predicted value and the actual value is within 0.3 points, which is relatively stable. According to the analysis of single-factor results, the scores of students of different majors in the four dimensions of entrepreneurial psychology are all higher than 3.5, but there is no significant difference among the four dimensions (P > 0.05), indicating that the major has no significant impact on entrepreneurial psychology; law students with different educational backgrounds have significant differences in entrepreneurial psychology (P < 0.05), among which students with a master's degree have the strongest entrepreneurial will, while doctoral students have the lowest entrepreneurial will; in terms of entrepreneurial psychological capital, men's self-efficacy is higher than women's, and the difference is significant (P < 0.05). The difference between males and females in the scores of entrepreneurial psychological factors' four aspects is not very obvious. In terms of entrepreneurial psychological capital, males' self-efficacy is significantly higher than females' (P < 0.05). Artificial Intelligence (AI) technology has great application prospects in the prediction and evaluation of college students' entrepreneurial psychology, and college students' entrepreneurial psychology is highly correlated with gender and education.

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“…This study introduces the Whale Optimization Algorithm (WOA) to enhance the predictive ability of the GRU model. The WOA algorithm has a simpler structure, fewer internal parameters, and stronger search capabilities than other optimization algorithms (Du et al 2021).…”

Section: Introductionmentioning

confidence: 99%

An Integrated Framework of GRU Based on Improved Whale Optimization Algorithm for Flood Prediction

Tian

Zhang

et al. 2021

Preprint

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Accurate prediction of floods is the first step in formulating flood control strategies and reducing flood disasters. This research proposes a deep learning model based on Gate Recurrent Unit (GRU), Random Forest Algorithm (RF), Whale Optimization Algorithm (WOA) and Optimal Variational Mode Decomposition (OVMD) for flood prediction. First, the random historical time series is decomposed using OVMD. Secondly, combined with the RF feature importance measurement, select features with high importance to obtain the optimal input set. Third, use the GRU model to predict all sub-models, and use the WOA algorithm to optimize the hyperparameters in the GRU model. This study also proposes a hybrid strategy to improve the traditional WOA algorithm and enhance the optimization ability of the WOA algorithm. Finally, the prediction results of all sub-modes were aggregated to generate the final prediction result. The model was validated using data from three hydrological stations in the upper, middle and lower reaches of the Minjiang river basin in China. Through the results of the experiment, it can be seen that the proposed prediction model can effectively predict the flood time series, and has better accuracy than other models.

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An urban short-term traffic flow prediction model based on wavelet neural network with improved whale optimization algorithm

Cited by 51 publications

References 15 publications

[Retracted] Analysis of Landscape Ecological Planning Based on the High‐Order Multiwavelet Neural Network Algorithm

[Retracted] Analysis of Landscape Ecological Planning Based on the High‐Order Multiwavelet Neural Network Algorithm

The Effect of Law Students in Entrepreneurial Psychology Under the Artificial Intelligence Technology

An Integrated Framework of GRU Based on Improved Whale Optimization Algorithm for Flood Prediction

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