scholarly journals Forecast and Early Warning of Regional Bus Passenger Flow Based on Machine Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Wusheng Liu ◽  
Qian Tan ◽  
Wei Wu
Keyword(s):  
Machine Learning ◽  
Early Warning ◽  
Integrated Circuit ◽  
Large Scale ◽  
Effective Control ◽  
Support Vector ◽  
Short Term ◽  
Early Warning Model ◽  
Passenger Flow ◽  
Warning Model

This paper mainly forecasts the short-term passenger flow of regional bus stations based on the integrated circuit (IC) card data of bus stations and puts forward an early warning model for regional bus passenger flow. Firstly, the bus stations were aggregated into virtual regional bus stations. Then, the short-term passenger flow of regional bus stations was predicted by the machine learning (ML) method of support vector machine (SVM). On this basis, the early warning model for regional bus passenger flow was developed through the capacity analysis of regional bus stations. The results show that the prediction accuracy of short-term passenger flow could be improved by replacing actual bus stations with virtual regional bus stations because the passenger flow of regional bus stations is more stable than that of a single bus station. The accurate prediction and early warning of regional bus passenger flow enable urban bus dispatchers to maintain effective control of urban public transport, especially during special and large-scale activities.

scholarly journals Research on Pump Inspection Cycle Early Warning Method Based on Big Data

2021 ◽  
Vol 245 ◽  
pp. 02026
Author(s):  
Du Lihong ◽  
Liu Yufang ◽  
Cao Fei ◽  
Li Fang ◽  
Min Guizhi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Influencing Factors ◽  
Early Warning ◽  
Learning Technology ◽  
Inspection Period ◽  
Engineering Technology ◽  
Early Warning Model ◽  
Single Well ◽  
Warning Model

At present, the existing indicator diagram can only be used for expost judgment and can not give early warning, and the influencing factors of pump inspection period are nonlinear, multi constrained and multi variable. In this paper, big data machine learning method is used to carry out relevant research. Firstly, around the influencing factors of pump inspection cycle, relevant data are collected and the evaluation index of pump inspection cycle is designed. Then, based on feature engineering technology, the production parameters of oil wells in different pump inspection periods are calculated to form the analysis sample set of pump inspection period. Finally, the early warning model of pump inspection period is established by using machine learning technology. The experimental results show that: the pump inspection cycle early warning model established by stochastic forest algorithm can identify the pump inspection status of single well, and the accuracy rate is about 85%.

scholarly journals Intelligent Informatization Early Warning Analysis of Agricultural Economy Based on Support Vector Sequential Regression Model

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ying Yang
Keyword(s):  
Agricultural Economic ◽  
Early Warning ◽  
Science And Technology ◽  
Internet Technology ◽  
Economic Research ◽  
Support Vector ◽  
Agricultural Economy ◽  
Early Warning Model ◽  
Warning Model ◽  
Sequential Regression

The development of science and technology has laid a solid foundation for the economic informatization of agriculture, and at the same time it brought technical guarantee for the development of agriculture, and the development of agriculture has provided an important material foundation for the development of science and technology. How to identify and deeply study agricultural economic informatization, give early warning to risk information, and ensure the steady development of the whole industry has become a key issue in the application of Internet technology in the field of agricultural development. This paper studies the present situation of agricultural economy informatization development process and applies support vector machine to forecast regional economic development level. The warning limit of agricultural economic growth rate is obtained on the basis of warning situation and warning indicator in early warning index system. The economic early warning model is established based on the support vector sequential regression method, and then the data is trained by MATLAB software to verify the rationality of the early warning model, and the accuracy and corresponding error of the model are given. Experimental results show that the prediction accuracy is 99.3%, the error is less than 0.05, and the prediction effect is relatively ideal, for agricultural economic intelligence information to provide accurate warning and agricultural economic research agricultural commercial development to provide support.

scholarly journals Research of Financial Early-Warning Model on Evolutionary Support Vector Machines Based on Genetic Algorithms

2009 ◽  
Vol 2009 ◽  
pp. 1-8
Author(s):  
Zuoquan Zhang ◽  
Fan Lang ◽  
Qin Zhao
Keyword(s):  
Genetic Algorithms ◽  
Support Vector Machines ◽  
Early Warning ◽  
Support Vector ◽  
Early Warning Model ◽  
Vector Machines ◽  
New Learning ◽  
And Performance ◽  
Learning Machine ◽  
Warning Model

A support vector machine is a new learning machine; it is based on the statistics learning theory and attracts the attention of all researchers. Recently, the support vector machines (SVMs) have been applied to the problem of financial early-warning prediction (Rose, 1999). The SVMs-based method has been compared with other statistical methods and has shown good results. But the parameters of the kernel function which influence the result and performance of support vector machines have not been decided. Based on genetic algorithms, this paper proposes a new scientific method to automatically select the parameters of SVMs for financial early-warning model. The results demonstrate that the method is a powerful and flexible way to solve financial early-warning problem.

Research on grain storage temperature prediction model based on improved long short-term memory

2021 ◽  
pp. 1-10
Author(s):  
Liang Ge ◽  
Enhong Chen
Keyword(s):  
Early Warning ◽  
Short Term Memory ◽  
Storage Temperature ◽  
Grain Storage ◽  
Short Term ◽  
Term Memory ◽  
Early Warning Model ◽  
Long Short Term Memory ◽  
Warning Model ◽  
Grain Temperature

Stored-grain temperature is the most important factor in grain storage. According to the measured data, the temperature in the grain pile can be effectively predicted, which can find problems in advance, reduce grain loss and increase grain quality. Long Short-Term memory (LSTM) can perform better in longer sequences than ordinary RNN. This paper is applied to the analysis of big data of grain storage and the early warning of grain storage temperature. In this paper, the selected LSTM is optimized and the early warning model of grain situation is established, and the analysis steps of the early warning model are given. In order to verify the availability of the improved LSTM network structure, RNN and three variants were used to predict the grain temperature under the same conditions, the prediction effect of the improved CLSTM is better.

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