Journal
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Volume 43, Issue 4, Pages 4215-4225Publisher
IOS PRESS
DOI: 10.3233/JIFS-213137
Keywords
Prediction; GM (1,1) model; Markov model; rainfall
Categories
Funding
- National Natural Science Foundation of China [51979106]
- Scientific and Technological Plan Project of Henan Province [182102310014]
- Key Research Project of Henan Universities [18A630030]
- Quality Curriculum Construction Project of Postgraduate Education in Henan Province (Grey Systems Theory) [HNYJS2015KC02]
- Postgraduate Innovative Project of North China University of Water Resources and Electric Power [YK2021-113]
Ask authors/readers for more resources
This paper proposes a pseudo-grey metabolic grey Markov model to deal with the prediction issue of original sequences that are oscillation sequences. The model not only provides new ideas for the data preprocessing problem of the grey Markov model, but also solves the problem of errors due to individual subjectivity in state interval division. It provides a novel idea for the development of grey prediction models. The rationality and validity of the model are illustrated by taking the Zhengzhou City of Henan Province as examples.
Purpose: The purpose of this paper is to propose a pseudo-grey metabolic grey Markov model to deal with the prediction issue in which the original sequences are oscillation sequences. Design/methodology/approach: First, the original sequences were processed with the accelerated advection transformation and the weighted mean generation transformation to make them smoother. Then, the mean GM (1, 1) model was applied to the multi-step prediction of the pre-processed data sequences. Finally, with the help of the optimal partitioning method, the pseudo-grey metabolic Markov model was used to correct the prediction results and determine the final prediction values. Findings: The results demonstrate that the accuracy of this model is significantly higher than that of the traditional grey Markov model, which further verifies the rationality of the proposed model. Therefore, scientific and reasonable prediction of urban rainfall is of great theoretical significance and application value for the government and decision-making departments to formulate drought prevention and disaster mitigation measures. Originality/value: The model in this paper not only provides new ideas for the data preprocessing problem of the grey Markov model, but also solves the problem of errors due to individual subjectivity in state interval division. It provides a novel idea for the development of grey prediction models. The rationality and validity of the model are illustrated by taking the Zhengzhou City of Henan Province as examples.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available