research-article

Application of Stochastic Differential Equation in Atmospheric Temperature Prediction

Authors:

Mingming Xu,

Cijun Fang,

Desheng TianAuthors Info & Claims

ICoMS '22: Proceedings of the 2022 5th International Conference on Mathematics and Statistics

Pages 120 - 125

https://doi.org/10.1145/3545839.3545859

Published: 10 September 2022 Publication History

Get Access

Abstract

Stochastic differential equations are widely used in atmospheric temperature prediction. This paper based on the established stochastic differential logistic model of global average temperature, it takes Euler's numerical model method to estimate the parameter values and analyze, evaluate the model fitting. The prediction efficiency of the model is determined thereby. Besides, the global average temperature for the next few years is predicted as stated in this paper. Finally, by analyzing the main research variable of global average temperature - environmental capacity, it shows that that if human beings do not get controlled, the global average temperature will continue to increase.

References

[1]

Mingming Xu, Desheng Tian. 2021. A Stochastic Differential Equation Model of Air Temperature. Journal of Hubei University of Technology, 36(05): 112-116.

Google Scholar

[2]

Blane Hollingsworth. Stochastic differential equations: a dynamical systems approach. Harbin Institute of Technology Press, 2021.

Google Scholar

[3]

Sili Wang. 2017. Parameter estimation of several types of random differential equations. Nanjing University of Technology.

Google Scholar

[4]

Zhiqiang Zhang. 2019. Numerical solution of a class of fractional stochastic differential equations and its application. Henan University.

Google Scholar

[5]

Hong Wang. 2009. Application of Numerical Solution of Stochastic Differential Equations in Reservoir Flood Regulation. Jilin University.

Google Scholar

[6]

Juan Li. 2018. The stability of the random differential equation. The University of Science and Technology of China.

Google Scholar

[7]

Dandan Hu. 2017. Logistic distribution parameters under several data types of the approximate large likelihood estimation of two parameters. Shanghai Normal University.

Google Scholar

[8]

Xinrui Cai, Lijin Wang. 2017. Several parameter estimation methods for stochastic differential equations. Journal of the University of Chinese Academy of Sciences, 34(05): 529-537.

Google Scholar

[9]

Xiaohua Hu. 2011. Logistic curve parameter estimation and application. Mathematical theory and application, 12 (4): 26-50.

Google Scholar

[10]

Jianqing Zhai, Tanlong Dai, Guofu Wang. 2021. Global climate characteristics and major weather and climate events in 2020.Meteorology, v. 47; No. 556(04): 471-477.

Google Scholar

Index Terms

Application of Stochastic Differential Equation in Atmospheric Temperature Prediction
1. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
      1. Continuous optimization
        Stochastic control and optimization

Recommendations

Brief Infinite horizon backward stochastic differential equation and exponential convergence index assignment of stochastic control systems

This paper studies exponential convergence index assignment of stochastic control systems from the viewpoint of backward stochastic differential equation. Like deterministic control systems, it is shown that the exact controllability of an open-loop ...
Three-stage stochastic Runge-Kutta methods for stochastic differential equations

In this paper we discuss three-stage stochastic Runge-Kutta (SRK) methods with strong order 1.0 for a strong solution of Stratonovich stochastic differential equations (SDEs). Higher deterministic order is considered. Two methods, a three-stage explicit ...
Classification of stochastic Runge-Kutta methods for the weak approximation of stochastic differential equations

In the present paper, a class of stochastic Runge-Kutta methods containing the second order stochastic Runge-Kutta scheme due to E. Platen for the weak approximation of Ito stochastic differential equation systems with a multi-dimensional Wiener process ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

ICoMS '22: Proceedings of the 2022 5th International Conference on Mathematics and Statistics

June 2022

137 pages

ISBN:9781450396233

DOI:10.1145/3545839

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 September 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICoMS 2022

ICoMS 2022: 2022 5th International Conference on Mathematics and Statistics

June 17 - 19, 2022

Paris, France

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

0
Total Citations
52
Total Downloads

Downloads (Last 12 months)29
Downloads (Last 6 weeks)2

Reflects downloads up to 18 Nov 2024

Other Metrics

View Author Metrics

Citations

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Abstract

References

Index Terms

Recommendations

Brief Infinite horizon backward stochastic differential equation and exponential convergence index assignment of stochastic control systems

Three-stage stochastic Runge-Kutta methods for stochastic differential equations

Classification of stochastic Runge-Kutta methods for the weak approximation of stochastic differential equations

Comments

Information

Published In

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Login options

Full Access

View options

PDF

eReader

HTML Format

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations