Research and quantitative analysis of ecological development trend of Saihanba forest farm
- DOI
- 10.2991/978-94-6463-415-0_80How to use a DOI?
- Keywords
- TOPSIS algorithm; Saihanba; Ecological civilization; Sandstorm; The environment
- Abstract
The cluster analysis method was used to select the more important influencing factors and related data in the ecological environment of Saihanba Forest Farm, and the evaluation was evaluated into three more systematic evaluation indicators. The analytic hierarchy process was used to determine the weight of each influencing factor, and the ecological environment quality evaluation index system of Saihanba Forest Farm was established. This paper takes Beijing’s ability to resist dust storms as an example for evaluation and analysis. Firstly, the entropy weight method-TOPSIS model is used to judge the severity of dust storm changes in Beijing. Secondly, six Saihanba environmental index parameters are selected to establish a mathematical model of Saihanba for Beijing’s ability to resist dust storms. Experiments show that the model provides an important basis for the positive effect of the restoration of Saihanba Forest Farm on Beijing’s ability to resist dust storms.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Huichan Li AU - Linshi Zhu AU - Maojie Pan PY - 2024 DA - 2024/05/14 TI - Research and quantitative analysis of ecological development trend of Saihanba forest farm BT - Proceedings of the 2023 9th International Conference on Advances in Energy Resources and Environment Engineering (ICAESEE 2023) PB - Atlantis Press SP - 751 EP - 765 SN - 2589-4943 UR - https://doi.org/10.2991/978-94-6463-415-0_80 DO - 10.2991/978-94-6463-415-0_80 ID - Li2024 ER -