Loss Assessment and Response Strategies Based On ARIMA And TOPSIS Models Under Triple La Niña Events
DOI:
https://doi.org/10.54097/f6q09458Keywords:
Triple La Niña, SST, ARIMA model, TOPSIS model, Loss AssessmentAbstract
This study aims to develop forecasting and quantitative loss analysis models for potential triple La Niña events in different countries in order to assess and respond to potential La Niña disaster losses. First ,to build the ARIMA model, this study collected data from 2011 to 2020 and selected five indicators including Sea Surface Temperature (SST), Precipitation (PRCP), Temperature(TEMP), Standard Temperature and Pressure(STP) and Sea Level Pressure(SLP) that were more significantly correlated with the La Niña phenomenon, and then conducted correlation analysis using Pearson coefficient. Based on this, the research conducted principal component analysis to obtain three characteristic factors SST, PRCP and TEMP, and based on them, building ARIMA model to predict the possibility of La Niña occurrence in the future. Then, the study used the ADF test to check the smoothness. Second, this study carried out a TOPSIS evaluation to analyse the multiple damages caused by the high temperature and drought brought by the La Niña event in a country, taking into account the entropy weighting method-TOPSIS evaluation model, and determined the ranking of the indicators: La Niña event has the greatest impact on agriculture, followed by ecology and environment. Finally, the results of the score ranking were used to provide solutions. This study predicts the probability of future La Niña events through the development of two models, ARIMA and TOPSIS, assesses and analyses multi-indicator losses, comprehensively evaluates and ranks loss targets, and makes policy recommendations. The results of these studies contribute to the prevention and mitigation of losses caused by La Niña events, ensuring human safety and minimising economic losses.
Downloads
References
Shen, X. (2012). Influence of Arctic Oscillation and ENSO on extreme climate events in North China [Doctoral dissertation, Chinese Academy of Meteorological Sciences].
Long, Y., Xu, H., Yu, H., et al. (2023). Railway freight volume prediction based on ARIMA-LSTM-XGBoost hybrid model. Science Technology and Engineering, 23(25), 10879-10886
Ci, B., Zhang, P. (2022). Financial time series prediction based on ARIMA-LSTM model.Statistics and Decision, 38(11), 145-149. DOI: 10.13546/j.cnki.tjyjc.2022.11.029
Wu, H., Wang, J., Wu, W., et al. (2023). Analysis of global surface temperature prediction based on ARIMA model. Modern Information Technology, 7(16), 147-150.DOI: 10.19850/j.cnki.2096-4706.2023.16.032.
Bai, Y., Wang, X., & Guo, H. (2023). Characteristics and mechanism comparison of two "second cooling" La Niña events during 2020-2022 and 2010-2012. Journal of Ocean University of China (Natural Science Edition), 1-14. http://kns.cnki.net/kcms/detail/37.1414.P.20230904.0846.001.html
Zou, T., Guo, P. & Wu, Q. Applying an entropy weighted TOPSIS method to evaluate energy green consumption revolution progressing of China. Environ Sci Pollut Res 30, 42267–42281 (2023). https://doi.org/10.1007/s11356-023-25175-6
Sun, S., Bi, Z., Zhou, S., Wang, H., Li, Q., Liu, Y., … Zhou, Y. (2021). Spatiotemporal shifts in key hydrological variables and dominant factors over China. Hydrological Processes, 35(8). doi:10.1002/hyp.14319
Silva, K.A., de Souza Rolim, G. & de Oliveira Aparecido, L.E. Forecasting El Niño and La Niña events using decision tree classifier. Theor Appl Climatol 148, 1279–1288 (2022). https://doi.org/10.1007/s00704-022-03999-5
Li, Y., Strapasson, A., & Rojas, O. (2020). Assessment of El Niño and La Niña impacts on China: Enhancing the Early Warning System on Food and Agriculture. Weather and Climate Extremes, 27, 100208. ISSN 2212-0947. https://doi.org/10.1016/j.wace.2019.100208.
Liu, Y., Cai, W., Lin, X. et al. Nonlinear El Niño impacts on the global economy under climate change. Nat Commun 14, 5887 (2023). https://doi.org/10.1038/s41467-023-41551-9
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.






