@inproceedings{de76d405887c49f580bc77805cc35c96,
title = "Disaster Information Digitization for Intelligent Forecast in Tarim River Basin Using Multiplicative Seasonal ARIMA Model",
abstract = "The Tarim River Basin is prone to flood disasters with large human and property losses due to its topographic structure. This article aims to explore the value of multiplicative seasonal ARIMA(SARIMA) model in predicting flood disasters in the Tarim River Basin, and to find out the pattern of occurrence and change of disasters. Using the flood disaster data in the Tarim Basin from 1980 to 2019, a traditional SARIMA model was created and the additive and level shift sequence were adjusted. The results showed that the prediction effect of the adjusted model SARIMA (0,0,1)(0,1,1))12. The stationary \$\textbackslash{}mathrm\{R\}\textasciicircum{}\{2\}\$ is 0.755. The Ljung-Box Q test shows that the statistics Q=16.254,P=0.435. The average number of flood disasters in 2022 is predicted to be 2.44, and the peak period of flooding is concentrated in May-August. The model has a certain reference value for predicting the future trend of disasters.",
keywords = "SARIMA, Tarim, flood disaster, prediction, time series",
author = "Baoxin Chen and Kan Chen and Xi Wang and Xu Wang",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Conference on Telecommunications, Optics and Computer Science, TOCS 2021 ; Conference date: 10-12-2021 Through 11-12-2021",
year = "2021",
doi = "10.1109/TOCS53301.2021.9688657",
language = "English",
series = "2021 IEEE Conference on Telecommunications, Optics and Computer Science, TOCS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "836--839",
booktitle = "2021 IEEE Conference on Telecommunications, Optics and Computer Science, TOCS 2021",
address = "United States",
}