A method for forecasting low-temperature disaster based on random forest and LSTM_NN

Hongliu Huang; Fangqiong Luo; Yiyuan Liu; Linli Jiang

doi:10.1117/12.2636459

6 May 2022 A method for forecasting low-temperature disaster based on random forest and LSTM_NN

Hongliu Huang, Fangqiong Luo, Yiyuan Liu, Linli Jiang

Proceedings Volume 12176, International Conference on Algorithms, Microchips and Network Applications; 1217626 (2022) https://doi.org/10.1117/12.2636459
Event: International Conference on Algorithms, Microchips, and Network Applications 2022, 2022, Zhuhai, China

Abstract

Aiming at solving the difficulties of modeling factor processing and forecast modeling caused by time correlation and non-linear variation of low-temperature disastrous weather processes during winter, this paper takes the winter low-temperature cold damage index calculated from the daily temperature and precipitation data in Guangxi as the forecast factor. Based on the NCEP/NCAR reanalysis data and forecast field data, a large number of characteristic factors are extracted by using the random forest algorithm (RFS). Combined with the long-short-term memory neural network (LSTM NN) in the nonlinear time series memory characteristics and nonlinear timing dynamic system control advantages, we established a model for low-temperature cold hazard forecast with 72 hours based on the random forest and LSTM_NN. Experimental results indicate that the current method has high prediction accuracy, good stability, and displays better applicability to nonlinear low-temperature cold hazard forecasting.

Citation Download Citation

Hongliu Huang, Fangqiong Luo, Yiyuan Liu, and Linli Jiang "A method for forecasting low-temperature disaster based on random forest and LSTM_NN", Proc. SPIE 12176, International Conference on Algorithms, Microchips and Network Applications, 1217626 (6 May 2022); https://doi.org/10.1117/12.2636459

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