Moisture Content Prediction Model for Hourly Steps of Small Dead Combustibles on the Surface

Experimenting in Betula platyphylla and Larix gmelinii forests of Chongli District, Zhangjiakou City, traditional direct estimation methods and long short-term memory neural network models(LSTM) were used for single-step moisture content prediction. Combining restructured direct estimation methods, informer, and LSTM enabled predictions of moisture content sequences at different intervals. An analysis was conducted on the informer's accuracy in predicting moisture content sequences for 2 combustibles without relying on meteorological elements. Results revealed significant differences in the performance of 3 moisture content sequence prediction models for varying intervals. Direct estimation methods exhibited the highest prediction accuracy at shorter time intervals, while the informer model excelled at longer intervals, followed by the LSTM model. Utilizing the informer not only resolved high time complexity and memory consumption issues of the LSTM model but also enhanced the prediction accuracy of moisture content sequences over longer intervals. Traditional meteorological factor regressions and direct estimation methods for moisture content prediction rely on real-time values of current and historical meteorological elements. Using deep learning methods to address multi-variable and multi-step time series prediction achieved a 30-hour forecast of moisture content sequences. The B. platyphylla forest moisture content was predicted with an MAE of 0.2943, while L. gmelinii forest moisture content had an MAE of 0.1791, providing a theoretical basis for forest fire prediction.