西南林业大学学报2024，Vol.44Issue(9)：147-156,10.DOI:10.11929/j.swfu.202307023
地表细小死可燃物小时步长的含水率预测模型
Moisture Content Prediction Model for Hourly Steps of Small Dead Combustibles on the Surface
摘要
Abstract
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 meteorologic-al 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 in-former not only resolved high time complexity and memory consumption issues of the LSTM model but also en-hanced 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 cur-rent 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 mois-ture content was predicted with an MAE of0.294 3,while L.gmelinii forest moisture content had an MAE of 0.1791,providing a theoretical basis for forest fire prediction.关键词
地表细小死可燃物/含水率/长短期记忆神经网络/informerKey words
surface fine dead combustible moisture/moisture content/LSTM/informer分类
农业科技引用本文复制引用
张佐忠,高德民,王浩宇,牛海峰,郭在军..地表细小死可燃物小时步长的含水率预测模型[J].西南林业大学学报,2024,44(9):147-156,10.基金项目
科技冬奥专项崇礼区森林智慧防火项目(2020SLZHFH-2)资助. （2020SLZHFH-2）
