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Updated: Jul 10, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

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Published on: July 3, 2020

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基于深度学习的LSTM方法用于预测年度花粉曲线:Olea和Urticaceae花粉类型作为案例研究

Antonio Picornell1, Sandro Hurtado2, María Luisa Antequera-Gómez2

  • 1Department of Botany and Plant Physiology, University of Malaga, Malaga 29071, Spain.

Computers in biology and medicine
|November 21, 2023
PubMed
概括

准确的空气中花粉预测对公共卫生至关重要. 这项研究发现,长短期记忆 (LSTM) 算法,特别是CNN-LSTM,与传统方法相比,显著改善了每月花粉积分预测.

关键词:
这是LSTM的LSTM.神经网络的神经网络的神经网络预测花粉的预测随机的森林随机的森林

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