基于LSTM-AT-DP模型的多因素环境参数时间序列预测模型的研究
Longwei Liang1,2, Hui Shi2,3, Zhaoyuan Wang1
1College of Agriculture, Shihezi University, Shihezi, China.
Frontiers in plant science
|September 3, 2025
概括
使用具有注意力机制和数据预处理的长期短期内存网络的新设施环境预测模型提高了准确性并减少了错误. 这种先进的模型为农业设施的环境监管提供了更高的精度.
科学领域:
- 农业工程
- 环境监测
- 人工智能
背景情况:
- 现有的设施环境预测模型缺乏准确性和及时性,阻碍了农业环境中的精确环境监管.
- 挑战包括多因素非线性合和长期预测中的错误积累.
研究的目的:
- 开发一种新的设施环境预测模型,以克服现有方法的局限性.
- 提高农业设施环境预测的准确性和及时性.
主要方法:
- 提出了一个具有注意力 (LSTM-AT) 和数据预处理 (DP) 的长期内存网络.
- 数据预处理涉及波段值和滑窗技术.
- 一个动态加权的注意力机制功能,以改善时间建模.
主要成果:
- 在24小时的预测中获得了高确定系数 (R2):0.9602 (温度),0.9529 (湿度) 和0.9839 (辐射).
- 与基线LSTM模型相比,显著改善了湿度预测.
- 有效抑制长期预测中的错误积累.
结论:
- 该LSTM-AT-DP模型显著提高了设施环境的预测准确性和可靠性.
- 注意力机制对于识别和权衡关键的时间特征至关重要.
- 这为准确的农业设施环境法规提供了强大的技术支持.
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