风速和风向对作物产量预测的影响使用动态时间曲线和集成学习模型
Bright Bediako-Kyeremeh1, TingHuai Ma2,3, Huan Rong4
1School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China.
PeerJ
|June 17, 2024
概括
这项研究引入了一种新的深度学习模型,将长短期记忆 (LSTM) 和随机森林 (RF) 与动态时间扭曲 (DTW) 结合起来,以预测果仁作物产量. 改进的模型,包括风速和风向,大大提高了预测准确度,突出了这些环境因素的重要性.
科学领域:
- 农业科学 农业科学
- 机器学习 机器学习
- 环境科学 环境科学
背景情况:
- 果仁种植在经济上具有重要意义,全球需求很高.
- 风速和风向对果产量预测的影响研究不足.
- 现有的深度学习模型往往忽略了关键的时空环境数据.
研究的目的:
- 开发和评估一种先进的深度学习模型,用于预测果作物的产量.
- 研究风速和风向对产量预测准确性的影响.
- 增强使用动态时间扭曲 (DTW) 进行时空数据相似性评估的整体模型.
主要方法:
- 提出了一个混合模型,结合了长短期记忆 (LSTM) 和随机森林 (RF).
- 集成的动态时间扭曲 (DTW) 分析风速和风向数据的时空相似性.
- 在加纳博诺地区使用R平方,增强Dickey-Fuller (ADF) 测试,根平均平方误差 (RMSE) 和平均偏差误差 (MBE) 评估模型性能.
主要成果:
- 与没有风数据 (0.74) 的LSTM-DTW-RF模型相比,结合风数据的LSTM-DTW-RF模型获得了更高的R平方得分 (0.847).
- 通过ADF测试,LSTM-DTW-RF模型在静止性评估中表现出更高的信心 (90%).
- 贾曼南方显示出最高的模型评估分数 (RMSE:0.883,R-squared:0.835,MBE:0.212),而贾曼北方在风速相似性方面表现出色,温家乐在风向相似性方面表现出色.
结论:
- 结合风速和风向可以显著提高果产量预测的准确性.
- 动态时间扭曲 (DTW) 是有效的分析环境因素作物产量预测,由于其不变的性质.
- 这些发现支持可持续农业和垂直农业实践的知情决策.
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