对热电驱动传感器进行有效的土壤温度概况估计.
Jiri Konecny1, Jaromir Konecny1, Kamil Bancik1
1Department of Cybernetics and Biomedical Engineering, VSB-Technical University of Ostrava, 17. Listopadu 2172/15, 708 00 Ostrava-Poruba, Czech Republic.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
这项研究预测了使用机器学习为物联网 (IoT) 传感器供电的土壤温度概况. 改进后的模型实现了更低的误差,并简化了输入,以实现高效的能源采集.
科学领域:
- 环境科学 环境科学
- 农业技术 农业技术
- 传感器网络 传感器网络
背景情况:
- 为环境和农业应用提供物联网 (IoT) 传感器的动力是一个重大挑战.
- 利用空气和土壤之间的温度差异为能源采集提供了一个有前途的解决方案.
- 准确的土壤温度概况数据对于有效的能源采集技术至关重要.
研究的目的:
- 开发和评估用于预测土壤温度概况的机器学习模型.
- 通过提高土壤温度预测准确性和效率,优化物联网传感器的能源采集.
- 为了简化土壤温度预测模型的输入参数.
主要方法:
- 利用了来自捷克共和国的气象和土壤温度资料数据.
- 训练有素的机器学习模型包括多项式回归 (PR),支持向量回归 (SVR) 和长短期记忆 (LSTM).
- 简化模型输入环境温度和太阳辐射.
主要成果:
- 对于土壤温度配置文件,预测误差为0.79°C.
- 与最先进的研究相比,温度误差减少了10.9%.
- 通过简化输入参数,显著降低了计算成本.
结论:
- 拟议的机器学习方法为预测土壤温度提供了更有效,更准确的方法.
- 这一进步促进了物联网传感器在环境和农业环境中的优化能源采集.
- 简化的输入参数提高了解决方案的实用性和成本效益.
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