在物联网系统中用于天气预测的迷雾启用机器学习方法:一个案例研究
Buket İşler1, Şükrü Mustafa Kaya2, Fahreddin Raşit Kılıç3
1Department of Software Engineering, Istanbul Topkapi University, Istanbul 34087, Türkiye.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
这项研究使用物联网传感器和深度学习来增强温度预测,通过波形处理双向长期短期记忆 (W-BiLSTM) 模型实现97%的准确性. 该方法提供可靠的预测,即使在有限的基础设施.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 气象学 天气学
背景情况:
- 准确的温度预测对于公共安全,环境风险管理和节能至关重要.
- 在政府测量基础设施不足的地区,预测受到阻碍.
- 物联网传感器网络为数据稀缺地区的数据收集提供了解决方案.
研究的目的:
- 在数据有限的地区提高温度预测的准确性.
- 为大规模传感器数据确定最佳实时处理方法.
- 确保温度预测的可靠性.
主要方法:
- 使用物联网传感器网络收集温度,压力和湿度数据.
- 使用离散波段变换 (DWT) 预处理的数据用于特征提取和降噪.
- 采用并比较了三个深度学习模型:波形处理的人工神经网络 (W-ANN),波形处理长期短期记忆网络 (W-LSTM) 和波形处理双向长期短期记忆网络 (W-BiLSTM).
主要成果:
- 波形处理的双向长短期记忆 (W-BiLSTM) 模型以97%的测试准确度和2%的平均绝对百分比误差 (MAPE) 实现了最高的性能.
- 在预测准确度方面,W-BiLSTM显著超过了W-LSTM和W-ANN模型.
- 根据土耳其国家气象局 (TSMS) 数据验证的预报显示了94%的一致性,证实了模型的稳定性.
结论:
- 该W-BiLSTM模型可实现可靠的温度预测,克服政府测量基础设施不足的局限性.
- 这种方法支持数据驱动的环境风险管理和节能决策.
- 物联网传感器网络与先进的深度学习相结合,为关键环境监测提供了可扩展的解决方案.
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