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基于ECOA-BiTCN-BiLSTM混合模型的寒冷地区露水量预测
Yi Zhang1, Pengtao Liu1, Yingying Xu1
1College of Electrical and Computer Science, Jilin Jianzhu University, Changchun, China.
Scientific reports
|February 5, 2025
概括
这项研究引入了一种增强的混合模型,用于预测寒冷地区的露水,实现更高的准确性. 优化的模型在预测露水形成方面取得了显著的改进,为农业应用提供了宝贵的工具.
科学领域:
- 农业气象 农业气象学
- 环境科学中的人工智能
背景情况:
- 准确的露水预测对于寒冷地区的农业规划至关重要.
- 现有的预测模型经常与复杂的环境因素和超参数优化作斗争.
研究的目的:
- 开发和验证一种新的混合预测模型,用于寒冷地区的露水.
- 通过使用先进的元启发算法优化模型超参数来提高预测准确性.
主要方法:
- 双向时间卷积网络 (BiTCN) 和双向长期短期记忆 (BiLSTM) 神经网络的集成.
- 使用增强的鱼优化算法 (ECOA) 优化模型超参数,使用四种混合策略.
- 使用来自中国东北部城市农田的真实世界露水数据验证ECOA-BiTCN-BiLSTM模型.
主要成果:
- ECOA-BiTCN-BiLSTM模型实现了0.002424的平均绝对误差 (MAE),0.003984的根平均平方误差 (RMSE) 和0.123050的平均绝对百分比误差 (MAPE).
- 该模型显示了0.999840.2的高确定系数 (R2).
- 对比实验显示,拟议模型的性能优于BiTCN,BiLSTM和其他集群智能优化模型.
结论:
- 开发的ECOA-BiTCN-BiLSTM模型显著优于冷地区露水预测的现有方法.
- 增强的优化算法有效地减少了超参数选择的影响,导致更高的预测准确性.
- 这种混合型号为农业环境中准确的露水预测提供了非常有效的解决方案.
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