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基于ISGA-AMSCNN-DD的温室气温预测模型
Yuqiang Yang1, Kun Song1, Huanzhi Luo1
1Research Center of Guangdong Smart Oceans Sensor Networks and Equipment Engineering, Guangdong Ocean University, Zhanjiang, People's Republic of China.
Environmental technology
|August 11, 2025
概括
准确的温室温度预测对于先进的控制至关重要. 一个新的混合模型ISGA-AMSCNN-DD通过集成优化算法和深度学习来改善预测,以实现可靠的环境管理.
科学领域:
- 农业工程 农业工程
- 人工智能的人工智能
- 环境科学 环境科学
背景情况:
- 准确的短期温室气温预测对于先进的控制策略,如模型预测控制,至关重要.
- 天气,作物透气和调节装置之间的非线性相互作用挑战了预测的准确性.
研究的目的:
- 开发一种新的混合预测模型,ISGA-AMSCNN-DD,用于增强温室气温预测.
- 为了应对温室中复杂的环境动态所带来的挑战.
主要方法:
- 集成改进的雪算法 (ISGA) 进行参数优化.
- 使用注意力增强的多尺度卷积神经网络 (AMSCNN) 进行特征提取.
- 采用树突网络架构 (DD) 来改进模型通用化.
主要成果:
- ISGA-AMSCNN-DD模型在真实世界温室气体数据中表现出卓越的性能.
- 实现了高精度,R2为0.9796,PBIAS为0.1218%,NSE为0.9849,RMSE为0.6232°C,MAPE为2.8623%. 这是一个很好的方法.
结论:
- 拟议的ISGA-AMSCNN-DD模型对于短期温室气温预测是准确可靠的.
- 该模型为智能温室管理和精确的温度控制提供了坚实的基础.
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