通过优化长期和短期记忆算法预测菌温室中的CO2度
Haoyi Yao1,2, Yunfeng Wang3,4, Xun Ma1,2
1School of Energy and Environmental Science, Yunnan Normal University, Kunming, 650500, China.
使用变量模式分解与搜索算法 (VMD-SSA-LSTM) 和土虫优化 (VMD-DBO-LSTM) 的优化模型显著提高了温室中二氧化碳预测的准确性. 在VMD-DBO-LSTM模型提供更快的计算,同时保持高精度.
科学领域:
- 农业工程 农业工程
- 环境科学 环境科学
- 人工智能的人工智能
背景情况:
- 准确预测二氧化碳 (CO2) 水平对于优化种植环境至关重要.
- 现有的预测模型可能缺乏动态温室条件所需的准确性和效率.
研究的目的:
- 开发和评估两种新的,优化的温室气体二氧化碳度预测模型:VMD-SSA-LSTM和VMD-DBO-LSTM.
- 与LSTM,EMD-LSTM和VMD-LSTM等传统模型相比,提高预测的准确性和有效性.
主要方法:
- 来自温室的时间序列CO2数据使用变量模式分解 (VMD) 进行了分解.
- 使用Sparrow Search Algorithm (SSA) 和Dung Beetle Optimization (DBO) 来优化长短期内存 (LSTM) 的网络参数.
- 优化的LSTM模型 (VMD-SSA-LSTM和VMD-DBO-LSTM) 用于多变量时间序列预测二氧化碳度.
主要成果:
- 两种VMD-SSA-LSTM和VMD-DBO-LSTM模型都显示出比基线LSTM,EMD-LSTM和VMD-LSTM模型更高的预测准确度.
- 与VDD-SSA-LSTM相比,VDD-DBO-LSTM模型的计算速度更快.
- 定量结果显示,平均绝对误差低 (例如,VMD-SSA-LSTM在阳光明的日子里为2.3488 ppm),R2值高 (例如,VMD-DBO-LSTM在阴天是0.9942).
结论:
- 拟议的VMD-SSA-LSTM和VMD-DBO-LSTM模型对于准确预测温室中的二氧化碳度是有效的.
- 优化算法 (SSA和DBO) 显著提高了LSTM对环境监测的预测能力.
- VMD-DBO-LSTM为实时温室环境控制提供了一个有希望的,计算效率高的解决方案.
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