基于SCV-CBA模型的江山港水质预测研究
1Water Conservancy Science and Engineering College, Taiyuan University of Technology, Taiyuan, 030000, China. xuyiting0570@163.com.
Scientific reports
|July 8, 2025
概括
这项研究引入了一种新的混合模型,用于准确预测水质,改善溶解氧气预测. SCV-CBA模型有效处理复杂的时间序列数据,为环境监测提供可靠的工具.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 水质预测对于环境管理至关重要,但因时间序列数据中的复杂时间振荡而复杂.
- 现有的方法往往难以捕捉水质数据集中的复杂模式.
研究的目的:
- 开发和验证一个混合模型,SSA优化CEEMDAN-VMD-CNN-BiLSTM-Attention (SCV-CBA),用于增强水质时间序列预测.
- 提高溶氧预测模型的准确性和稳定性.
主要方法:
- 使用CEEMDAN将时间序列数据分解为内在模式函数 (IMFs).
- 优化SSA的k-means集群和优化SSA的变量模式分解 (VMD) 用于特征提取.
- 整合卷积神经网络 (CNN),双向长期短期记忆 (BiLSTM) 和注意力机制,用于全面的数据分析.
主要成果:
- 该SCV-CBA模型在溶解氧预测方面表现出卓越的性能,平均平方误差 (MSE) 较低,根平均平方误差 (RMSE) 和平均绝对误差 (MAE) 较低,并且R平方 (R2) 值高,为0.97679.
- 废弃试验,概括试验和不同特征维度试验证实了该模型的稳定性和可行性.
结论:
- 拟议的SCV-CBA混合模型为水质预测提供了强大而准确的解决方案,特别是溶解氧.
- 该模型能够处理复杂的时间动态并提取详细特征,这使其成为环境监测和管理的宝贵工具.
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