基于变量模态分解的每月流量预测,与甲虫优化算法相结合,用于封闭的循环单位模型
Ban Wen-Chao1, Shen Liang-Duo2, Chen Liang1
1School of Marine Engineering Equipment of Zhejiang Ocean University, Zhoushan, 316022, China.
Environmental monitoring and assessment
|November 27, 2023
概括
一个新的VMD-DBO-GRU模型提高了每月排水预测的准确性. 这种方法结合了变量模态分解 (VMD),虫优化 (DBO) 和封闭循环单元 (GRU) 来实现更好的水资源管理.
科学领域:
- 水文学的水文学
- 水资源管理 水资源管理
- 环境科学中的人工智能
背景情况:
- 准确的每月排水预测对于有效的水资源管理和利用至关重要.
- 现有的预测模型在实现高预测准确性方面经常面临挑战.
- 人工智能和优化算法的进步为改善水文预测提供了潜力.
研究的目的:
- 开发和验证一种新的混合模型,用于改进每月流量预测.
- 整合变量模态分解 (VMD),虫优化算法 (DBO) 和封闭循环单元 (GRU) 以提高预测准确度.
- 在水资源管理中提供可靠的替代方案,用于每月排水预测.
主要方法:
- 使用变量模态分解 (VMD) 进行历史流失数据的分解.
- 通过 Dung Beetle Optimization (DBO) 算法优化 Gated Recurrent Unit (GRU) 模型参数.通过 Dung Beetle Optimization (DBO) 算法优化 Gated Recurrent Unit (GRU) 模型参数.
- 预测使用优化的GRU对分解的流水组件进行预测,然后对预测进行整合.
主要成果:
- 与基线模型 (BP,SVM,GRU,VMD-GRU,DBO-GRU,EMD-GRU) 相比,拟议的VMD-DBO-GRU模型显示出更高的预测准确度.
- 从安沙水库使用广泛的月流量数据 (1980-2020年) 进行验证,证实了该模型的有效性.
- 混合方法成功地捕获了月度流水数据中的复杂模式.
结论:
- VMD-DBO-GRU模型在每月排水预测准确度方面取得了显著的进步.
- 这种混合方法为水资源管理和规划提供了强大而准确的工具.
- 该研究强调了将先进的分解和优化技术与用于水文预测的深度学习相结合的潜力.
更多相关视频
12:26Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
Published on: October 11, 2016
13.3K
11:53The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
Published on: October 14, 2017
11.7K
相关概念视频
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
56
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
56
01:24Rapidly Varying Flow
64
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
64