开发一种基于GRU深度神经网络和鱼优化算法的新型混合模型,用于精确预测河流的流量.
Amin Gharehbaghi1, Redvan Ghasemlounia2, Farshad Ahmadi3
1Department of Civil Engineering, Faculty of Engineering, Hasan Kalyoncu University, Şahinbey, Gaziantep, 27110, Turkey.
Scientific reports
|June 3, 2025
概括
一种新的混合深度神经网络 (DNN) 模型,2GRU×-WOA,通过优化输入变量和模型参数,显著改善了每月流量预测. 这种先进的模型提高了水文循环评估的准确性.
科学领域:
- 水文学的水文学
- 人工智能的人工智能
- 环境科学 环境科学
背景情况:
- 流量是评估人类和气候对水文循环影响的关键指标.
- 精确的流量预测对于水资源管理和洪水控制至关重要.
研究的目的:
- 开发一个创新的深度神经网络 (DNN) 结构,用于增强平均月度流量预测.
- 为了提高准确性,将双门循环单位 (GRU) 模型与鱼优化算法 (WOA) 集成.
主要方法:
- 开发了一个混合的2GRU×-WOA模型,结合了乘法层和元启发式优化.
- 使用皮尔森相关系数 (PCC) 和共弦幅度灵敏度 (CAS) 的特征选择确定了降水 (Pm) 作为关键输入.
- 该模型以特定的参数进行了优化:tanh-softsign激活,0.5断课率和70个隐藏的神经元.
主要成果:
- 混合型2GRU×-WOA模型实现了卓越的性能,其R2=0.79,NSE=0.76,MAE=0.21 (m3/s),MBE=-0.11 (m3/s) 和RMSE=0.36 (m3/s).
- 与基准GRU和Bi-GRU模型相比,混合模型显示R2增加了6.8%,RMSE减少了20.4%.
- 单个GRU和Bi-GRU模型的性能指标较低 (例如,R2分别为0.59和0.66).
结论:
- 拟议的混合型2GRU×-WOA模型在流量预测准确度方面取得了重大进展.
- 这种方法为水文预测提供了一个强大的工具,有助于更好地管理水资源.
- 该研究强调了将深度学习与元启发式算法集成为复杂环境建模的有效性.
相关概念视频
01:30Typical Model Studies
448
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
448
01:21Design Example: Creating a Hydraulic Model of a Dam Spillway
338
Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.
338
01:24Rapidly Varying Flow
146
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...
146
01:29Gradually Varying Flow
130
Gradually varying flow (GVF) in open channels describes situations where water depth changes slowly along the channel due to factors like non-uniform bed slope, channel shape variations, or obstructions. This flow type occurs when the depth adjusts gradually to balance gravitational forces, shear forces, and energy requirements, resulting in a low rate of depth change.Characteristics of Gradually Varying FlowGVF is commonly observed in natural streams, rivers, and canals, where flow depth...
130
01:18Uniform Depth Channel Flow: Problem Solving
131
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
131
01:27Uniform Depth Channel Flow
175
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
175