STL分解组合深度学习模型用于每日水库入水量预测,用于水电生产
Njogho Kenneth Tebong1,2, Théophile Simo2,3, Armand Nzeukou Takougang2
1Research Unit Condensed Matter, Electronics and Signal Processing, Department of Physics, Faculty of Sciences, University of Dschang, PO Box 67, Dschang, Cameroon.
Heliyon
|June 12, 2023
概括
精确的水库流入预测水资源管理是通过集体深度学习模型改进的. STL-Dense多变量模型在预测水库流入量方面表现出卓越的性能.
科学领域:
- 水文和水资源水文与水资源
- 环境科学中的人工智能
- 时间序列预测时间序列预测
背景情况:
- 准确的水库流入预测对于有效的水资源管理和运营规划至关重要.
- 传统的预测方法经常与水文系统的复杂,非线性动态作斗争.
- 深度学习模型为提高这些预测的准确性提供了有希望的替代方案.
研究的目的:
- 开发和评估集体深度学习模型,以进行增强的水库流入预测.
- 在预测水文数据的预处理中使用Loess (STL) 调查季节性趋势分解的有效性.
- 为了比较各种组合配置的性能,包括单变量和多变量方法.
主要方法:
- 使用Loess (STL) 应用季节性趋势分解来分解水库的流入量和降水数据.
- 使用密集,长期短期记忆 (LSTM) 和卷积神经网络 (Conv1D) 架构开发集合模型.
- 评估七个拟议的整体模型 (例如,STL-Dense,STL-LSTM多变量) 使用Lom Pangar水库 (2015-2020) 的每日数据.
主要成果:
- 在所有评估模型中,STL-Dense多变量模型实现了最佳性能.
- 最好的模型的关键性能指标包括MAE为14.636 m3/s,RMSE为20.841 m3/s,MAPE为6.622%,NSE为0.988.
- 虽然整体模型显示出前景,但一些个别深度学习模型 (Dense,Conv1D,LSTM) 的表现优于其STL分解的单变量对应模型.
结论:
- 集成深度学习模型,特别是多变量方法与STL分解相结合,显著提高了水库流入预测的准确性.
- 这些发现强调了整合多个数据输入和多种建模技术对于稳健的水资源管理的重要性.
- 该研究为优化水库运营和减轻与水资源稀缺或过剩相关的风险提供了宝贵的见解.
相关概念视频
01:18Uniform Depth Channel Flow: Problem Solving
95
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...
95
01:17Design Example: Analyzing Capacity Contours for Flood Risk Assessment
74
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
74
01:21Design Example: Creating a Hydraulic Model of a Dam Spillway
233
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.
233
01:27Uniform Depth Channel Flow
99
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...
99
01:24Rapidly Varying Flow
105
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...
105
01:30Typical Model Studies
385
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.
385