使用神经网络自动回归异源 (NNARX) 模型量化河岸侵蚀的非线性多独立变量.
Azlinda Saadon1, Jazuri Abdullah1, Ihsan Mohd Yassin2
1School of Civil Engineering, College of Engineering, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia.
Heliyon
|February 26, 2024
概括
本研究引入了一个神经网络自回归原生 (NNARX) 模型,用于准确的河岸侵蚀预测. 该模型有效地捕捉了由流量变化影响的复杂侵蚀模式,为河流管理提供了更好的洞察力.
科学领域:
- 水文和水资源工程 水文和水资源工程
- 计算流体动力学的流体动力学.
- 机器学习应用 机器学习应用
背景情况:
- 河岸侵蚀在水资源管理和基础设施稳定方面带来了重大挑战.
- 传统的方法往往难以准确预测河岸侵蚀的非线性动态.
- 了解侵蚀速度对于缓解土地退化和管理河流走廊至关重要.
研究的目的:
- 为预测河岸侵蚀率提出和验证一种新型神经网络自回归原生 (NNARX) 模型.
- 准确估计在不同流量条件下复杂的河岸侵蚀行为.
- 与传统方法相比,提供更准确的预测工具.
主要方法:
- 开发和应用一个神经网络自回归异源 (NNARX) 模型.
- 使用来自Sg.Sg.Sg.Sg.Sg.Sg.Sg.Sg.Sg.Sg.Sg.的203个培训和135个测试数据点的数据集. 马来西亚的伯纳姆.
- 使用重复变量作为模型输入的方法建立非维度参数.
- 在模型准确性评估中使用一步前进时间序列预测.
主要成果:
- 型号号没有. 6,具有5个独立变量和10个隐藏层,表现出强大的预测性能.
- 实现了高准确性,差异比率为94% (培训) 和90% (测试).
- 型号号没有. 6的R平方值为0.932 (训练) 和0.788 (测试),表明模型很适合.
- 确定了最大侵蚀的最佳近岸速度 (0.2-0.5米/秒) (1.5-1.8米/年) 和较低侵蚀速度 (0.1-0.4米/年) 的较高速度 (0.8-1.3米/秒).
- 灵敏度分析强调了剪切速度与近岸速度的比率是最有影响力的因素 (91%的准确性).
结论:
- 开发的NNARX模型准确地预测了受流量变化影响的河岸侵蚀速率的非线性行为.
- 这些发现为先进的道迁移和土地退化模拟提供了宝贵的见解.
- 该研究为有效的河岸保护和管理策略提供了一个强大的工具.
相关概念视频
Multiple Regression
3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
Rapidly Varying Flow
62
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...
62
Multi-input and Multi-variable systems
106
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
In the absence...
106
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
54
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...
54
Neural Regulation
39.4K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
39.4K
Typical Model Studies
359
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.
359


