评估卷积神经网络使用残余块和全球平均积聚技术来预测沉积物度
Cheng-Chia Huang1, Che-Cheng Chang2, Chiao-Ming Chang3
1Department of Water Resources Engineering and Conservation, Feng Chia University, Taichung City, Taiwan.
Scientific reports
|October 9, 2025
概括
这项研究引入了一种新的卷积神经网络-沉积物度预测 (CNN-SCP) 模型,用于精确的水沉积物监测. 先进的CNN-SCP模型为实时应用提供了更高的精度和效率.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 水资源管理 水资源管理
背景情况:
- 由于环境因素和传感器的局限性,监测水资源中的沉积物度 (SC) 是复杂的.
- 需要精确,具有成本效益和用户友好的沉积物监测技术.
研究的目的:
- 使用卷积神经网络 (CNN) 开发一种先进的沉积物度预测模型.
- 通过结合剩余块 (RB) 和全球平均聚合层来提高CNN的性能.
主要方法:
- 该研究使用卷积神经网络 (CNN) 作为基础架构.
- 剩余块 (RB) 被集成,以解决消失梯度问题并改善特征传播.
- 采用全球平均的聚合层来减少参数,防止过拟合,并增强模型的概括性.
主要成果:
- 拟议的CNN-SCP模型与原始CNN和现有方法相比,表现优越.
- 关键指标显示显著改善:MAE (115.42对263.67),MAPE (6.38对14.67),RMSE (134.24对294.51) 和CC (0.97对0.90) 等.
- 包括5个剩余块特别增强了模型的稳定性和趋同性.
结论:
- 开发的CNN-SCP模型显示了实时沉积物监测的巨大潜力.
- 这些发现支持在水资源管理中部署CNN-SCP,以提供有效的预警系统.
相关概念视频
01:11Residuals and Least-Squares Property
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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
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A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
When the residual values are plotted against the variable x, it is called a residual...
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