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Updated: Jun 28, 2025

Image-based Lagrangian Particle Tracking in Bed-load Experiments
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Image-based Lagrangian Particle Tracking in Bed-load Experiments

Published on: July 20, 2017

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使用可解释的深度学习模型来预测河流悬浮沉积物负载

Zeinab Mohammadi-Raigani1, Hamid Gholami2, Aliakbar Mohamadifar1

  • 1Department of Natural Resources Engineering, University of Hormozgan, Bandar‑Abbas, Hormozgan, Iran.

Environmental science and pollution research international
|April 24, 2024
PubMed
概括

这项研究开发了可解释的深度学习模型,以预测河流悬浮沉积物负载 (SSL). 密集的深度神经网络 (DDNN) 显示出卓越的性能,河流排放被确定为影响SSL预测的关键因素.

关键词:
深度学习模型深度学习模型地理信息系统 (GIS) 是一个悬浮沉积物负载的悬浮沉积物负载塔莱根河流域的流域是塔莱根河流域.

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