Scholarlink

<p>Enhancing the precision of discharge coefficient (C<sub>d</sub>) prediction holds paramount importance for effective Water distribution control. Calculating the C<sub>d</sub> for radial gates is often complex, with existing methods frequently depending on intricate procedures and underlying assumptions. This study introduces a deep learning-based stacking ensemble model for C<sub>d</sub> prediction. The proposed model comprises a dual-layer structure. Four machine learning algorithms are exploited as baseline models. The Meta model employed long short-term memory (LSTM) with attention mechanism to amalgamate the outputs from the base models and assign sufficient weight to each base model. The spatial attention mechanism effectively highlighted relevant patterns within the data. The proposed model achieved an impressive root mean square error of 0.0175. The ensemble model outperformed existing longstanding models. The proposed system holds substantial strategic importance, enabling optimal water resource management.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A novel stacking ensemble model for predicting discharge coefficient of submerged multi parallel radial gates

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月3日

查看全文
  • Noran M. Abdelazim,
  • Mohamed Hosny,
  • Fahmy S. Abdelhaleem,
  • Ahmed M. Elshenhab,
  • Amir Ibrahim

摘要

Enhancing the precision of discharge coefficient (Cd) prediction holds paramount importance for effective Water distribution control. Calculating the Cd for radial gates is often complex, with existing methods frequently depending on intricate procedures and underlying assumptions. This study introduces a deep learning-based stacking ensemble model for Cd prediction. The proposed model comprises a dual-layer structure. Four machine learning algorithms are exploited as baseline models. The Meta model employed long short-term memory (LSTM) with attention mechanism to amalgamate the outputs from the base models and assign sufficient weight to each base model. The spatial attention mechanism effectively highlighted relevant patterns within the data. The proposed model achieved an impressive root mean square error of 0.0175. The ensemble model outperformed existing longstanding models. The proposed system holds substantial strategic importance, enabling optimal water resource management.