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<p>Accurate modeling of filter head losses is essential for the optimal design and management of microirrigation systems, particularly when treating reclaimed water. In this study, Gene Expression Programming (GEP) was applied—for the first time in the literature—to develop predictive models for head losses in disc, screen, and sand filters operating with effluents. A k-fold cross-validation procedure was adopted to rigorously evaluate model performance under both direct learning (DL) and transfer learning (TL) scenarios. GEP models achieved Scatter Index (SI) values ranging from 0.049 to 0.071 for direct learning, consistently outperforming existing empirical equations when a fair comparison using independent test sets was conducted. Under transfer learning, screen filter-derived models demonstrated superior transferability to both sand and disc filters (SI 0.058–0.101), while combining disc and sand filter data improved predictions for screen filters (SI 0.064–0.069). These results provide a novel, data-driven framework for filter head loss estimation that can support microirrigation system design when filter-specific training data are unavailable.</p>

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Estimating filter head losses caused by reclaimed water based on machine learning approaches

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月21日

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  • Mohammad Hossein Kazemi,
  • Sepideh Karimi,
  • Miquel Duran-Ros,
  • Gerard Arbat,
  • Francisco Ramírez de Cartagena,
  • Jaume Puig-Bargués,
  • Pau Marti,
  • Jalal Shiri

摘要

Accurate modeling of filter head losses is essential for the optimal design and management of microirrigation systems, particularly when treating reclaimed water. In this study, Gene Expression Programming (GEP) was applied—for the first time in the literature—to develop predictive models for head losses in disc, screen, and sand filters operating with effluents. A k-fold cross-validation procedure was adopted to rigorously evaluate model performance under both direct learning (DL) and transfer learning (TL) scenarios. GEP models achieved Scatter Index (SI) values ranging from 0.049 to 0.071 for direct learning, consistently outperforming existing empirical equations when a fair comparison using independent test sets was conducted. Under transfer learning, screen filter-derived models demonstrated superior transferability to both sand and disc filters (SI 0.058–0.101), while combining disc and sand filter data improved predictions for screen filters (SI 0.064–0.069). These results provide a novel, data-driven framework for filter head loss estimation that can support microirrigation system design when filter-specific training data are unavailable.