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Hyperparameter-optimized deep learning for next-day pH forecasting in distributed water quality monitoring systems.

Overview

Authors: El-Sayed Kenawy1, Shady Y. El-mashad2,3, Ebrahim A. Mattar4, Amal H. Alharbi5, Marwa Radwan1
  1. Faculty of Artificial Intelligence, Delta University for Science and Technology,Mansoura, 11152 Egypt
  2. Department of Computer Systems Engineering, Faculty of Engineering at Shoubra, Benha University,Cairo, Egypt
  3. Faculty of Computer Science, Benha National University,Al-Obour, Egypt
  4. College of Engineering, University of Bahrain,Zallaq, Bahrain
  5. Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Institutions: Delta University for Science and Technology (Egypt); Benha University (Egypt); Benha National University (Egypt); University of Bahrain (Bahrain); Princess Nourah bint Abdulrahman University (Saudi Arabia)
Journal: Scientific reports, volume 16, issue 1, article 23727
Dates: received 14 April 2026; accepted 15 July 2026; published online 1 August 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-62977-3 · PMID 42542450 · PMCID PMC13428741 · OpenAlex W7172098997
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: methods / tools (subfield)
Methods: Machine learning, Statistics, Connectivity, Physiology & signal measures
Keywords: Multi-site water quality forecasting, Next-day pH prediction, Hyperparameter optimization, Deep learning, Environmental monitoring, Computational biology and bioinformatics, Engineering, Mathematics and computing
Topic: Hydrological Forecasting Using AI (Environmental Engineering, Environmental Science), according to OpenAlex
Citations: not cited yet (Europe PMC); 43 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Code

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Data

Datasets cited

Data availability statement

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Read it in the paper: doi.org/10.1038/s41598-026-62977-3.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 8 keywords, 35 references.

Cite

This paper

Kenawy, E.-S., El-mashad, S. Y., Mattar, E. A., Alharbi, A. H., & Radwan, M. (2026). Hyperparameter-optimized deep learning for next-day pH forecasting in distributed water quality monitoring systems. Scientific reports, 16(1), 23727. https://doi.org/10.1038/s41598-026-62977-3

BibTeX

@article{kenawy2026hyperparameter,
author = {Kenawy, El-Sayed and El-mashad, Shady Y. and Mattar, Ebrahim A. and Alharbi, Amal H. and Radwan, Marwa},
title = {{Hyperparameter-optimized deep learning for next-day pH forecasting in distributed water quality monitoring systems}},
journal = {Scientific reports},
year = {2026},
month = aug,
volume = {16},
number = {1},
pages = {23727},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-62977-3},
url = {https://doi.org/10.1038/s41598-026-62977-3},
pmid = {42542450},
pmcid = {PMC13428741}
}

RIS

TY - JOUR
AU - Kenawy, El-Sayed
AU - El-mashad, Shady Y.
AU - Mattar, Ebrahim A.
AU - Alharbi, Amal H.
AU - Radwan, Marwa
TI - Hyperparameter-optimized deep learning for next-day pH forecasting in distributed water quality monitoring systems
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/08/01
VL - 16
IS - 1
SP - 23727
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-62977-3
UR - https://doi.org/10.1038/s41598-026-62977-3
LA - en
ER -

CSL-JSON

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"language": "en",
"issued": {
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