Developing a novel hybrid model based on GRU deep neural network and Whale optimization algorithm for precise forecasting of river's streamflow.

Amin Gharehbaghi, Redvan Ghasemlounia, Farshad Ahmadi, Rasoul Mirabbasi, Ali Torabi Haghighi

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

House price prediction using a hybrid GRU-MLP based on binary whale optimization algorithm and ant colony optimization for hyperparameter tuning.Scientific reports · 2026
Article

4 · The record

Corrections and comments

Erratum issued
Correction: Developing a novel hybrid model based on GRU deep neural network and Whale optimization algorithm for precise forecasting of river's streamflow.2025

5 · Who and what money

Authors and funding

5 authors.

Amin GharehbaghiDepartment of Civil Engineering, Faculty of Engineering, Hasan Kalyoncu University, Şahinbey, Gaziantep, 27110, Turkey.ORCID http://orcid.org/0000-0002-2898-3681

Redvan GhasemlouniaDepartment of Civil Engineering, Faculty of Engineering, Istanbul Gedik University, Istanbul, 34876, Turkey.ORCID http://orcid.org/0000-0003-1796-4562

Farshad AhmadiDepartment of Hydrology & Water Resources Engineering, Shahid Chamran University of Ahvaz, Ahvaz, 6135783151, Iran.ORCID http://orcid.org/0000-0001-7387-0224

Rasoul MirabbasiDepartment of Water Engineering, Shahrekord University, Shahrekord, Iran.ORCID http://orcid.org/0000-0002-9897-0042

Ali Torabi HaghighiWater, Energy, and Environmental Engineering Research Unit, University of Oulu, Oulu, Finland. ali.torabihaghighi@oulu.fi.ORCID http://orcid.org/0000-0002-5157-0156

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Streamflow contemplates a fundamental criterion to evaluate the impact of human activities and climate changes on the hydrological cycle. In this study, a novel innovative deep neural network (DNN) structure by integrating a double Gated Recurrent Units (GRU) neural network model with a multiplication layer and meta-heuristic whale optimization algorithm (WOA) (i.e., hybrid 2GRU×-WOA model) is developed to improve the prediction accuracy and performance of mean monthly Chehel-Chai River's streamflow (CCRSF

Indexed as

Chehel-Chai river’s streamflowGRU and Bi-GRU modelsMeta-heuristic Whale optimization algorithmNovel hybrid 2GRU×–WOA modelTLP parameter

Identifiers

PMID40461540

DOI10.1038/s41598-025-03185-3

PMCPMC12134273

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.