Evidence map›Paper›PMID 39141038›Full record

ArticleWater science and technology : a journal of the International Association on Water Pollution Research2024

Fine-tuning inflow prediction models: integrating optimization algorithms and TRMM data for enhanced accuracy.

Enas Ali, Bilel Zerouali, Aqil Tariq, Okan Mert Katipoğlu, Nadjem Bailek, Celso Augusto Guimarães Santos, Sherif S M Ghoneim, Abu Reza Md Towfiqul Islam

Abstract read
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In one paragraph

Article in Water science and technology : a journal of the International Association on Water Pollution Research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Enas AliUniversity Centre for Research and Development, Chandigarh University, Mohali, Punjab, India.ORCID https://orcid.org/0000-0002-8943-2154
Bilel ZeroualiLaboratory of Architecture, Cities and Environment, Department of Hydraulic, Faculty of Civil Engineering and Architecture, Hassiba Benbouali University of Chlef, Chlef, Algeria.ORCID https://orcid.org/0000-0003-4735-9750
Aqil TariqDepartment of Wildlife, Fisheries and Aquaculture, College of Forest Resources, Mississippi State University, 775 Stone Boulevard, Mississippi State, MS, USA.
Okan Mert KatipoğluFaculty of Engineering and Architecture, Department of Civil Engineering, Erzincan Binali Yıldırım University, Erzincan, Turkey.ORCID https://orcid.org/0000-0001-6421-6087
Nadjem BailekLaboratory of Mathematics Modeling and Applications, Department of Mathematics and Computer Science, Faculty of Sciences and Technology, Ahmed Draia University of Adrar, Adrar, Algeria; MEU Research Unit, Middle East University, Amman, Jordan E-mail: bailek.nadjem@univ-adrar.edu.dz.ORCID https://orcid.org/0000-0001-9051-8548
Celso Augusto Guimarães SantosDepartment of Civil and Environmental Engineering, Federal University of Paraíba, João Pessoa, Paraíba, Brazil.ORCID https://orcid.org/0000-0001-7927-9718
Sherif S M GhoneimDepartment of Electrical Engineering, College of Engineering, Taif University, Taif, Saudi Arabia.ORCID https://orcid.org/0000-0002-9387-1950
Abu Reza Md Towfiqul IslamDepartment of Disaster Management, Begum Bekeya University, Rangpur, Bangladesh.ORCID https://orcid.org/0000-0001-5779-1382

Funding

Taif University TU-DSPP-2024-14
6 · The paper itself

Abstract

This research explores machine learning algorithms for reservoir inflow prediction, including long short-term memory (LSTM), random forest (RF), and metaheuristic-optimized models. The impact of feature engineering techniques such as discrete wavelet transform (DWT) and XGBoost feature selection is investigated. LSTM shows promise, with LSTM-XGBoost exhibiting strong generalization from 179.81 m

Indexed as

AlgorithmsMachine LearningModels, Theoreticaldata-driven frameworksdiscrete wavelet transforminflow predictionparameter optimizationparticle swarm optimizationreservoir management

Identifiers

PMID39141038

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.