Evidence map›Paper›PMID 37128305›Full record

ArticleHeliyon2023

Artificial intelligent systems optimized by metaheuristic algorithms and teleconnection indices for rainfall modeling: The case of a humid region in the mediterranean basin.

Bilel Zerouali, Celso Augusto Guimarães Santos, Camilo Allyson Simões de Farias, Raul Souza Muniz, Salah Difi, Zaki Abda, Mohamed Chettih, Salim Heddam, Samy A Anwar, Ahmed Elbeltagi

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.9field-weighted citation impact, top 9% of its field
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

4 citing papers in PubMed, 24 citations in OpenAlex.

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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

10 authors at 7 institutions in 3 countries.

Bilel ZeroualiVegetal Chemistry-Water-Energy Laboratory, Faculty of Civil Engineering and Architecture, Hassiba Benbouali, Department of Hydraulic, University of Chlef, B.P. 78C, Ouled Fares, 02180, Chlef, Algeria.
Celso Augusto Guimarães SantosDepartment of Civil and Environmental Engineering, Federal University of Paraíba, 58051-900, João Pessoa, Brazil.
Camilo Allyson Simões de FariasAcademic Unit of Environmental Science and Technology, Federal University of Campina Grande, 58840-000, Pombal, Brazil.
Raul Souza MunizDepartment of Civil and Environmental Engineering, Federal University of Paraíba, 58051-900, João Pessoa, Brazil.
Salah DifiVegetal Chemistry-Water-Energy Laboratory, Faculty of Civil Engineering and Architecture, Hassiba Benbouali, Department of Hydraulic, University of Chlef, B.P. 78C, Ouled Fares, 02180, Chlef, Algeria.
Zaki AbdaResearch Laboratory of Water Resources, Soil and Environment, Department of Civil Engineering, Faculty of Civil Engineering and Architecture, Amar Telidji University, P. O. Box 37.G, Laghouat, 03000, Algeria.
Mohamed ChettihResearch Laboratory of Water Resources, Soil and Environment, Department of Civil Engineering, Faculty of Civil Engineering and Architecture, Amar Telidji University, P. O. Box 37.G, Laghouat, 03000, Algeria.
Salim HeddamAgronomy Department, Hydraulics Division, University 20 Août 1955, Route El Hadaik, BP 26, Skikda, 21024, Algeria.
Samy A AnwarEgyptian Meteorological Authority, Qobry EL-Kobba, P.O. Box 11784, Cairo, Egypt.
Ahmed ElbeltagiAgricultural Engineering Department, Mansoura University, Mansoura, 35516, Egypt.
Hassiba Benbouali University of Chlef · DZUniversidade Federal da Paraíba · BRUniversity of Laghouat · DZEgyptian Atomic Energy Authority · EGMansoura University · EGUniversidade Federal de Campina Grande · BRUniversity of Skikda · DZ

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Characterized by their high spatiotemporal variability, rainfalls are difficult to predict, especially under climate change. This study proposes a multilayer perceptron (MLP) network optimized by Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Firefly Algorithm (FFA), and Teleconnection Pattern Indices - such as North Atlantic Oscillation (NAO), Southern Oscillations (SOI), Western Mediterranean Oscillation (WeMO), and Mediterranean Oscillation (MO) - to model monthly rainfalls at the Sebaou River basin (Northern Algeria). Afterward, we compared the best-optimized MLP to the application of the Extreme Learning Machine optimized by the Bat algorithm (Bat-ELM). Assessment of the various input combinations revealed that the NAO index was the most influential parameter in improving the modeling accuracy. The results indicated that the MLP-FFA model was superior to MLP-GA and MLP-PSO for the testing phase, presenting

Indexed as

Bat algorithmFirefly algorithmMachine learningMultilayer perceptronPrecipitation

Identifiers

PMID37128305
PMCPMC10147990
OpenAlexW4362658785

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.