Artificial intelligent systems optimized by metaheuristic algorithms and teleconnection indices for rainfall modeling: The case of a humid region in the mediterranean basin
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 P...
Published in: | Heliyon |
---|---|
Main Authors: | , , , , , , , , , |
Format: | Text |
Language: | English |
Published: |
Elsevier
2023
|
Subjects: | |
Online Access: | http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10147990/ http://www.ncbi.nlm.nih.gov/pubmed/37128305 https://doi.org/10.1016/j.heliyon.2023.e15355 |
id |
ftpubmed:oai:pubmedcentral.nih.gov:10147990 |
---|---|
record_format |
openpolar |
spelling |
ftpubmed:oai:pubmedcentral.nih.gov:10147990 2023-06-11T04:14:33+02:00 Artificial intelligent systems optimized by metaheuristic algorithms and teleconnection indices for rainfall modeling: The case of a humid region in the mediterranean basin Zerouali, Bilel Santos, Celso Augusto Guimarães de Farias, Camilo Allyson Simões Muniz, Raul Souza Difi, Salah Abda, Zaki Chettih, Mohamed Heddam, Salim Anwar, Samy A. Elbeltagi, Ahmed 2023-04-06 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10147990/ http://www.ncbi.nlm.nih.gov/pubmed/37128305 https://doi.org/10.1016/j.heliyon.2023.e15355 en eng Elsevier http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10147990/ http://www.ncbi.nlm.nih.gov/pubmed/37128305 http://dx.doi.org/10.1016/j.heliyon.2023.e15355 © 2023 Published by Elsevier Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Heliyon Research Article Text 2023 ftpubmed https://doi.org/10.1016/j.heliyon.2023.e15355 2023-05-07T01:03:28Z 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 RMSE values equal to 33.36, 30.50, and 29.92 mm, respectively. The comparison between the best MLP model and Bat-ELM revealed the high performance of Bat-ELM for rainfall modeling at the Sebaou River basin, with RMSE reducing from 29.92 to 11.89 mm and NSE value from 0.902 to 0.985 during the testing phase. This study shows that incorporating the North Atlantic Oscillation (NAO) as a predictor improved the accuracy of artificial intelligence systems optimized by metaheuristic algorithms, specifically Bat-ELM, for rainfall modeling tasks such as filling in missing data of rainfall time series. Text North Atlantic North Atlantic oscillation PubMed Central (PMC) Soi ENVELOPE(30.704,30.704,66.481,66.481) Heliyon 9 4 e15355 |
institution |
Open Polar |
collection |
PubMed Central (PMC) |
op_collection_id |
ftpubmed |
language |
English |
topic |
Research Article |
spellingShingle |
Research Article Zerouali, Bilel Santos, Celso Augusto Guimarães de Farias, Camilo Allyson Simões Muniz, Raul Souza Difi, Salah Abda, Zaki Chettih, Mohamed Heddam, Salim Anwar, Samy A. Elbeltagi, Ahmed Artificial intelligent systems optimized by metaheuristic algorithms and teleconnection indices for rainfall modeling: The case of a humid region in the mediterranean basin |
topic_facet |
Research Article |
description |
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 RMSE values equal to 33.36, 30.50, and 29.92 mm, respectively. The comparison between the best MLP model and Bat-ELM revealed the high performance of Bat-ELM for rainfall modeling at the Sebaou River basin, with RMSE reducing from 29.92 to 11.89 mm and NSE value from 0.902 to 0.985 during the testing phase. This study shows that incorporating the North Atlantic Oscillation (NAO) as a predictor improved the accuracy of artificial intelligence systems optimized by metaheuristic algorithms, specifically Bat-ELM, for rainfall modeling tasks such as filling in missing data of rainfall time series. |
format |
Text |
author |
Zerouali, Bilel Santos, Celso Augusto Guimarães de Farias, Camilo Allyson Simões Muniz, Raul Souza Difi, Salah Abda, Zaki Chettih, Mohamed Heddam, Salim Anwar, Samy A. Elbeltagi, Ahmed |
author_facet |
Zerouali, Bilel Santos, Celso Augusto Guimarães de Farias, Camilo Allyson Simões Muniz, Raul Souza Difi, Salah Abda, Zaki Chettih, Mohamed Heddam, Salim Anwar, Samy A. Elbeltagi, Ahmed |
author_sort |
Zerouali, Bilel |
title |
Artificial intelligent systems optimized by metaheuristic algorithms and teleconnection indices for rainfall modeling: The case of a humid region in the mediterranean basin |
title_short |
Artificial intelligent systems optimized by metaheuristic algorithms and teleconnection indices for rainfall modeling: The case of a humid region in the mediterranean basin |
title_full |
Artificial intelligent systems optimized by metaheuristic algorithms and teleconnection indices for rainfall modeling: The case of a humid region in the mediterranean basin |
title_fullStr |
Artificial intelligent systems optimized by metaheuristic algorithms and teleconnection indices for rainfall modeling: The case of a humid region in the mediterranean basin |
title_full_unstemmed |
Artificial intelligent systems optimized by metaheuristic algorithms and teleconnection indices for rainfall modeling: The case of a humid region in the mediterranean basin |
title_sort |
artificial intelligent systems optimized by metaheuristic algorithms and teleconnection indices for rainfall modeling: the case of a humid region in the mediterranean basin |
publisher |
Elsevier |
publishDate |
2023 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10147990/ http://www.ncbi.nlm.nih.gov/pubmed/37128305 https://doi.org/10.1016/j.heliyon.2023.e15355 |
long_lat |
ENVELOPE(30.704,30.704,66.481,66.481) |
geographic |
Soi |
geographic_facet |
Soi |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
Heliyon |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10147990/ http://www.ncbi.nlm.nih.gov/pubmed/37128305 http://dx.doi.org/10.1016/j.heliyon.2023.e15355 |
op_rights |
© 2023 Published by Elsevier Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
op_doi |
https://doi.org/10.1016/j.heliyon.2023.e15355 |
container_title |
Heliyon |
container_volume |
9 |
container_issue |
4 |
container_start_page |
e15355 |
_version_ |
1768392622694465536 |