Szerző dc.contributor.author | Ehteram Mohammad | hu_HU |
Szerző dc.contributor.author | Panahi Fatemeh | hu_HU |
Szerző dc.contributor.author | Najah Ahmed Ali | hu_HU |
Szerző dc.contributor.author | Mosavi Amir | hu_HU |
Szerző dc.contributor.author | El-Shafie Ahmed | hu_HU |
Elérhetőség dátuma dc.date.accessioned | 2022-04-11T10:31:18Z | |
Rendelkezésre állás dátuma dc.date.available | 2022-04-11T10:31:18Z | |
Kiadás dc.date.issued | 2022 | hu_HU |
Issn dc.identifier.issn | 2296-665X | hu_HU |
Uri dc.identifier.uri | http://hdl.handle.net/20.500.12944/17577 | |
Kivonat dc.description.abstract | Predicting evaporation is essential for managing water resources in basins. Improvement of the prediction accuracy is essential to identify adequate inputs on evaporation. In this study, artificial neural network (ANN) is coupled with several evolutionary algorithms, i.e., capuchin search algorithm (CSA), firefly algorithm (FFA), sine cosine algorithm (SCA), and genetic algorithm (GA) for robust training to predict daily evaporation of seven synoptic stations with different climates. The inclusive multiple model (IMM) is then used to predict evaporation based on established hybrid ANN models. The adjusting model parameters of the current study is a major challenge. Also, another challenge is the selection of the best inputs to the models. The IMM model had significantly improved the root mean square error (RMSE) and Nash Sutcliffe efficiency (NSE) values of all the proposed models. The results for all stations indicated that the IMM model and ANN-CSA could outperform other models. The RMSE of the IMM was 18, 21, 22, 30, and 43% lower than those of the ANN-CSA, ANN-SCA, ANN-FFA, ANN-GA, and ANN models in the Sharekord station. The MAE of the IMM was 0.112 mm/day, while it was 0.189 mm/day, 0.267 mm/day, 0.267 mm/day, 0.389 mm/day, 0.456 mm/day, and 0.512 mm/day for the ANN-CSA, ANN-SCA, and ANN-FFA, ANN-GA, and ANN models, respectively, in the Tehran station. The current study proved that the inclusive multiple models based on improved ANN models considering the fuzzy reasoning had the high ability to predict evaporation. Copyright | hu_HU |
Nyelv dc.language | en | hu_HU |
Kiadó dc.publisher | Frontiers Media S.A. | hu_HU |
Kulcsszó dc.subject | artificial intelligence | hu_HU |
Kulcsszó dc.subject | artificial neural network | hu_HU |
Kulcsszó dc.subject | capuchin search algorithm | hu_HU |
Kulcsszó dc.subject | evaporation | hu_HU |
Kulcsszó dc.subject | inclusive multiple models | hu_HU |
Kulcsszó dc.subject | machine learning | hu_HU |
Cím dc.title | Inclusive Multiple Model Using Hybrid Artificial Neural Networks for Predicting Evaporation | hu_HU |
Típus dc.type | folyóiratcikk | hu_HU |
Változat dc.description.version | published | hu_HU |
Hozzáférés dc.rights.accessRights | nyílt hozzáférésű | hu_HU |
Doi azonosító dc.identifier.doi | 10.3389/fenvs.2021.789995 | hu_HU |
Tudományág dc.subject.discipline | Műszaki tudományok | hu_HU |
Tudományterület dc.subject.sciencebranch | Műszaki tudományok/Informatikai tudományok | hu_HU |
Mtmt azonosító dc.identifier.mtmt | 32546445 | hu_HU |
Folyóirat dc.identifier.journalTitle | Frontiers in Environmental Science | hu_HU |
Évfolyam dc.identifier.journalVolume | 9 | hu_HU |
Wos azonosító dc.identifier.wos | 000747950100001 | hu_HU |
Scopus azonosító dc.identifier.scopus | 2-s2.0-85123444502 | hu_HU |
Szerző intézménye dc.contributor.department | Információs Társadalom Kutatóintézet | hu_HU |
Kar dc.contributor.faculty | Eötvös József Kutatóközpont | hu_HU |