Artificial neural networks approach for forecasting of monthly relative humidity in Sivas, Turkey
| dc.contributor.author | Gürlek, Cahit | |
| dc.date.accessioned | 2024-03-08T07:52:56Z | |
| dc.date.available | 2024-03-08T07:52:56Z | |
| dc.date.issued | 2023 | tr |
| dc.department | Eğitim Bilimleri Enstitüsü | tr |
| dc.description | Relative humidity is a crucial parameter for various agricultural and engineering | en_US |
| dc.description.abstract | Relative humidity is a crucial parameter for various agricultural and engineering applications and atmospheric dynamics; hence its accurate and reliable estimation is essential. This study aims to predict monthly relative humidity by means of the artificial neural networks (ANNs) method using neighbouring data in Sivas Province, Turkey. Nineteen years (2000- 2018) monthly mean relative humidity data of five measurement stations was used for ANN analysis. The prediction accuracy of the ANN models was evaluated with the coefficient of determination (R2), mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean squared error (RMSE). Contour plot maps were also generated for visual comparison. R2, MAE, MAPE and RMSE values ranged between 0.952-0.965, 1.916-2.586, 3.422-4.974 and 2.472-3.391, respectively. The results showed that the ANN method provided satisfactory predictions for relative humidity. | tr |
| dc.identifier.scopus | 2-s2.0-85166585348 | en_US |
| dc.identifier.scopusquality | N/A | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12418/15031 | |
| dc.identifier.wos | WOS:001042056200002 | en_US |
| dc.identifier.wosquality | Q3 | |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | en | en_US |
| dc.relation.publicationcategory | Rapor | tr |
| dc.rights | info:eu-repo/semantics/closedAccess | tr |
| dc.title | Artificial neural networks approach for forecasting of monthly relative humidity in Sivas, Turkey | en_US |
| dc.type | Article | en_US |
