dor_id: 4128794
506.#.#.a: Público
590.#.#.d: Los artículos enviados a la revista "Atmósfera", se juzgan por medio de un proceso de revisión por pares
510.0.#.a: Consejo Nacional de Ciencia y Tecnología (CONACyT); Sistema Regional de Información en Línea para Revistas Científicas de América Latina, el Caribe, España y Portugal (Latindex); Scientific Electronic Library Online (SciELO); SCOPUS, Web Of Science (WoS); SCImago Journal Rank (SJR)
561.#.#.u: https://www.atmosfera.unam.mx/
650.#.4.x: Físico Matemáticas y Ciencias de la Tierra
336.#.#.b: article
336.#.#.3: Artículo de Investigación
336.#.#.a: Artículo
351.#.#.6: https://www.revistascca.unam.mx/atm/index.php/atm/index
351.#.#.b: Atmósfera
351.#.#.a: Artículos
harvesting_group: RevistasUNAM
270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx
590.#.#.c: Open Journal Systems (OJS)
270.#.#.d: MX
270.1.#.d: México
590.#.#.b: Concentrador
883.#.#.u: https://revistas.unam.mx/catalogo/
883.#.#.a: Revistas UNAM
590.#.#.a: Coordinación de Difusión Cultural
883.#.#.1: https://www.publicaciones.unam.mx/
883.#.#.q: Dirección General de Publicaciones y Fomento Editorial
850.#.#.a: Universidad Nacional Autónoma de México
856.4.0.u: https://www.revistascca.unam.mx/atm/index.php/atm/article/view/53131/46889
100.1.#.a: Comert, Mehmet Murat; Adem, Kemal; Erdogan, Muberra
524.#.#.a: Comert, Mehmet Murat, et al. (2023). Comparative analysis of estimated solar radiation with different learning methods and empirical models. Atmósfera; Vol. 37, 2023; 273-284. Recuperado de https://repositorio.unam.mx/contenidos/4128794
245.1.0.a: Comparative analysis of estimated solar radiation with different learning methods and empirical models
502.#.#.c: Universidad Nacional Autónoma de México
561.1.#.a: Instituto de Ciencias de la Atmósfera y Cambio Climático, UNAM
264.#.0.c: 2023
264.#.1.c: 2023-03-07
653.#.#.a: Solar radiation; Empirical Equations; Machine learning; Deep learning; LSTM model
506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC 4.0 Internacional, https://creativecommons.org/licenses/by-nc/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico editora@atmosfera.unam.mx
884.#.#.k: https://www.revistascca.unam.mx/atm/index.php/atm/article/view/53131
001.#.#.#: 022.oai:ojs.pkp.sfu.ca:article/53131
041.#.7.h: eng
520.3.#.a: Solar radiation, which is used in hydrological and agricultural modeling, agricultural, solar energy systems, and climatological studies, is the most important element of the energy reaching the earth. The present study compared the performance of two empirical equations -Angstrom and Hargreaves-Samani equations- and three machine learning models -Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM)-. Various learning models were developed for the variables used in each empirical equation. In the present study, monthly data of six stations in Turkey, three stations receiving the most solar radiation and three stations receiving the lowest solar radiation, were used. In terms of the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and determination coefficient (R2) values of each model, the LSTM was the most successful model, followed by ANN and SVM. The MAE value was 2.65 with the Hargreaves-Samani equation and decreased to 0.987 with the LSTM model, while MAE was 1.24 in the Angstrom equation and decreased to 0.747 with the LSTM model. The study revealed that the deep learning model is more appropriate to use than the empirical equations, even in cases with limited data.
773.1.#.t: Atmósfera; Vol. 37 (2023); 273-284
773.1.#.o: https://www.revistascca.unam.mx/atm/index.php/atm/index
022.#.#.a: ISSN electrónico: 2395-8812; ISSN impreso: 0187-6236
310.#.#.a: Trimestral
300.#.#.a: Páginas: 273-284
264.#.1.b: Instituto de Ciencias de la Atmósfera y Cambio Climático, UNAM
doi: https://doi.org/10.20937/ATM.53131
harvesting_date: 2023-06-20 16:00:00.0
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