American Psychological Association 6th edition

Heras, D., & Matovelle, C. (2021). Machine-learning methods for hydrological imputation data: analysis of the goodness of fit of the model in hydrographic systems of the Pacific -Ecuador. Revista Ambiente E Água, 16(3), 1-12. https://doi.org/10.4136/ambi-agua.2708

ISO-690 (author-date, English)

HERAS, Diego and MATOVELLE, Carlos, 2021. Machine-learning methods for hydrological imputation data: analysis of the goodness of fit of the model in hydrographic systems of the Pacific -Ecuador. Revista Ambiente e Água. 1 May 2021. Vol. 16, no. 3, p. 1-12. DOI 10.4136/ambi-agua.2708.

Modern Language Association 9th edition

Heras, D., and C. Matovelle. “Machine-Learning Methods for Hydrological Imputation Data: Analysis of the Goodness of Fit of the Model in Hydrographic Systems of the Pacific -Ecuador.”. Revista Ambiente E Água, vol. 16, no. 3, May 2021, pp. 1-12, https://doi.org/10.4136/ambi-agua.2708.

Mohr Siebeck - Recht (Deutsch - Österreich)

Heras, Diego/Matovelle, Carlos: Machine-learning methods for hydrological imputation data: analysis of the goodness of fit of the model in hydrographic systems of the Pacific -Ecuador., Revista Ambiente e Água 2021, 1-12.

Emerald - Harvard

Heras, D. and Matovelle, C. (2021), “Machine-learning methods for hydrological imputation data: analysis of the goodness of fit of the model in hydrographic systems of the Pacific -Ecuador.”, Revista Ambiente E Água, Vol. 16 No. 3, pp. 1-12.

Warning: These citations may not always be 100% accurate.