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.
Elsevier - Harvard (with titles)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, 1-12. https://doi.org/10.4136/ambi-agua.2708
American Psychological Association 7th editionHeras, 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
Springer - Basic (author-date)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 16:1-12. https://doi.org/10.4136/ambi-agua.2708
Juristische Zitierweise (Stüber) (Deutsch)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.