Evaluation of Evapotranspiration Classification Using Self Organizing Maps and Weather Research and Forecasting Variables

Maria Solis-Aulestia, Israel Pineda, Elisa J. Piispa, Scott L. Williams

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

2 Citas (Scopus)

Resumen

Remotely sensed data and Artificial Intelligence provide an avenue to Standard Evaporation (ETo) classification. Hence, we developed a methodology for classifying ETo using Self-Organizing Maps on data from the Weather Research and Forecasting (WRF) model obtained from March to October, 2020, at 3 km resolution over Ecuador. This is a preliminary effort to provide a visual comparison of ETo classified maps under different scenarios: a 'shallow' learning that takes only data from one time slice at 12:00 noon for one and three months; a 'deeper' learning that considers hourly slices for three months; raw and pre-processed WRF variables; and different numbers of classified classes. The results of these exercises show overall stable mountainous classification, while the Amazonia and Western regions appear more volatile. Moreover, the 'deeper' learning produces a more stable ETo classification in all regions with a decreased migration of classes over time, as presented at http://www.yachay.openfabtech.org/somETo/description.php.

Idioma originalInglés
Título de la publicación alojadaIGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas3195-3198
Número de páginas4
ISBN (versión digital)9781665427920
DOI
EstadoPublicada - 2022
Publicado de forma externa
Evento2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 - Kuala Lumpur, Malasia
Duración: 17 jul. 202222 jul. 2022

Serie de la publicación

NombreInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volumen2022-July

Conferencia

Conferencia2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
País/TerritorioMalasia
CiudadKuala Lumpur
Período17/07/2222/07/22

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