Time series analysis of agro-meteorological through algorithms scalable data mining case: Chili river watershed, Arequipa

Abarca Romero Melisa, Karla Fernández Fabián, Jose Alfredo Herrera Quispe

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

The paper proposes a model for predicting climate change, using algorithms in mining techniques based on approximate data, applied to agro-meteorological data, by identifying groups search of motifs and time series forecasting. To achieve the goal you work with the water balance components: flow, precipitation and evaporation; also took into account the climatic variety seasons marked by humidity (December, January, February, March) and dry (other months) providing better to abstract sub-classification for temporary data processing three classification techniques: linear regression, Naive Bayes and neural networks, where the results of each algorithm are compared with other results. Then the mathematical method of linear regression predicting water balance components for a period of approximately 12 months on the data of dams Pane and Fraile Water Resources in River Basin Chili, Arequipa is performed.

Idioma originalInglés
Título de la publicación alojadaProceedings - 2015 41st Latin American Computing Conference, CLEI 2015
EditoresAlex Cuadros-Vargas, Hector Cancela, Ernesto Cuadros-Vargas
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781467391436
DOI
EstadoPublicada - 16 dic. 2015
Evento41st Latin American Computing Conference, CLEI 2015 - Arequipa, Perú
Duración: 19 oct. 201523 oct. 2015

Serie de la publicación

NombreProceedings - 2015 41st Latin American Computing Conference, CLEI 2015

Conferencia

Conferencia41st Latin American Computing Conference, CLEI 2015
País/TerritorioPerú
CiudadArequipa
Período19/10/1523/10/15

Nota bibliográfica

Publisher Copyright:
© 2015 IEEE.

Huella

Profundice en los temas de investigación de 'Time series analysis of agro-meteorological through algorithms scalable data mining case: Chili river watershed, Arequipa'. En conjunto forman una huella única.

Citar esto