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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2015 41st Latin American Computing Conference, CLEI 2015
EditorsAlex Cuadros-Vargas, Hector Cancela, Ernesto Cuadros-Vargas
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467391436
DOIs
StatePublished - 16 Dec 2015
Event41st Latin American Computing Conference, CLEI 2015 - Arequipa, Peru
Duration: 19 Oct 201523 Oct 2015

Publication series

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

Conference

Conference41st Latin American Computing Conference, CLEI 2015
Country/TerritoryPeru
CityArequipa
Period19/10/1523/10/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

  • agro-meteorological data
  • evaporation
  • flow
  • Motifs
  • Naive Bayes
  • neural networks linear regression
  • precipitation
  • prediction

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