Source :
2015 XLI Latin American Computing Conference (CLEI)
Descriptors :
Motifs , agro-meteorological data , flow , precipitation , evaporation , naive bayes , neural networks linear regression , and prediction
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.
Title of Article :
Time series analysis of agro-meteorological through algorithms scalable data mining case: Chili river watershed, Arequipa
Author/Authors :
Abarca Romero Melisa , Karla Fernández Fabián , Jose Herrera Quispe
Author/Authors - جزئيات :
