Time series analysis of agro-meteorological data through algorithms in a scalable data mining case: Chili river watershed, Arequipa
Melissa Abarca$^{1}$, Karla Mariel Fernández Fabián$^{1}$, Jose Herrera Quispe$^{1}$
$^{1}$Universidad Nacional de San Agustin. Arequipa Peru
email: melinm2@gmail.com, karla.m.f.f@gmail.com, jherreraq@gmail.com

Schedule:Mon 19th@18:15, Room: C

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.

BibTex

@InProceedings{CLEI-2015:144700,
	author 		= {Melissa Abarca and Karla Mariel Fernández Fabián and Jose Herrera Quispe},
	title 		= {Time series analysis of agro-meteorological data through algorithms in a scalable data mining case: Chili river watershed, Arequipa},
	booktitle 	= {2015 XLI Latin American Computing Conference (CLEI)},
	pages 		= {440--447},
	year 		= {2015},
	editor 		= {Hector Cancela and Alex Cuadros-Vargas and Ernesto Cuadros-Vargas},
	address 	= {Arequipa-Peru},
	month 		= {October},
	organization 	= {CLEI},
	publisher 	= {CLEI},
	url 		= {http://clei.org/clei2015/144700},
	isbn 		= {978-1-4673-9143-6},
	}