TY - GEN
T1 - Time series analysis of agro-meteorological through algorithms scalable data mining case
T2 - 41st Latin American Computing Conference, CLEI 2015
AU - Melisa, Abarca Romero
AU - Fabián, Karla Fernández
AU - Quispe, Jose Herrera
N1 - Funding Information:
This research was supported in part by a research grant from Southern Illinois University at Edwardsville. I thank graduate students, HyungJun Cho, Ming Zhou, and Murali Neralla, for their assistance in coding the analytical method and simulation and running the experiments. I also thank the department editor and anonymous referees for their useful comments.
Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/16
Y1 - 2015/12/16
N2 - 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.
AB - 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.
KW - agro-meteorological data
KW - evaporation
KW - flow
KW - Motifs
KW - Naive Bayes
KW - neural networks linear regression
KW - precipitation
KW - prediction
UR - http://www.scopus.com/inward/record.url?scp=84961912388&partnerID=8YFLogxK
U2 - 10.1109/CLEI.2015.7359466
DO - 10.1109/CLEI.2015.7359466
M3 - Articulo (Contribución a conferencia)
AN - SCOPUS:84961912388
T3 - Proceedings - 2015 41st Latin American Computing Conference, CLEI 2015
BT - Proceedings - 2015 41st Latin American Computing Conference, CLEI 2015
A2 - Cuadros-Vargas, Alex
A2 - Cancela, Hector
A2 - Cuadros-Vargas, Ernesto
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 19 October 2015 through 23 October 2015
ER -
