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

Abarca Romero Melisa; Karla Fernández Fabián; Jose Herrera Quispe

doi:10.1109/CLEI.2015.7359466

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

Abarca Romero Melisa, Karla Fernández Fabián, Jose Herrera Quispe

Producción científica: Capítulo del libro/informe/acta de congreso › Articulo (Contribución a conferencia) › revisión exhaustiva

1 Cita (Scopus)

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 original	Inglés
Título de la publicación alojada	Proceedings - 2015 41st Latin American Computing Conference, CLEI 2015
Editores	Alex Cuadros-Vargas, Hector Cancela, Ernesto Cuadros-Vargas
Editorial	Institute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)	9781467391436
DOI	https://doi.org/10.1109/CLEI.2015.7359466
Estado	Publicada - 16 dic. 2015
Publicado de forma externa	Sí
Evento	41st Latin American Computing Conference, CLEI 2015 - Arequipa, Perú Duración: 19 oct. 2015 → 23 oct. 2015

Serie de la publicación

Nombre	Proceedings - 2015 41st Latin American Computing Conference, CLEI 2015

Conferencia

Conferencia	41st Latin American Computing Conference, CLEI 2015
País/Territorio	Perú
Ciudad	Arequipa
Período	19/10/15 → 23/10/15

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

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10.1109/CLEI.2015.7359466

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Melisa, A. R., Fabián, K. F., & Quispe, J. H. (2015). Time series analysis of agro-meteorological through algorithms scalable data mining case: Chili river watershed, Arequipa. En A. Cuadros-Vargas, H. Cancela, & E. Cuadros-Vargas (Eds.), Proceedings - 2015 41st Latin American Computing Conference, CLEI 2015 ???articleNumberLabel??? 7359466 (Proceedings - 2015 41st Latin American Computing Conference, CLEI 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CLEI.2015.7359466

Melisa, Abarca Romero ; Fabián, Karla Fernández ; Quispe, Jose Herrera. / Time series analysis of agro-meteorological through algorithms scalable data mining case : Chili river watershed, Arequipa. Proceedings - 2015 41st Latin American Computing Conference, CLEI 2015. editor / Alex Cuadros-Vargas ; Hector Cancela ; Ernesto Cuadros-Vargas. Institute of Electrical and Electronics Engineers Inc., 2015. (Proceedings - 2015 41st Latin American Computing Conference, CLEI 2015).

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

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.",

keywords = "agro-meteorological data, evaporation, flow, Motifs, Naive Bayes, neural networks linear regression, precipitation, prediction",

author = "Melisa, {Abarca Romero} and Fabi{\'a}n, {Karla Fern{\'a}ndez} and Quispe, {Jose Herrera}",

note = "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: {\textcopyright} 2015 IEEE.; 41st Latin American Computing Conference, CLEI 2015 ; Conference date: 19-10-2015 Through 23-10-2015",

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Melisa, AR, Fabián, KF & Quispe, JH 2015, Time series analysis of agro-meteorological through algorithms scalable data mining case: Chili river watershed, Arequipa. En A Cuadros-Vargas, H Cancela & E Cuadros-Vargas (eds.), Proceedings - 2015 41st Latin American Computing Conference, CLEI 2015., 7359466, Proceedings - 2015 41st Latin American Computing Conference, CLEI 2015, Institute of Electrical and Electronics Engineers Inc., 41st Latin American Computing Conference, CLEI 2015, Arequipa, Perú, 19/10/15. https://doi.org/10.1109/CLEI.2015.7359466

Time series analysis of agro-meteorological through algorithms scalable data mining case: Chili river watershed, Arequipa. / Melisa, Abarca Romero; Fabián, Karla Fernández; Quispe, Jose Herrera.
Proceedings - 2015 41st Latin American Computing Conference, CLEI 2015. ed. / Alex Cuadros-Vargas; Hector Cancela; Ernesto Cuadros-Vargas. Institute of Electrical and Electronics Engineers Inc., 2015. 7359466 (Proceedings - 2015 41st Latin American Computing Conference, CLEI 2015).

Producción científica: Capítulo del libro/informe/acta de congreso › Articulo (Contribución a conferencia) › revisión exhaustiva

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

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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 -

Melisa AR, Fabián KF, Quispe JH. Time series analysis of agro-meteorological through algorithms scalable data mining case: Chili river watershed, Arequipa. En Cuadros-Vargas A, Cancela H, Cuadros-Vargas E, editores, Proceedings - 2015 41st Latin American Computing Conference, CLEI 2015. Institute of Electrical and Electronics Engineers Inc. 2015. 7359466. (Proceedings - 2015 41st Latin American Computing Conference, CLEI 2015). doi: 10.1109/CLEI.2015.7359466

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

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