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

Research output: Chapter in Book/Report/Conference proceeding › Paper (Conference contribution) › peer-review

2 Scopus citations

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 language	English
Title of host publication	Proceedings - 2015 41st Latin American Computing Conference, CLEI 2015
Editors	Alex Cuadros-Vargas, Hector Cancela, Ernesto Cuadros-Vargas
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781467391436
DOIs	https://doi.org/10.1109/CLEI.2015.7359466
State	Published - 16 Dec 2015
Externally published	Yes
Event	41st Latin American Computing Conference, CLEI 2015 - Arequipa, Peru Duration: 19 Oct 2015 → 23 Oct 2015

Publication series

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

Conference

Conference	41st Latin American Computing Conference, CLEI 2015
Country/Territory	Peru
City	Arequipa
Period	19/10/15 → 23/10/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 13 Climate Action

Keywords

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

Access to Document

10.1109/CLEI.2015.7359466

Cite this

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. In A. Cuadros-Vargas, H. Cancela, & E. Cuadros-Vargas (Eds.), Proceedings - 2015 41st Latin American Computing Conference, CLEI 2015 Article 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).

@inproceedings{e00bf1951af64ff6b840bc3cf2b04fff,

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",

year = "2015",

month = dec,

day = "16",

doi = "10.1109/CLEI.2015.7359466",

language = "Ingl{\'e}s",

series = "Proceedings - 2015 41st Latin American Computing Conference, CLEI 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

editor = "Alex Cuadros-Vargas and Hector Cancela and Ernesto Cuadros-Vargas",

booktitle = "Proceedings - 2015 41st Latin American Computing Conference, CLEI 2015",

}

Melisa, AR, Fabián, KF & Quispe, JH 2015, Time series analysis of agro-meteorological through algorithms scalable data mining case: Chili river watershed, Arequipa. in 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, Peru, 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).

Research output: Chapter in Book/Report/Conference proceeding › Paper (Conference contribution) › peer-review

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 - https://www.scopus.com/pages/publications/84961912388

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. In Cuadros-Vargas A, Cancela H, Cuadros-Vargas E, editors, 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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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