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 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.|
|State||Published - 16 Dec 2015|
|Event||41st Latin American Computing Conference, CLEI 2015 - Arequipa, Peru|
Duration: 19 Oct 2015 → 23 Oct 2015
|Name||Proceedings - 2015 41st Latin American Computing Conference, CLEI 2015|
|Conference||41st Latin American Computing Conference, CLEI 2015|
|Period||19/10/15 → 23/10/15|
Bibliographical notePublisher Copyright:
© 2015 IEEE.
- agro-meteorological data
- Naive Bayes
- neural networks linear regression