Naive Bayes Classifier (NBC) for Forecasting Rainfall in Banyuwangi District Using Projection Pursuit Regression (PPR) Method
- 10.2991/acsr.k.220202.036How to use a DOI?
- General Circulation Model (GCM); Statistical Downscaling (SDs); Projection Pursuit Regression (PPR); Classification; Naive Bayes Classifier (NBC)
Rainfall is one of the climates that has a big influence on life, such as aviation, plantations, and agriculture. Remote areas like Banyuwangi Regency are most likely to lack information on weather and climate data. Rainfall information in the future is also very decisive for the community in carrying out their daily lives, therefore prediction models or rainfall forecasting are very necessary for the community. This situation has encouraged the development of various models of approaches for forecasting rainfall. One approach for forecasting rainfall is the use of Global Circulation Model (GCM) data. GCM resolution is too low to predict local climate which is influenced by topography and land use, but it is still possible to use GCM to obtain local scale information if Statistical Downscaling (SDs) technique is used. SDs is a technique that connects GCM output as a predictor variable with local rainfall in Banyuwangi Regency as a response variable with an intermediary functional model. As for the GCM output response variable, there are three variables used in this study, namely rainfall, sea level pressure, and air temperature with a domain of 3×3 to 10×10. Forecasting rainfall in Banyuwangi Regency is carried out using the Projection Pursuit Regression (PPR) method. At the modeling stage with PPR, the optimum domain and many functions will be determined, where the chosen domain is the 6×6 domain and the optimum number of functions is m=6 with RMSEP value of 89.79. Furthermore, a process is needed to represent the forecasting results in simpler way, such as classification. The classification method used in this study is the Naive Bayes Classifier (NBC). Evidently, NBC uses PPR as a model produces forecasts classification with correct values for 18 months out of a total of 24 months with 75% of accuracy.
- © 2022 The Authors. Published by Atlantis Press International B.V.
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Cite this article
TY - CONF AU - Ana Ulul Azmi AU - Alfian Futuhul Hadi AU - Yuliani Setia Dewi AU - I Made Tirta AU - Firdaus Ubaidillah AU - Dian Anggraeni PY - 2022 DA - 2022/02/08 TI - Naive Bayes Classifier (NBC) for Forecasting Rainfall in Banyuwangi District Using Projection Pursuit Regression (PPR) Method BT - Proceedings of the International Conference on Mathematics, Geometry, Statistics, and Computation (IC-MaGeStiC 2021) PB - Atlantis Press SP - 190 EP - 195 SN - 2352-538X UR - https://doi.org/10.2991/acsr.k.220202.036 DO - 10.2991/acsr.k.220202.036 ID - Azmi2022 ER -