Effective Features and Hybrid Classifier for Rainfall Prediction
- DOI
- 10.1080/18756891.2014.960234How to use a DOI?
- Keywords
- rainfall prediction, hybrid classifier, feature indicator, ABC, genetic, FFNN
- Abstract
Rainfall prediction has emerged as a challenging time-series prediction problem in recent years. In this paper, we propose a novel rainfall prediction technique using effective feature indicators and a hybrid technique. Our proposed model consists of three phases, namely, layer model simulation, training phase and testing phase. At the outset, the input rainfall dataset is preprocessed using the feature indicators. There are five feature indicators used in the preprocessing step namely, channel index (CI), ulcer index (UI), rate of change (ROC), relative strength index (RSI) and average directional movement index (ADX). Subsequently, feature matrices are formed based on the preprocessed rainfall data. Once the feature matrix is formed, the prediction is done based on the hybrid classifier. In the hybrid classifier, artificial bee colony algorithm is combined with the genetic algorithm for training the feed forward neural network. The performance of the algorithm is analyzed with the help of real datasets gathered from Rayalaseema, Aandhra and Telangana regions. Finally, from comparative analysis it is established that the proposed rainfall prediction yields better result (MAC=4.0672) when compared with Artificial Bee Colony with Neural Network.
- Copyright
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - KavithaRani B. AU - A. Govardhan PY - 2014 DA - 2014/10/01 TI - Effective Features and Hybrid Classifier for Rainfall Prediction JO - International Journal of Computational Intelligence Systems SP - 937 EP - 951 VL - 7 IS - 5 SN - 1875-6883 UR - https://doi.org/10.1080/18756891.2014.960234 DO - 10.1080/18756891.2014.960234 ID - B.2014 ER -