Effective Features and Hybrid Classifier for Rainfall Prediction
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, tra...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2014-09-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/25868530.pdf |
| _version_ | 1818534356553039872 |
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| author | B KavithaRani A. Govardhan |
| author_facet | B KavithaRani A. Govardhan |
| author_sort | B KavithaRani |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T18:10:35Z |
| format | Article |
| id | doaj.art-6dbfe261ad4b4ec8a5041f37caddc45f |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-12-11T18:10:35Z |
| publishDate | 2014-09-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-6dbfe261ad4b4ec8a5041f37caddc45f2022-12-22T00:55:34ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832014-09-017510.1080/18756891.2014.960234Effective Features and Hybrid Classifier for Rainfall PredictionB KavithaRaniA. GovardhanRainfall 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.https://www.atlantis-press.com/article/25868530.pdfrainfall predictionhybrid classifierfeature indicatorABCgeneticFFNN |
| spellingShingle | B KavithaRani A. Govardhan Effective Features and Hybrid Classifier for Rainfall Prediction International Journal of Computational Intelligence Systems rainfall prediction hybrid classifier feature indicator ABC genetic FFNN |
| title | Effective Features and Hybrid Classifier for Rainfall Prediction |
| title_full | Effective Features and Hybrid Classifier for Rainfall Prediction |
| title_fullStr | Effective Features and Hybrid Classifier for Rainfall Prediction |
| title_full_unstemmed | Effective Features and Hybrid Classifier for Rainfall Prediction |
| title_short | Effective Features and Hybrid Classifier for Rainfall Prediction |
| title_sort | effective features and hybrid classifier for rainfall prediction |
| topic | rainfall prediction hybrid classifier feature indicator ABC genetic FFNN |
| url | https://www.atlantis-press.com/article/25868530.pdf |
| work_keys_str_mv | AT bkavitharani effectivefeaturesandhybridclassifierforrainfallprediction AT agovardhan effectivefeaturesandhybridclassifierforrainfallprediction |