Hybridization of Haar Wavelet Decomposition and Computational Intelligent Algorithms for the Estimation of Climate Change Behavior
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14 avril 2014
- handle: 10670/1.c7f2si
- hal: hal-01397202
- doi: 10.1007/978-3-642-55032-4_23
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info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-642-55032-4_23
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Mots-clés
Haar Wavelet Decomposition Relevance Vector Machine Adaptive Linear Neural Network Climate ChangeSujets proches
Climatic changes--Environmental aspects Global climate changes Climate change Climatic change Climate variations Climate changes Global climatic changes Changes, Climatic Climatic variations Climatic fluctuationsCiter ce document
Haruna Chiroma et al., « Hybridization of Haar Wavelet Decomposition and Computational Intelligent Algorithms for the Estimation of Climate Change Behavior », HAL-SHS : sciences de l'information, de la communication et des bibliothèques, ID : 10.1007/978-3-642-55032-4_23
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Résumé
We propose a hybrid of haar wavelet decomposition, relevance vector machine, and adaptive linear neural network (HWD-RVMALNN) for the estimation of climate change behavior. The HWD-RVMALNN is able to improve estimation accuracy of climate change more than the approaches already discussed in the literature. Comparative simulation results show that the HWD-RVMALNN outperforms cyclical weight/bias rule, Levenberg-Marquardt, resilient back-propagation, support vector machine, and learning vector quantization neural networks in both estimation accuracy and computational efficiency. The model proposes in this study can provide future knowledge of climate change behavior. The future climate change behavior can be used by policy makers in formulating policies that can drastically reduce the negative impact of climate change, and be alert on possible consequences expected to occur in the future.