Performance Comparison of Machine Learning Models for Annual Precipitation Prediction Using Different Decomposition Methods
It has become increasingly difficult in recent years to predict precipitation scientifically and accurately due to the dual effects of human activities and climatic conditions. This paper focuses on four aspects to improve precipitation prediction accuracy. Five decomposition methods (time-varying f...
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ftmdpi:oai:mdpi.com:/2072-4292/13/5/1018/ 2023-08-20T04:08:27+02:00 Performance Comparison of Machine Learning Models for Annual Precipitation Prediction Using Different Decomposition Methods Chao Song Xiaohong Chen agris 2021-03-08 application/pdf https://doi.org/10.3390/rs13051018 EN eng Multidisciplinary Digital Publishing Institute Atmospheric Remote Sensing https://dx.doi.org/10.3390/rs13051018 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 13; Issue 5; Pages: 1018 decomposition methods Elman neural network difference precipitation prediction Guangzhou Text 2021 ftmdpi https://doi.org/10.3390/rs13051018 2023-08-01T01:13:48Z It has become increasingly difficult in recent years to predict precipitation scientifically and accurately due to the dual effects of human activities and climatic conditions. This paper focuses on four aspects to improve precipitation prediction accuracy. Five decomposition methods (time-varying filter-based empirical mode decomposition (TVF-EMD), robust empirical mode decomposition (REMD), complementary ensemble empirical mode decomposition (CEEMD), wavelet transform (WT), and extreme-point symmetric mode decomposition (ESMD) combined with the Elman neural network (ENN)) are used to construct five prediction models, i.e., TVF-EMD-ENN, REMD-ENN, CEEMD-ENN, WT-ENN, and ESMD-ENN. The variance contribution rate (VCR) and Pearson correlation coefficient (PCC) are utilized to compare the performances of the five decomposition methods. The wavelet transform coherence (WTC) is used to determine the reason for the poor prediction performance of machine learning algorithms in individual years and the relationship with climate indicators. A secondary decomposition of the TVF-EMD is used to improve the prediction accuracy of the models. The proposed methods are used to predict the annual precipitation in Guangzhou. The subcomponents obtained from the TVF-EMD are the most stable among the four decomposition methods, and the North Atlantic Oscillation (NAO) index, the Nino 3.4 index, and sunspots have a smaller influence on the first subcomponent (Sc-1) than the other subcomponents. The TVF-EMD-ENN model has the best prediction performance and outperforms traditional machine learning models. The secondary decomposition of the Sc-1 of the TVF-EMD model significantly improves the prediction accuracy. Text North Atlantic North Atlantic oscillation MDPI Open Access Publishing Remote Sensing 13 5 1018 |
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English |
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decomposition methods Elman neural network difference precipitation prediction Guangzhou |
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decomposition methods Elman neural network difference precipitation prediction Guangzhou Chao Song Xiaohong Chen Performance Comparison of Machine Learning Models for Annual Precipitation Prediction Using Different Decomposition Methods |
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decomposition methods Elman neural network difference precipitation prediction Guangzhou |
description |
It has become increasingly difficult in recent years to predict precipitation scientifically and accurately due to the dual effects of human activities and climatic conditions. This paper focuses on four aspects to improve precipitation prediction accuracy. Five decomposition methods (time-varying filter-based empirical mode decomposition (TVF-EMD), robust empirical mode decomposition (REMD), complementary ensemble empirical mode decomposition (CEEMD), wavelet transform (WT), and extreme-point symmetric mode decomposition (ESMD) combined with the Elman neural network (ENN)) are used to construct five prediction models, i.e., TVF-EMD-ENN, REMD-ENN, CEEMD-ENN, WT-ENN, and ESMD-ENN. The variance contribution rate (VCR) and Pearson correlation coefficient (PCC) are utilized to compare the performances of the five decomposition methods. The wavelet transform coherence (WTC) is used to determine the reason for the poor prediction performance of machine learning algorithms in individual years and the relationship with climate indicators. A secondary decomposition of the TVF-EMD is used to improve the prediction accuracy of the models. The proposed methods are used to predict the annual precipitation in Guangzhou. The subcomponents obtained from the TVF-EMD are the most stable among the four decomposition methods, and the North Atlantic Oscillation (NAO) index, the Nino 3.4 index, and sunspots have a smaller influence on the first subcomponent (Sc-1) than the other subcomponents. The TVF-EMD-ENN model has the best prediction performance and outperforms traditional machine learning models. The secondary decomposition of the Sc-1 of the TVF-EMD model significantly improves the prediction accuracy. |
format |
Text |
author |
Chao Song Xiaohong Chen |
author_facet |
Chao Song Xiaohong Chen |
author_sort |
Chao Song |
title |
Performance Comparison of Machine Learning Models for Annual Precipitation Prediction Using Different Decomposition Methods |
title_short |
Performance Comparison of Machine Learning Models for Annual Precipitation Prediction Using Different Decomposition Methods |
title_full |
Performance Comparison of Machine Learning Models for Annual Precipitation Prediction Using Different Decomposition Methods |
title_fullStr |
Performance Comparison of Machine Learning Models for Annual Precipitation Prediction Using Different Decomposition Methods |
title_full_unstemmed |
Performance Comparison of Machine Learning Models for Annual Precipitation Prediction Using Different Decomposition Methods |
title_sort |
performance comparison of machine learning models for annual precipitation prediction using different decomposition methods |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2021 |
url |
https://doi.org/10.3390/rs13051018 |
op_coverage |
agris |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
Remote Sensing; Volume 13; Issue 5; Pages: 1018 |
op_relation |
Atmospheric Remote Sensing https://dx.doi.org/10.3390/rs13051018 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs13051018 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
5 |
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1018 |
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1774720729357484032 |