Daily Precipitation Prediction Based on SVM-CEEMDAN-BiLSTM Model
In order to solve the problem of low prediction accuracy of the maximum value and rain-free days in daily precipitation series,a coupled model of precipitation prediction based on a support vector machine (SVM),complete ensemble empirical modal decomposition (CEEMDAN),and bi-directional long and sho...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Pearl River
2023-01-01
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| Series: | Renmin Zhujiang |
| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2023.09.008 |
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| _version_ | 1841536073652502528 |
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| author | LING Ming XIAO Liying ZHAO Jia WANG Pinggen WANG Yin XIANG Kai CAI Gaotang |
| author_facet | LING Ming XIAO Liying ZHAO Jia WANG Pinggen WANG Yin XIANG Kai CAI Gaotang |
| author_sort | LING Ming |
| collection | DOAJ |
| description | In order to solve the problem of low prediction accuracy of the maximum value and rain-free days in daily precipitation series,a coupled model of precipitation prediction based on a support vector machine (SVM),complete ensemble empirical modal decomposition (CEEMDAN),and bi-directional long and short-term memory neural network (BiLSTM) was proposed. This paper applied the coupled model to predict the daily precipitation at Jingdezhen Station and Ganxian Station in the Poyang Lake Basin, and the results were compared with those of traditional model combinations. The results show that the precipitation prediction results of the coupled model are basically consistent with the measured ones, with the highest accuracy. It thus provides a reference for solving the problem of low prediction accuracy of the maximum value and rain-free days during daily precipitation prediction. |
| format | Article |
| id | doaj-art-1657ff07402e4db18f911d40906c35bc |
| institution | Kabale University |
| issn | 1001-9235 |
| language | zho |
| publishDate | 2023-01-01 |
| publisher | Editorial Office of Pearl River |
| record_format | Article |
| series | Renmin Zhujiang |
| spelling | doaj-art-1657ff07402e4db18f911d40906c35bc2025-01-15T02:22:25ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352023-01-014447639221Daily Precipitation Prediction Based on SVM-CEEMDAN-BiLSTM ModelLING MingXIAO LiyingZHAO JiaWANG PinggenWANG YinXIANG KaiCAI GaotangIn order to solve the problem of low prediction accuracy of the maximum value and rain-free days in daily precipitation series,a coupled model of precipitation prediction based on a support vector machine (SVM),complete ensemble empirical modal decomposition (CEEMDAN),and bi-directional long and short-term memory neural network (BiLSTM) was proposed. This paper applied the coupled model to predict the daily precipitation at Jingdezhen Station and Ganxian Station in the Poyang Lake Basin, and the results were compared with those of traditional model combinations. The results show that the precipitation prediction results of the coupled model are basically consistent with the measured ones, with the highest accuracy. It thus provides a reference for solving the problem of low prediction accuracy of the maximum value and rain-free days during daily precipitation prediction.http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2023.09.008 |
| spellingShingle | LING Ming XIAO Liying ZHAO Jia WANG Pinggen WANG Yin XIANG Kai CAI Gaotang Daily Precipitation Prediction Based on SVM-CEEMDAN-BiLSTM Model Renmin Zhujiang |
| title | Daily Precipitation Prediction Based on SVM-CEEMDAN-BiLSTM Model |
| title_full | Daily Precipitation Prediction Based on SVM-CEEMDAN-BiLSTM Model |
| title_fullStr | Daily Precipitation Prediction Based on SVM-CEEMDAN-BiLSTM Model |
| title_full_unstemmed | Daily Precipitation Prediction Based on SVM-CEEMDAN-BiLSTM Model |
| title_short | Daily Precipitation Prediction Based on SVM-CEEMDAN-BiLSTM Model |
| title_sort | daily precipitation prediction based on svm ceemdan bilstm model |
| url | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2023.09.008 |
| work_keys_str_mv | AT lingming dailyprecipitationpredictionbasedonsvmceemdanbilstmmodel AT xiaoliying dailyprecipitationpredictionbasedonsvmceemdanbilstmmodel AT zhaojia dailyprecipitationpredictionbasedonsvmceemdanbilstmmodel AT wangpinggen dailyprecipitationpredictionbasedonsvmceemdanbilstmmodel AT wangyin dailyprecipitationpredictionbasedonsvmceemdanbilstmmodel AT xiangkai dailyprecipitationpredictionbasedonsvmceemdanbilstmmodel AT caigaotang dailyprecipitationpredictionbasedonsvmceemdanbilstmmodel |