Application of the decomposition-prediction-reconstruction framework to medium- and long-term runoff forecasting

Medium- and long-term runoff forecasting has always been a problem, especially in the wet season. Forecasting performance can be improved using complementary ensemble empirical mode decomposition (CEEMD) to produce clearer signals as model inputs. In the forecasting models based on CEEMD, the entire...

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Autores principales:	Yi Ji, Hong-Tao Dong, Zhen-Xiang Xing, Ming-Xin Sun, Qiang Fu, Dong Liu
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	ceemd-lssvm hybrid approach medium- and long-term runoff forecasting nnbr Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
Acceso en línea:	https://doaj.org/article/aa8ae81aa5db404d816f3836f1ef8afa
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spelling	oai:doaj.org-article:aa8ae81aa5db404d816f3836f1ef8afa2021-11-06T07:09:27ZApplication of the decomposition-prediction-reconstruction framework to medium- and long-term runoff forecasting1606-97491607-079810.2166/ws.2020.337https://doaj.org/article/aa8ae81aa5db404d816f3836f1ef8afa2021-03-01T00:00:00Zhttp://ws.iwaponline.com/content/21/2/696https://doaj.org/toc/1606-9749https://doaj.org/toc/1607-0798Medium- and long-term runoff forecasting has always been a problem, especially in the wet season. Forecasting performance can be improved using complementary ensemble empirical mode decomposition (CEEMD) to produce clearer signals as model inputs. In the forecasting models based on CEEMD, the entire time series is decomposed into several sub-series, each sub-series is divided into training and validation datasets and forecasted by some common models, such as least squares support vector machine (LSSVM), and finally an ensemble forecasting result is obtained by summing the forecasted results of each sub-series. This model was applied to forecast the inflow runoff of the Shitouxia Reservoir (STX Reservoir). The forecasting results show that the Nash efficiency coefficient of the LSSVM model is 0.815, and the Nash efficiency coefficient of the CEEMD-LSSVM model is 0.954, an increase of 13.9%. The root mean square error value is reduced from 20.654 to 10.235, a decrease of 50.4%. The runoff forecasting performance can be effectively improved by applying the CEEMD-LSSVM model. When analyzing the annual runoff forecasting results month by month, it was found that the forecasting results for November to April were unsatisfactory compared results from the nearest neighbor bootstrapping regressive (NNBR) model, which was more suitable for the dry season, but the forecasting results for May to October improved significantly. This also proves that the CEEMD-LSSVM model has a great advantage in the forecasting of inflow runoff during the wet season. In the optimized operation of reservoirs, the forecasting result of inflow runoff in the wet season is more important than in the dry season. Therefore, when forecasting annual runoff month by month, the CEEMD-LSSVM model is recommended for the wet season combined with the NNBR model for the dry season. HIGHLIGHTS CEEMD is suitable for non-linear and non-stationary time series.; A comprehensive evaluation system was used to evaluate the accuracy of the different models.; The performance of the models can be improved by using the CEEMD method.; Different models are used for forecasting in the dry season and the wet season respectively.;Yi JiHong-Tao DongZhen-Xiang XingMing-Xin SunQiang FuDong LiuIWA Publishingarticleceemd-lssvmhybrid approachmedium- and long-term runoff forecastingnnbrWater supply for domestic and industrial purposesTD201-500River, lake, and water-supply engineering (General)TC401-506ENWater Supply, Vol 21, Iss 2, Pp 696-709 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ceemd-lssvm hybrid approach medium- and long-term runoff forecasting nnbr Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
spellingShingle	ceemd-lssvm hybrid approach medium- and long-term runoff forecasting nnbr Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506 Yi Ji Hong-Tao Dong Zhen-Xiang Xing Ming-Xin Sun Qiang Fu Dong Liu Application of the decomposition-prediction-reconstruction framework to medium- and long-term runoff forecasting
description	Medium- and long-term runoff forecasting has always been a problem, especially in the wet season. Forecasting performance can be improved using complementary ensemble empirical mode decomposition (CEEMD) to produce clearer signals as model inputs. In the forecasting models based on CEEMD, the entire time series is decomposed into several sub-series, each sub-series is divided into training and validation datasets and forecasted by some common models, such as least squares support vector machine (LSSVM), and finally an ensemble forecasting result is obtained by summing the forecasted results of each sub-series. This model was applied to forecast the inflow runoff of the Shitouxia Reservoir (STX Reservoir). The forecasting results show that the Nash efficiency coefficient of the LSSVM model is 0.815, and the Nash efficiency coefficient of the CEEMD-LSSVM model is 0.954, an increase of 13.9%. The root mean square error value is reduced from 20.654 to 10.235, a decrease of 50.4%. The runoff forecasting performance can be effectively improved by applying the CEEMD-LSSVM model. When analyzing the annual runoff forecasting results month by month, it was found that the forecasting results for November to April were unsatisfactory compared results from the nearest neighbor bootstrapping regressive (NNBR) model, which was more suitable for the dry season, but the forecasting results for May to October improved significantly. This also proves that the CEEMD-LSSVM model has a great advantage in the forecasting of inflow runoff during the wet season. In the optimized operation of reservoirs, the forecasting result of inflow runoff in the wet season is more important than in the dry season. Therefore, when forecasting annual runoff month by month, the CEEMD-LSSVM model is recommended for the wet season combined with the NNBR model for the dry season. HIGHLIGHTS CEEMD is suitable for non-linear and non-stationary time series.; A comprehensive evaluation system was used to evaluate the accuracy of the different models.; The performance of the models can be improved by using the CEEMD method.; Different models are used for forecasting in the dry season and the wet season respectively.;
format	article
author	Yi Ji Hong-Tao Dong Zhen-Xiang Xing Ming-Xin Sun Qiang Fu Dong Liu
author_facet	Yi Ji Hong-Tao Dong Zhen-Xiang Xing Ming-Xin Sun Qiang Fu Dong Liu
author_sort	Yi Ji
title	Application of the decomposition-prediction-reconstruction framework to medium- and long-term runoff forecasting
title_short	Application of the decomposition-prediction-reconstruction framework to medium- and long-term runoff forecasting
title_full	Application of the decomposition-prediction-reconstruction framework to medium- and long-term runoff forecasting
title_fullStr	Application of the decomposition-prediction-reconstruction framework to medium- and long-term runoff forecasting
title_full_unstemmed	Application of the decomposition-prediction-reconstruction framework to medium- and long-term runoff forecasting
title_sort	application of the decomposition-prediction-reconstruction framework to medium- and long-term runoff forecasting
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/aa8ae81aa5db404d816f3836f1ef8afa
work_keys_str_mv	AT yiji applicationofthedecompositionpredictionreconstructionframeworktomediumandlongtermrunoffforecasting AT hongtaodong applicationofthedecompositionpredictionreconstructionframeworktomediumandlongtermrunoffforecasting AT zhenxiangxing applicationofthedecompositionpredictionreconstructionframeworktomediumandlongtermrunoffforecasting AT mingxinsun applicationofthedecompositionpredictionreconstructionframeworktomediumandlongtermrunoffforecasting AT qiangfu applicationofthedecompositionpredictionreconstructionframeworktomediumandlongtermrunoffforecasting AT dongliu applicationofthedecompositionpredictionreconstructionframeworktomediumandlongtermrunoffforecasting
_version_	1718443815667236864

Application of the decomposition-prediction-reconstruction framework to medium- and long-term runoff forecasting

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