{"title":"利用残差数据和多次正校正技术预测山区集水区水库每小时来水","authors":"W. Guo, Wei-Bo Chen, Chih-Hsin Chang","doi":"10.2166/nh.2023.072","DOIUrl":null,"url":null,"abstract":"\n \n This study coupled the ensemble learning method with residual error (RE) correction to propose a more accurate hydrologic model for the time-series prediction of the reservoir inflow. To enhance the prediction capability of the model in mountain catchment, three deep learning (DL) models, namely the encoder–decoder gated recurrent units (ED-GRU), encoder–decoder long short-term memory network (ED-LSTM), and combined convolutional neural network with LSTM (CNN-LSTM), were deployed to train reservoir inflow prediction model for the lead times of 1–24 h. The prediction outputs from three DL models were then incorporated into the categorical gradient boosting regression (CGBR) model to resolve the highly non-linear relationship between model inputs and outputs. In the final procedure, the RE correction method was implemented by using the outcomes of the CGBR model to construct the proposed hybrid model. The proposed model was applied to simulate the hourly inflow in the Shihmen and Feitsui Reservoirs. The proposed model achieved improved performance by an average proportion of 66.2% compared to the three DL models. It is demonstrated that the proposed model is accurate in predicting the reservoir peak and total inflows and also performs well for storm events with multi-peak hydrographs.","PeriodicalId":55040,"journal":{"name":"Hydrology Research","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of hourly inflow for reservoirs at mountain catchments using residual error data and multiple-ahead correction technique\",\"authors\":\"W. Guo, Wei-Bo Chen, Chih-Hsin Chang\",\"doi\":\"10.2166/nh.2023.072\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n \\n This study coupled the ensemble learning method with residual error (RE) correction to propose a more accurate hydrologic model for the time-series prediction of the reservoir inflow. To enhance the prediction capability of the model in mountain catchment, three deep learning (DL) models, namely the encoder–decoder gated recurrent units (ED-GRU), encoder–decoder long short-term memory network (ED-LSTM), and combined convolutional neural network with LSTM (CNN-LSTM), were deployed to train reservoir inflow prediction model for the lead times of 1–24 h. The prediction outputs from three DL models were then incorporated into the categorical gradient boosting regression (CGBR) model to resolve the highly non-linear relationship between model inputs and outputs. In the final procedure, the RE correction method was implemented by using the outcomes of the CGBR model to construct the proposed hybrid model. The proposed model was applied to simulate the hourly inflow in the Shihmen and Feitsui Reservoirs. The proposed model achieved improved performance by an average proportion of 66.2% compared to the three DL models. It is demonstrated that the proposed model is accurate in predicting the reservoir peak and total inflows and also performs well for storm events with multi-peak hydrographs.\",\"PeriodicalId\":55040,\"journal\":{\"name\":\"Hydrology Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2023-08-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Hydrology Research\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.2166/nh.2023.072\",\"RegionNum\":4,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Environmental Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Hydrology Research","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.2166/nh.2023.072","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Environmental Science","Score":null,"Total":0}
Prediction of hourly inflow for reservoirs at mountain catchments using residual error data and multiple-ahead correction technique
This study coupled the ensemble learning method with residual error (RE) correction to propose a more accurate hydrologic model for the time-series prediction of the reservoir inflow. To enhance the prediction capability of the model in mountain catchment, three deep learning (DL) models, namely the encoder–decoder gated recurrent units (ED-GRU), encoder–decoder long short-term memory network (ED-LSTM), and combined convolutional neural network with LSTM (CNN-LSTM), were deployed to train reservoir inflow prediction model for the lead times of 1–24 h. The prediction outputs from three DL models were then incorporated into the categorical gradient boosting regression (CGBR) model to resolve the highly non-linear relationship between model inputs and outputs. In the final procedure, the RE correction method was implemented by using the outcomes of the CGBR model to construct the proposed hybrid model. The proposed model was applied to simulate the hourly inflow in the Shihmen and Feitsui Reservoirs. The proposed model achieved improved performance by an average proportion of 66.2% compared to the three DL models. It is demonstrated that the proposed model is accurate in predicting the reservoir peak and total inflows and also performs well for storm events with multi-peak hydrographs.
期刊介绍:
Hydrology Research provides international coverage on all aspects of hydrology in its widest sense, and welcomes the submission of papers from across the subject. While emphasis is placed on studies of the hydrological cycle, the Journal also covers the physics and chemistry of water. Hydrology Research is intended to be a link between basic hydrological research and the practical application of scientific results within the broad field of water management.