dc.date.accessioned | 2024-03-15T17:48:09Z | |
dc.date.created | 2023-06-15T17:21:55Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Lin, Kangling Chen, Hua Zhou, Yanlai Sheng, Sheng Luo, Yuxuan Guo, Shenglian Xu, Chong-Yu . Exploring a similarity search-based data-driven framework for multi-step-ahead flood forecasting. Science of the Total Environment. 2023, 891 | |
dc.identifier.uri | http://hdl.handle.net/10852/109623 | |
dc.description.abstract | Due to a small proportion of observations, reliable and accurate flood forecasts for large floods present a fundamental challenge to artificial neural network models, especially when the forecast horizons exceed the flood concentration time of a river basin. This study proposed for the first time a Similarity search-based data-driven framework, and takes the advanced Temporal Convolutional Network based Encoder-Decoder model (S-TCNED) as an example for multi-step-ahead flood forecasting. A total of 5232 hourly hydrological data were divided into two datasets for model training and testing. The input sequence of the model included hourly flood flows of a hydrological station and rainfall data (traced back to the previous 32 h) of 15 gauge stations, and the output sequence stepped into 1- up to 16-hour-ahead flood forecasts. A conventional TCNED model was also built for comparison purposes. The results demonstrated that both TCNED and S-TCNED could make suitable multi-step-ahead flood forecasts, while the proposed S-TCNED model not only could effectively mimic the long-term rainfall-runoff relationship but also could provide more reliable and accurate forecasts of large floods than the TCNED model even in extreme weather conditions. There is a significant positive correlation between the mean sample label density improvement and the mean Nash-Sutcliffe Efficiency (NSE) improvement of the S-TCNED over the TCNED at the long forecast horizons (13 h up to 16 h). Based on the analysis of the sample label density, it is found that the similarity search largely improves the model performance by enabling the S-TCNED model to learn the development process of similar historical floods in a targeted manner. We conclude that the proposed S-TCNED model that converts and associates the previous rainfall-runoff sequence with the forecasting runoff sequence under a similar scenario can enhance the reliability and accuracy of flood forecasts while extending the length of forecast horizons. | |
dc.language | EN | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.title | Exploring a similarity search-based data-driven framework for multi-step-ahead flood forecasting | |
dc.title.alternative | ENEngelskEnglishExploring a similarity search-based data-driven framework for multi-step-ahead flood forecasting | |
dc.type | Journal article | |
dc.creator.author | Lin, Kangling | |
dc.creator.author | Chen, Hua | |
dc.creator.author | Zhou, Yanlai | |
dc.creator.author | Sheng, Sheng | |
dc.creator.author | Luo, Yuxuan | |
dc.creator.author | Guo, Shenglian | |
dc.creator.author | Xu, Chong-Yu | |
dc.date.embargoenddate | 2025-05-26 | |
cristin.unitcode | 185,15,22,0 | |
cristin.unitname | Institutt for geofag | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 2 | |
dc.identifier.cristin | 2155027 | |
dc.identifier.bibliographiccitation | info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Science of the Total Environment&rft.volume=891&rft.spage=&rft.date=2023 | |
dc.identifier.jtitle | Science of the Total Environment | |
dc.identifier.volume | 891 | |
dc.identifier.pagecount | 17 | |
dc.identifier.doi | https://doi.org/10.1016/j.scitotenv.2023.164494 | |
dc.type.document | Tidsskriftartikkel | |
dc.type.peerreviewed | Peer reviewed | |
dc.source.issn | 0048-9697 | |
dc.type.version | AcceptedVersion | |
cristin.articleid | 164494 | |