Cargando…
Runoff Forecasting Using Machine-Learning Methods: Case Study in the Middle Reaches of Xijiang River
Runoff forecasting is useful for flood early warning and water resource management. In this study, backpropagation (BP) neural network, generalized regression neural network (GRNN), extreme learning machine (ELM), and wavelet neural network (WNN) models were employed, and a high-accuracy runoff fore...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8856602/ https://www.ncbi.nlm.nih.gov/pubmed/35187478 http://dx.doi.org/10.3389/fdata.2021.752406 |
_version_ | 1784653881610862592 |
---|---|
author | Xiao, Lu Zhong, Ming Zha, Dawei |
author_facet | Xiao, Lu Zhong, Ming Zha, Dawei |
author_sort | Xiao, Lu |
collection | PubMed |
description | Runoff forecasting is useful for flood early warning and water resource management. In this study, backpropagation (BP) neural network, generalized regression neural network (GRNN), extreme learning machine (ELM), and wavelet neural network (WNN) models were employed, and a high-accuracy runoff forecasting model was developed at Wuzhou station in the middle reaches of Xijiang River. The GRNN model was selected as the optimal runoff forecasting model and was also used to predict the streamflow and water level by considering the flood propagation time. Results show that (1) the GRNN presents the best performance in the 7-day lead time of streamflow; (2) the WNN model shows the highest accuracy in the 7-day lead time of water level; (3) the GRNN model performs well in runoff forecasting by considering flood propagation time, increasing the Qualification Rate (QR) of mean streamflow and water level forecast to 98.36 and 82.74%, respectively, and illustrates scientifically of the peak underestimation in streamflow and water level. This research proposes a high-accuracy runoff forecasting model using machine learning, which would improve the early warning capabilities of floods and droughts, the results also lay an important foundation for the mid-long-term runoff forecasting. |
format | Online Article Text |
id | pubmed-8856602 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-88566022022-02-19 Runoff Forecasting Using Machine-Learning Methods: Case Study in the Middle Reaches of Xijiang River Xiao, Lu Zhong, Ming Zha, Dawei Front Big Data Big Data Runoff forecasting is useful for flood early warning and water resource management. In this study, backpropagation (BP) neural network, generalized regression neural network (GRNN), extreme learning machine (ELM), and wavelet neural network (WNN) models were employed, and a high-accuracy runoff forecasting model was developed at Wuzhou station in the middle reaches of Xijiang River. The GRNN model was selected as the optimal runoff forecasting model and was also used to predict the streamflow and water level by considering the flood propagation time. Results show that (1) the GRNN presents the best performance in the 7-day lead time of streamflow; (2) the WNN model shows the highest accuracy in the 7-day lead time of water level; (3) the GRNN model performs well in runoff forecasting by considering flood propagation time, increasing the Qualification Rate (QR) of mean streamflow and water level forecast to 98.36 and 82.74%, respectively, and illustrates scientifically of the peak underestimation in streamflow and water level. This research proposes a high-accuracy runoff forecasting model using machine learning, which would improve the early warning capabilities of floods and droughts, the results also lay an important foundation for the mid-long-term runoff forecasting. Frontiers Media S.A. 2022-02-04 /pmc/articles/PMC8856602/ /pubmed/35187478 http://dx.doi.org/10.3389/fdata.2021.752406 Text en Copyright © 2022 Xiao, Zhong and Zha. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Big Data Xiao, Lu Zhong, Ming Zha, Dawei Runoff Forecasting Using Machine-Learning Methods: Case Study in the Middle Reaches of Xijiang River |
title | Runoff Forecasting Using Machine-Learning Methods: Case Study in the Middle Reaches of Xijiang River |
title_full | Runoff Forecasting Using Machine-Learning Methods: Case Study in the Middle Reaches of Xijiang River |
title_fullStr | Runoff Forecasting Using Machine-Learning Methods: Case Study in the Middle Reaches of Xijiang River |
title_full_unstemmed | Runoff Forecasting Using Machine-Learning Methods: Case Study in the Middle Reaches of Xijiang River |
title_short | Runoff Forecasting Using Machine-Learning Methods: Case Study in the Middle Reaches of Xijiang River |
title_sort | runoff forecasting using machine-learning methods: case study in the middle reaches of xijiang river |
topic | Big Data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8856602/ https://www.ncbi.nlm.nih.gov/pubmed/35187478 http://dx.doi.org/10.3389/fdata.2021.752406 |
work_keys_str_mv | AT xiaolu runoffforecastingusingmachinelearningmethodscasestudyinthemiddlereachesofxijiangriver AT zhongming runoffforecastingusingmachinelearningmethodscasestudyinthemiddlereachesofxijiangriver AT zhadawei runoffforecastingusingmachinelearningmethodscasestudyinthemiddlereachesofxijiangriver |