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Runoff Forecast for the Flood Season Based on Physical Factors and Their Effect Process and Its Application in the Second Songhua River Basin, China
Sustainability, Volume: 14, Issue: 17, Start page: 10627
Swansea University Author:
Yunqing Xuan
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DOI (Published version): 10.3390/su141710627
Abstract
The Second Songhua River Basin is located at the northern edge of the East Asian monsoonregion in China. The river basin has a large interannual rainfall-runoff variation often associated withfrequent droughts and floods. Therefore, the mid-long-term runoff prediction is of great significance.Accord...
Published in: | Sustainability |
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ISSN: | 2071-1050 |
Published: |
MDPI AG
2022
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Online Access: |
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URI: | https://cronfa.swan.ac.uk/Record/cronfa60997 |
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Abstract: |
The Second Songhua River Basin is located at the northern edge of the East Asian monsoonregion in China. The river basin has a large interannual rainfall-runoff variation often associated withfrequent droughts and floods. Therefore, the mid-long-term runoff prediction is of great significance.According to a review of the national and international literature, there are few studies on sunspots inthe prediction of medium- and long-term runoff. In this study, sunspots are selected as the influencingfactors of runoff based on the mechanism of astronomical factors; sensitivity analysis was used toidentify the time delay of sunspots’ influence on runoff and determine the prediction factor (relativenumber of sunspots in January and March). The BP (backpropagation) network is used to identifythe correlation between prediction factors and prediction items (monthly average inflow rate of theFengman Reservoir and the Baishan Reservoir in the flood season), and then the prediction model isconstructed. According to the test results of historical data and the actual forecast results, the forecastis working well, and the accuracy of qualitative forecasting is high. |
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Keywords: |
medium-long-term runoff forecast; sunspots; BP (backpropagation) network; sensitivity analysis; Second Songhua River Basin |
College: |
Faculty of Science and Engineering |
Funders: |
This study was supported by key R&D project funding from Jilin Province Science and Technology Department, China, 20200403070SF. |
Issue: |
17 |
Start Page: |
10627 |