Estimation of daily suspended sediment concentration in the Ca River Basin using a sediment rating curve, multiple regression, and long short-term memory model

IF 2.7 4区 环境科学与生态学 Q2 WATER RESOURCES
Chien Pham Van, Hien T. T. Le, Le Van Chin
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Abstract

This study presents a sediment rating curve (SRC), multiple regression (MR), and long short-term memory (LSTM) model for estimating daily suspended sediment concentration (SSC). The data of daily SSC at Yen Thuong and daily flow at five locations in the Ca River Basin, Vietnam are used to demonstrate multiple approaches. Using the daily flow and SSC data in the period from 2009 to 2019, appropriate coefficients in each method are identified carefully using five popular criteria. The results showed that SRC and MR approaches reproduced acceptably the observed values, with the values of RMSE, MAE, and ME of daily SSC being less than 5% of daily SSC magnitude observed at the station, while NSE ranges from 0.47 to 0.63 and r coefficient varies between 0.69 and 0.80. The LSTM model represented the observed values of daily SSC very well. The values of two dimensionless criteria are greater than 0.94 and its values of three-dimensional criteria are smaller than 2.0% of the observed magnitude of daily SSC in both training and validation steps. The LSTM model is found to be the best among the three investigated approaches. Then, the model is applied to estimate daily SSC values for the period from 1969 to 2008 and the year 2020.
利用沉积物等级曲线、多元回归和长短期记忆模型估算卡河流域的日悬浮沉积物浓度
本研究提出了泥沙等级曲线(SRC)、多元回归(MR)和长短期记忆(LSTM)模型来估计日悬沙浓度(SSC)。本文利用Yen Thuong的日SSC数据和越南Ca河流域5个地点的日流量数据证明了多种方法。利用2009年至2019年期间的日流量和SSC数据,使用五种常用标准仔细确定每种方法的适当系数。结果表明,SRC和MR方法可较好地再现观测值,日SSC的RMSE、MAE和ME值均小于观测站日SSC观测值的5%,NSE范围在0.47 ~ 0.63之间,r系数在0.69 ~ 0.80之间。LSTM模型较好地反映了日SSC的观测值。在训练和验证步骤中,两个无量纲准则值均大于0.94,其三维准则值均小于每日SSC观测值的2.0%。LSTM模型是三种方法中效果最好的。然后,应用该模型估计了1969 ~ 2008年和2020年的日SSC值。
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来源期刊
CiteScore
4.80
自引率
10.70%
发文量
168
审稿时长
>12 weeks
期刊介绍: Journal of Water and Climate Change publishes refereed research and practitioner papers on all aspects of water science, technology, management and innovation in response to climate change, with emphasis on reduction of energy usage.
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