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DOI | 10.3390/w16091248 |
Forecasting the River Water Discharge by Artificial Intelligence Methods | |
Barbulescu, Alina; Zhen, Liu | |
发表日期 | 2024 |
EISSN | 2073-4441 |
起始页码 | 16 |
结束页码 | 9 |
卷号 | 16期号:9 |
英文摘要 | The management of water resources must be based on accurate models of the river discharge in the context of the water flow alteration due to anthropic influences and climate change. Therefore, this article addresses the challenge of detecting the best model among three artificial intelligence techniques (AI)-backpropagation neural networks (BPNN), long short-term memory (LSTM), and extreme learning machine (ELM)-for the monthly data series discharge of the Buz & abreve;u River, in Romania. The models were built for three periods: January 1955-September 2006 (S1 series), January 1955-December 1983 (S2 series), and January 1984-December 2010 (S series). In terms of mean absolute error (MAE), the best performances were those of ELM on both Training and Test sets on S2, with MAETraining = 5.02 and MAETest = 4.01. With respect to MSE, the best was LSTM on the Training set of S2 (MSE = 60.07) and ELM on the Test set of S2 (MSE = 32.21). Accounting for the R2 value, the best model was LSTM on S2 (R2Training = 99.92%, and R2Test = 99.97%). ELM was the fastest, with 0.6996 s, 0.7449 s, and 0.6467 s, on S, S1, and S2, respectively. |
英文关键词 | river discharge; BPNN; ELM; LSTM |
语种 | 英语 |
WOS研究方向 | Environmental Sciences & Ecology ; Water Resources |
WOS类目 | Environmental Sciences ; Water Resources |
WOS记录号 | WOS:001219781100001 |
来源期刊 | WATER |
文献类型 | 期刊论文 |
条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/294899 |
作者单位 | Transylvania University of Brasov; China University of Petroleum |
推荐引用方式 GB/T 7714 | Barbulescu, Alina,Zhen, Liu. Forecasting the River Water Discharge by Artificial Intelligence Methods[J],2024,16(9). |
APA | Barbulescu, Alina,&Zhen, Liu.(2024).Forecasting the River Water Discharge by Artificial Intelligence Methods.WATER,16(9). |
MLA | Barbulescu, Alina,et al."Forecasting the River Water Discharge by Artificial Intelligence Methods".WATER 16.9(2024). |
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