On the linear trends of a water discharge data under temporal variation. Case study: the upper sector of the Buzău river (Romania)

Mocanu-Vargancsik, C A; Tudor, Georgiana

doi:10.5775/fg.2020.041.i

Cited by 2 publications

(4 citation statements)

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“…More details on the statistical analyses performed on the monthly Buzău River water discharge may be found in [53][54][55].…”

Section: Study Area and Data Seriesmentioning

confidence: 99%

“…Reducing the flood effects on the anthropic settlements was a practical problem that needed to be solved, and it was a study direction for different researchers. Some analyzed the dam's environmental effect, emphasizing the river flow alteration [8,12,[52][53][54][55][56]. A deep study of the impact of such structures may contribute to understanding this phenomenon and taking measures to reduce the effects of natural calamities.…”

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confidence: 99%

“…Its importance has not yet been emphasized enough. In Romania, only a few researchers investigated the change in the river flow regimen after the dam inauguration [53][54][55]. Moreover, the research of rivers' dynamics by AI methods is insignificant, the most utilized tools being statistical methods and hydraulic modeling [5][6][7][53][54][55].…”

mentioning

confidence: 99%

“…This result indicates different long-term tendencies of the monthly subseries. The KPSS test did not reject the stationarity hypothesis for all series.More details on the statistical analyses performed on the monthly Buzău River water discharge may be found in[53][54][55].…”

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confidence: 99%

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Forecasting the River Water Discharge by Artificial Intelligence Methods

Bărbulescu,

Zhen

2024

Water

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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 Buzău 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.

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“…More details on the statistical analyses performed on the monthly Buzău River water discharge may be found in [53][54][55].…”

Section: Study Area and Data Seriesmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Forecasting the River Water Discharge by Artificial Intelligence Methods

Bărbulescu,

Zhen

2024

Water

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Echo State Network and Sparrow Search: Echo State Network for Modeling the Monthly River Discharge of the Biggest River in Buzău County, Romania

Zhen,

Bărbulescu

2024

Water

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Artificial intelligence (AI) has become an instrument used in all domains with good results. The water resources management field is not an exception. Therefore, in this article, we propose two machine learning (ML) techniques—an echo state network (ESN) and sparrow search algorithm–echo state network (SSA-ESN)—for monthly modeling of the water discharge of one of the biggest rivers in Romania for three periods (S, S1, and S2). In both models, R2 was over 0.989 on the test and training sets and the mean absolute error (MAE) varied between 4.4826 and 7.6038. The performance of the SSA-ESN was similar, but the ESN had the shortest run time. The influence of anomalies on the models’ quality was assessed by running the algorithms on a series without the aberrant values, which were detected by the seasonal hybrid extreme studentized deviate (S-H-ESD) test. The results indicate that removing the anomalies significantly improved both models’ performance, but the run time was increased.

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On the linear trends of a water discharge data under temporal variation. Case study: the upper sector of the Buzău river (Romania)

Cited by 2 publications

References 0 publications

Forecasting the River Water Discharge by Artificial Intelligence Methods

Forecasting the River Water Discharge by Artificial Intelligence Methods

Echo State Network and Sparrow Search: Echo State Network for Modeling the Monthly River Discharge of the Biggest River in Buzău County, Romania

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