Advanced Soft Computing Techniques for Monthly Streamflow Prediction in Seasonal Rivers

The rising incidence of droughts in specific global regions in recent years, primarily attributed to global warming, has markedly increased the demand for reliable and accurate streamflow estimation. Streamflow estimation is essential for the effective management and utilization of water resources,...

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Vydané v:Atmosphere Ročník 16; číslo 1; s. 106
Hlavní autori: Achite, Mohammed, Katipoğlu, Okan Mert, Kartal, Veysi, Sarıgöl, Metin, Jehanzaib, Muhammad, Gül, Enes
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.01.2025
Predmet:
Accuracy
Algorithms
Artificial intelligence
Artificial neural networks
Back propagation
Climate change
Computational intelligence
Correlation coefficient
Correlation coefficients
Deep learning
Drought
Environmental aspects
Error analysis
Floods
Forecast accuracy
Global warming
GMDH
Group method of data handling
Humidity
Hydroelectric power
Hydrology
Life on Earth
Long short-term memory
Machine learning
Monthly forecasts
Mountains
Neural networks
Performance evaluation
Precipitation
prediction
Quality of life
Recurrent neural networks
Regression analysis
Rivers
Soft computing
Statistical analysis
Stream discharge
Stream flow
Streamflow
Streamflow estimation
Streamflow forecasting
Vegetation
Water resources
Wavelet transforms
Turkey
Algeria
Iran
China
ISSN:2073-4433, 2073-4433
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Popis
Shrnutí:The rising incidence of droughts in specific global regions in recent years, primarily attributed to global warming, has markedly increased the demand for reliable and accurate streamflow estimation. Streamflow estimation is essential for the effective management and utilization of water resources, as well as for the design of hydraulic infrastructure. Furthermore, research on streamflow estimation has gained heightened importance because water is essential not only for the survival of all living organisms but also for determining the quality of life on Earth. In this study, advanced soft computing techniques, including long short-term memory (LSTM), convolutional neural network–recurrent neural network (CNN-RNN), and group method of data handling (GMDH) algorithms, were employed to forecast monthly streamflow time series at two different stations in the Wadi Mina basin. The performance of each technique was evaluated using statistical criteria such as mean square error (MSE), mean bias error (MBE), mean absolute error (MAE), and the correlation coefficient (R). The results of this study demonstrated that the GMDH algorithm produced the most accurate forecasts at the Sidi AEK Djillali station, with metrics of MSE: 0.132, MAE: 0.185, MBE: −0.008, and R: 0.636. Similarly, the CNN-RNN algorithm achieved the best performance at the Kef Mehboula station, with metrics of MSE: 0.298, MAE: 0.335, MBE: −0.018, and R: 0.597.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:2073-4433
2073-4433
DOI:10.3390/atmos16010106