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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MDPI AG
2025-01-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/16/1/106 |
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| author | Mohammed Achite Okan Mert Katipoğlu Veysi Kartal Metin Sarıgöl Muhammad Jehanzaib Enes Gül |
| author_facet | Mohammed Achite Okan Mert Katipoğlu Veysi Kartal Metin Sarıgöl Muhammad Jehanzaib Enes Gül |
| author_sort | Mohammed Achite |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7add0403a0054ac08559b57d11aff48d |
| institution | Kabale University |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-7add0403a0054ac08559b57d11aff48d2025-01-24T13:22:03ZengMDPI AGAtmosphere2073-44332025-01-0116110610.3390/atmos16010106Advanced Soft Computing Techniques for Monthly Streamflow Prediction in Seasonal RiversMohammed Achite0Okan Mert Katipoğlu1Veysi Kartal2Metin Sarıgöl3Muhammad Jehanzaib4Enes Gül5Laboratory of Water and Environment, Faculty of Nature and Life Sciences, Hassiba Benbouali University of Chlef, Chlef 02180, AlgeriaDepartment of Civil Engineering, Erzincan Binali Yıldırım University, 24002 Erzincan, TurkeyDepartment of Civil Engineering, Siirt and Firat University, 56000 Siirt, TurkeyErzincan Uzumlu Vocational School, Erzincan Binali Yildirim University; 24002 Erzincan, TurkeyResearch Institute of Engineering and Technology, Hanyang University, Ansan 15588, Republic of KoreaCivil Engineering Department, Inonu University, 44280 Malatya, TurkeyThe 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.https://www.mdpi.com/2073-4433/16/1/106deep learningdroughtsoft computingGMDHstreamflowprediction |
| spellingShingle | Mohammed Achite Okan Mert Katipoğlu Veysi Kartal Metin Sarıgöl Muhammad Jehanzaib Enes Gül Advanced Soft Computing Techniques for Monthly Streamflow Prediction in Seasonal Rivers Atmosphere deep learning drought soft computing GMDH streamflow prediction |
| title | Advanced Soft Computing Techniques for Monthly Streamflow Prediction in Seasonal Rivers |
| title_full | Advanced Soft Computing Techniques for Monthly Streamflow Prediction in Seasonal Rivers |
| title_fullStr | Advanced Soft Computing Techniques for Monthly Streamflow Prediction in Seasonal Rivers |
| title_full_unstemmed | Advanced Soft Computing Techniques for Monthly Streamflow Prediction in Seasonal Rivers |
| title_short | Advanced Soft Computing Techniques for Monthly Streamflow Prediction in Seasonal Rivers |
| title_sort | advanced soft computing techniques for monthly streamflow prediction in seasonal rivers |
| topic | deep learning drought soft computing GMDH streamflow prediction |
| url | https://www.mdpi.com/2073-4433/16/1/106 |
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