Research Article
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Year 2025, Volume: 9 Issue: 1, 62 - 77, 17.06.2025

Asli Bor , Merve Okan

Abstract

References

  • [1] Tan Kesgin, R. I., Demir, I., Kesgin, E., Abdelkader, M., & Agaccioglu, H. (2023). A data-driven approach to predict hydrometeorological variability and fluctuations in lake water levels. Journal of Water and Land Development, (58), 158-170. https://doi.org/10.24425/jwld.2023.146608.
  • [2] Demirel, M. C., Özen, A., Orta, S., Toker, E., Demir, H. K., Ekmekcioğlu, Ö., Tayşi, H., Eruçar, S., Sağ, A. B., Sarı, Ö., Tuncer, E., Hancı, H., Özcan, T. İ., Erdem, H., Koşucu, M. M., Başakın, E. E., Ahmed, K., Anwar, A., Avcuoğlu, M. B., Vanlı, Ö., Stisen, S., Booij, M. J. (2019). Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration. Water, 11(10), 2083. https://doi.org/10.3390/w11102083.
  • [3] Shamseldin, A. Y. (1997). Application of a neural network technique to rainfall-runoff modelling. Journal of Hydrology, 199(3): 272–294.
  • [4] Tokar, S. A., and Johnson, P. A. (1999). Rainfall-Runoff Modeling Using Artificial Neural Networks. Journal of Hydrologic Engineering, 4(3): 232–239.
  • [5] Chang, F.-J., and Chen, Y.-C. (2001). A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction. Journal of Hydrology, 245: 153–164.
  • [6] Öztopal, A., Kahya, C., and Asilhan, S. (2001). Yapay Sinir Ağları ile Akış Tahmini. 1. Türkiye Su Kongresi, İstanbul, Türkiye, 8 - 10 Ocak 2001, cilt.1. pp. 311–318.
  • [7] Jayawardena, A. W., and Fernando, T. M. K. G. (2001). River flow prediction: An artificial neural network approach. Regional Management of Water Resources, Maastricht, The Netherlands. pp. 239–246.
  • [8] Sivakumar, B., Jayawardena, A., and Fernando, T. M. K. G. (2002). River Flow Forecasting: Use of Phase-Space Reconstruction and Artificial Neural Networks Approaches. Journal of Hydrology, 265: 225–245.
  • [9] Dorado, J., RabuñAL, J. R., Pazos, A., Rivero, D., Santos, A., and Puertas, J. (2003). Prediction and modeling of the rainfall-runoff transformation of a typical urban basin using ann and gp. Applied Artificial Intelligence, Taylor & Francis, 17(4): 329–343.
  • [10] Kişi, Ö. (2005). Daily River Flow Forecasting Using Artificial Neural Networks and Auto-Regressive Models. Turkish Journal of Engineering and Environmental Sciences, 29: 9–20.
  • [11] Demirpençe, H. (2005). Köprüçay Akımlarının Yapay Sinir Ağları ile Tahmini. Antalya Yöresinin İnşaat Mühendisleri Sorunları Kongresi.
  • [12] Yurdusev, M. A., Acı, M., Turan, M. E., and İçağa, Y. (2008). Akarçay Nehri Aylık Akımlarının Yapay Sinir Ağları ile Tahmini. Celal Bayar Üniversitesi Fen Bilimleri Dergisi, 4(1): 73–88.
  • [13] Okkan, U., and Mollamahmutoğlu, A. (2010). Çoruh Nehri Günlük Akımlarının Yapay Sinir Ağları ile Tahmin Edilmesi. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 14(3): 251–261.
  • [14] Okkan, U., and Dalkilic, H. Y. (2010). Demirköprü Barajı Aylık Buharlaşma Yüksekliklerinin Yapay Sinir Ağları ile Tahmin Edilmesi. DSİ Teknik Bülten, 108: 30–36.
  • [15] Chen, S. M., Wang, Y. M., and Tsou, I. (2013). Using artificial neural network approach for modelling rainfall-runoff due to typhoon. Journal of Earth System Science, 122(2): 399–405.
  • [16] Kızılaslan, M. A., Sağın, F., Doğan, E., and Sönmez, O. (2014). Aşağı Sakarya Nehri akımlarının yapay sinir ağları ile tahmin edilmesi. SAÜ Fen Bilimleri Dergisi, 18(2): 99–103.
  • [17] Singh, G., Panda, R. K., and Lamers, M. (2015). Modeling of daily runoff from a small agricultural watershed using artificial neural network with resampling techniques. Journal of Hydroinformatics, 17(1): 56–74.
  • [18] Khan, M. Y. A., Hasan, F., Panwar, S., and Chakrapani, G. J. (2016). Neural network model for discharge and water-level prediction for Ramganga River catchment of Ganga Basin, India. Hydrological Sciences Journal, Taylor & Francis, 61(11): 2084–2095.
  • [19] Altunkaynak, A., and Başakin, E. E. (2018). Zaman Serileri Kullanılarak Nehir Akım Tahmini ve Farklı Yöntemlerle Karşılaştırılması. Erzincan University Journal of Science and Technology, 11(1): 92–101.
  • [20] Nacar, S., Hınıs, M. A., and Kankal, M. (2018). Forecasting Daily Streamflow Discharges Using Various Neural Network Models and Training Algorithms. KSCE Journal of Civil Engineering, 22(9): 3676–3685.
  • [21] Bor, A., and Okan, M. (2019). FIRAT HAVZASI karasu günlük akimlarinin yapay sinir ağlari ile modellenmesi. 10. Ulusal Hidroloji Kongresi, Muğla, Türkiye, Cilt 2. pp. 857-869.
  • [22] Fırat, M., and Dikbaş, F. (2006). Göllerde üç boyutlu hidrodinamik modellemede pom ve yapay sinir ağlari yöntemlerinin kullanilmasi : gökpinar baraj gölü örneği. Pamukkale University Engineering College Journal of Engineering Sciences, 12(1): 43–50.
  • [23] Coulibaly, P., Anctil, F., and Bobée, B. (1999). Prévision hydrologique par réseaux de neurones artificiels : état de l’art. Canadian Journal of Civil Engineering, 26: 293–304.
  • [24] Minns, A. W., and Hall, M. J. (1996). Artificial neural networks as rainfall-runoff models. Hydrological Sciences Journal, 41: 399–417.
  • [25] Gümüş, V., Başak, A., and Yenigün, K. (2018). Yapay Sinir Ağları ile Şanlıurfa İstasyonunun Kuraklığının Tahmini. Gazi Üniversitesi Fen Bilimleri Dergisi, 6(3): 621–633.
  • [26] Haykin, S. (1998). Neural Networks : A Comprehensive Foundation. Prentice-Hall. Upper Saddle River, NJ.
  • [27] Öztemel, E. (2006). Yapay Sinir Ağları. Papatya Publishing, Istanbul, Turkey.
  • [28] Şen, Z. (2004). Yapay Sinir Ağları İlkeleri. Turkish Water Foundation, Istanbul,Turkey.
  • [29] Chen, T. C., Han, D. J., Au, F. T. K., and Tham, L. G. (2003). Acceleration of Levenberg-Marquardt Training of Neural Networks with Variable Decay Rate. Proceedings of the International Joint Conference on Neural Networks, IEEE. pp. 1873–1878.
  • [30] Hagan, M. T., and Menhaj, M. B. (1994). Training Feedforward Networks with the Marquardt Algorithm. IEEE Transactions on Neural Networks, 5(6): 989–993.
  • [31] trainlm Levenberg-Marquardt backpropagation. https://www.mathworks.com/help/deeplearning/ref/trainlm.html [Accessed: 07 september 2023].
  • [32] Xu, M., Zeng, G., Xu, X., Huang, G., Jiang, R., and Sun, W. (2006). Application of Bayesian regularized BP neural network model for trend analysis, acidity and chemical composition of precipitation in North Carolina. Water, Air, and Soil Pollution, 172(1–4): 167–184.
  • [33] MacKay, D. J. C. (1992). Bayesian Interpolation. Neural Computation, 4(3): 415–447.
  • [34] Foresee, F. D., and Hagan, M. T. (1997). Gauss-Newton approximation to bayesian learning. IEEE International Conference on Neural Networks - Conference Proceedings, Houston, TX, USA, 9–12 June 1997. pp. 1930–1935.
  • [35] Kayri, M. (2016). Predictive abilities of Bayesian Regularization and Levenberg-Marquardt Algorithms in Artificial Neural Networks: A Comparative Empirical Study on Social Data. Mathematical and Computational Applications, 21(2).
  • [36] Gupta, H. V., Sorooshian, S., and Yapo, P. O. (1999). Status of automatic calibration for hydrologic models: comparison with multilevel expert calibration. Journal of Hydraulic Engineering, 4(2): 135–143.
  • [37] Moriasi, D. N., Arnold, J. G., Liew, M. W. Van, Bingner, R. L., Harmel, R. D., and Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. American Society of Agricultural and Biological Engineers, 50(3): 885–900.
  • [38] Nash, J. E., and Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I — A discussion of principles. Journal of Hydrology, 10(3): 282–290.
  • [39] Improve Shallow Neural Network Generalization and Avoid Overfitting. The MathWorks, Inc. https://www.mathworks.com/help/deeplearning/ug/improve-neural-network-generalization-and-avoid-overfitting.html [Accessed: 07 september 2023].

Comparison of Levenberg-Marquardt and Bayesian Regularization Learning Algorithms for Daily Runoff Forecasting

Year 2025, Volume: 9 Issue: 1, 62 - 77, 17.06.2025

Asli Bor , Merve Okan

Abstract

In this study, Multilayer Perceptron (MLP) with Levenberg-Marquardt and Bayesian Regularization algorithms machine learning methods are compared for modeling of the rainfall-runoff process. For this purpose, daily flows were forecast using 5844 discharge data monitored between 1999 and 2015 of D21A001 Kırkgöze gauging station on the Karasu River operated by DSI. 6 scenarios were developed during the studies. Our findings indicate that the estimated capability of the Bayesian Regularization algorithm were close to with Levenberg-Marquardt algorithm for training and testing, respectively. This study shows that different network structures and data representing land features can improve prediction for longer lead times. We consider that the ANN model accurately depicted the Karasu flows, and that our study will serve as a guide for more research on flooding and water storage.

Keywords

Discharge Forecasting Rainfall-runoff process Artificial Neural Network Euphrates-Tigris Basin

Thanks

The authors would like to thank the DSI (General Directorate of State Hydraulic Works), Department of Survey, Planning, and Allocations for the providing of the data.

References

  • [1] Tan Kesgin, R. I., Demir, I., Kesgin, E., Abdelkader, M., & Agaccioglu, H. (2023). A data-driven approach to predict hydrometeorological variability and fluctuations in lake water levels. Journal of Water and Land Development, (58), 158-170. https://doi.org/10.24425/jwld.2023.146608.
  • [2] Demirel, M. C., Özen, A., Orta, S., Toker, E., Demir, H. K., Ekmekcioğlu, Ö., Tayşi, H., Eruçar, S., Sağ, A. B., Sarı, Ö., Tuncer, E., Hancı, H., Özcan, T. İ., Erdem, H., Koşucu, M. M., Başakın, E. E., Ahmed, K., Anwar, A., Avcuoğlu, M. B., Vanlı, Ö., Stisen, S., Booij, M. J. (2019). Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration. Water, 11(10), 2083. https://doi.org/10.3390/w11102083.
  • [3] Shamseldin, A. Y. (1997). Application of a neural network technique to rainfall-runoff modelling. Journal of Hydrology, 199(3): 272–294.
  • [4] Tokar, S. A., and Johnson, P. A. (1999). Rainfall-Runoff Modeling Using Artificial Neural Networks. Journal of Hydrologic Engineering, 4(3): 232–239.
  • [5] Chang, F.-J., and Chen, Y.-C. (2001). A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction. Journal of Hydrology, 245: 153–164.
  • [6] Öztopal, A., Kahya, C., and Asilhan, S. (2001). Yapay Sinir Ağları ile Akış Tahmini. 1. Türkiye Su Kongresi, İstanbul, Türkiye, 8 - 10 Ocak 2001, cilt.1. pp. 311–318.
  • [7] Jayawardena, A. W., and Fernando, T. M. K. G. (2001). River flow prediction: An artificial neural network approach. Regional Management of Water Resources, Maastricht, The Netherlands. pp. 239–246.
  • [8] Sivakumar, B., Jayawardena, A., and Fernando, T. M. K. G. (2002). River Flow Forecasting: Use of Phase-Space Reconstruction and Artificial Neural Networks Approaches. Journal of Hydrology, 265: 225–245.
  • [9] Dorado, J., RabuñAL, J. R., Pazos, A., Rivero, D., Santos, A., and Puertas, J. (2003). Prediction and modeling of the rainfall-runoff transformation of a typical urban basin using ann and gp. Applied Artificial Intelligence, Taylor & Francis, 17(4): 329–343.
  • [10] Kişi, Ö. (2005). Daily River Flow Forecasting Using Artificial Neural Networks and Auto-Regressive Models. Turkish Journal of Engineering and Environmental Sciences, 29: 9–20.
  • [11] Demirpençe, H. (2005). Köprüçay Akımlarının Yapay Sinir Ağları ile Tahmini. Antalya Yöresinin İnşaat Mühendisleri Sorunları Kongresi.
  • [12] Yurdusev, M. A., Acı, M., Turan, M. E., and İçağa, Y. (2008). Akarçay Nehri Aylık Akımlarının Yapay Sinir Ağları ile Tahmini. Celal Bayar Üniversitesi Fen Bilimleri Dergisi, 4(1): 73–88.
  • [13] Okkan, U., and Mollamahmutoğlu, A. (2010). Çoruh Nehri Günlük Akımlarının Yapay Sinir Ağları ile Tahmin Edilmesi. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 14(3): 251–261.
  • [14] Okkan, U., and Dalkilic, H. Y. (2010). Demirköprü Barajı Aylık Buharlaşma Yüksekliklerinin Yapay Sinir Ağları ile Tahmin Edilmesi. DSİ Teknik Bülten, 108: 30–36.
  • [15] Chen, S. M., Wang, Y. M., and Tsou, I. (2013). Using artificial neural network approach for modelling rainfall-runoff due to typhoon. Journal of Earth System Science, 122(2): 399–405.
  • [16] Kızılaslan, M. A., Sağın, F., Doğan, E., and Sönmez, O. (2014). Aşağı Sakarya Nehri akımlarının yapay sinir ağları ile tahmin edilmesi. SAÜ Fen Bilimleri Dergisi, 18(2): 99–103.
  • [17] Singh, G., Panda, R. K., and Lamers, M. (2015). Modeling of daily runoff from a small agricultural watershed using artificial neural network with resampling techniques. Journal of Hydroinformatics, 17(1): 56–74.
  • [18] Khan, M. Y. A., Hasan, F., Panwar, S., and Chakrapani, G. J. (2016). Neural network model for discharge and water-level prediction for Ramganga River catchment of Ganga Basin, India. Hydrological Sciences Journal, Taylor & Francis, 61(11): 2084–2095.
  • [19] Altunkaynak, A., and Başakin, E. E. (2018). Zaman Serileri Kullanılarak Nehir Akım Tahmini ve Farklı Yöntemlerle Karşılaştırılması. Erzincan University Journal of Science and Technology, 11(1): 92–101.
  • [20] Nacar, S., Hınıs, M. A., and Kankal, M. (2018). Forecasting Daily Streamflow Discharges Using Various Neural Network Models and Training Algorithms. KSCE Journal of Civil Engineering, 22(9): 3676–3685.
  • [21] Bor, A., and Okan, M. (2019). FIRAT HAVZASI karasu günlük akimlarinin yapay sinir ağlari ile modellenmesi. 10. Ulusal Hidroloji Kongresi, Muğla, Türkiye, Cilt 2. pp. 857-869.
  • [22] Fırat, M., and Dikbaş, F. (2006). Göllerde üç boyutlu hidrodinamik modellemede pom ve yapay sinir ağlari yöntemlerinin kullanilmasi : gökpinar baraj gölü örneği. Pamukkale University Engineering College Journal of Engineering Sciences, 12(1): 43–50.
  • [23] Coulibaly, P., Anctil, F., and Bobée, B. (1999). Prévision hydrologique par réseaux de neurones artificiels : état de l’art. Canadian Journal of Civil Engineering, 26: 293–304.
  • [24] Minns, A. W., and Hall, M. J. (1996). Artificial neural networks as rainfall-runoff models. Hydrological Sciences Journal, 41: 399–417.
  • [25] Gümüş, V., Başak, A., and Yenigün, K. (2018). Yapay Sinir Ağları ile Şanlıurfa İstasyonunun Kuraklığının Tahmini. Gazi Üniversitesi Fen Bilimleri Dergisi, 6(3): 621–633.
  • [26] Haykin, S. (1998). Neural Networks : A Comprehensive Foundation. Prentice-Hall. Upper Saddle River, NJ.
  • [27] Öztemel, E. (2006). Yapay Sinir Ağları. Papatya Publishing, Istanbul, Turkey.
  • [28] Şen, Z. (2004). Yapay Sinir Ağları İlkeleri. Turkish Water Foundation, Istanbul,Turkey.
  • [29] Chen, T. C., Han, D. J., Au, F. T. K., and Tham, L. G. (2003). Acceleration of Levenberg-Marquardt Training of Neural Networks with Variable Decay Rate. Proceedings of the International Joint Conference on Neural Networks, IEEE. pp. 1873–1878.
  • [30] Hagan, M. T., and Menhaj, M. B. (1994). Training Feedforward Networks with the Marquardt Algorithm. IEEE Transactions on Neural Networks, 5(6): 989–993.
  • [31] trainlm Levenberg-Marquardt backpropagation. https://www.mathworks.com/help/deeplearning/ref/trainlm.html [Accessed: 07 september 2023].
  • [32] Xu, M., Zeng, G., Xu, X., Huang, G., Jiang, R., and Sun, W. (2006). Application of Bayesian regularized BP neural network model for trend analysis, acidity and chemical composition of precipitation in North Carolina. Water, Air, and Soil Pollution, 172(1–4): 167–184.
  • [33] MacKay, D. J. C. (1992). Bayesian Interpolation. Neural Computation, 4(3): 415–447.
  • [34] Foresee, F. D., and Hagan, M. T. (1997). Gauss-Newton approximation to bayesian learning. IEEE International Conference on Neural Networks - Conference Proceedings, Houston, TX, USA, 9–12 June 1997. pp. 1930–1935.
  • [35] Kayri, M. (2016). Predictive abilities of Bayesian Regularization and Levenberg-Marquardt Algorithms in Artificial Neural Networks: A Comparative Empirical Study on Social Data. Mathematical and Computational Applications, 21(2).
  • [36] Gupta, H. V., Sorooshian, S., and Yapo, P. O. (1999). Status of automatic calibration for hydrologic models: comparison with multilevel expert calibration. Journal of Hydraulic Engineering, 4(2): 135–143.
  • [37] Moriasi, D. N., Arnold, J. G., Liew, M. W. Van, Bingner, R. L., Harmel, R. D., and Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. American Society of Agricultural and Biological Engineers, 50(3): 885–900.
  • [38] Nash, J. E., and Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I — A discussion of principles. Journal of Hydrology, 10(3): 282–290.
  • [39] Improve Shallow Neural Network Generalization and Avoid Overfitting. The MathWorks, Inc. https://www.mathworks.com/help/deeplearning/ug/improve-neural-network-generalization-and-avoid-overfitting.html [Accessed: 07 september 2023].
There are 39 citations in total.

Details

Primary Language English
Subjects Numerical Modelization in Civil Engineering, Water Resources Engineering
Journal Section Research Articles
Authors

Asli Bor 0000-0002-1679-5130

Merve Okan 0000-0001-6095-2992

Early Pub Date May 19, 2025
Publication Date June 17, 2025
Submission Date October 14, 2023
Acceptance Date January 17, 2025
Published in Issue Year 2025Volume: 9 Issue: 1

Cite

APA Bor, A., & Okan, M. (2025). Comparison of Levenberg-Marquardt and Bayesian Regularization Learning Algorithms for Daily Runoff Forecasting. Journal of Innovative Science and Engineering, 9(1), 62-77. https://doi.org/10.38088/jise.1375510
AMA Bor A, Okan M. Comparison of Levenberg-Marquardt and Bayesian Regularization Learning Algorithms for Daily Runoff Forecasting. JISE. June 2025;9(1):62-77. doi:10.38088/jise.1375510
Chicago Bor, Asli, and Merve Okan. “Comparison of Levenberg-Marquardt and Bayesian Regularization Learning Algorithms for Daily Runoff Forecasting”. Journal of Innovative Science and Engineering 9, no. 1 (June 2025): 62-77. https://doi.org/10.38088/jise.1375510.
EndNote Bor A, Okan M (June 1, 2025) Comparison of Levenberg-Marquardt and Bayesian Regularization Learning Algorithms for Daily Runoff Forecasting. Journal of Innovative Science and Engineering 9 1 62–77.
IEEE A. Bor and M. Okan, “Comparison of Levenberg-Marquardt and Bayesian Regularization Learning Algorithms for Daily Runoff Forecasting”, JISE, vol. 9, no. 1, pp. 62–77, 2025, doi: 10.38088/jise.1375510.
ISNAD Bor, Asli - Okan, Merve. “Comparison of Levenberg-Marquardt and Bayesian Regularization Learning Algorithms for Daily Runoff Forecasting”. Journal of Innovative Science and Engineering 9/1 (June 2025), 62-77. https://doi.org/10.38088/jise.1375510.
JAMA Bor A, Okan M. Comparison of Levenberg-Marquardt and Bayesian Regularization Learning Algorithms for Daily Runoff Forecasting. JISE. 2025;9:62–77.
MLA Bor, Asli and Merve Okan. “Comparison of Levenberg-Marquardt and Bayesian Regularization Learning Algorithms for Daily Runoff Forecasting”. Journal of Innovative Science and Engineering, vol. 9, no. 1, 2025, pp. 62-77, doi:10.38088/jise.1375510.
Vancouver Bor A, Okan M. Comparison of Levenberg-Marquardt and Bayesian Regularization Learning Algorithms for Daily Runoff Forecasting. JISE. 2025;9(1):62-77.
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