FIRAT HAVZASI KARASU NEHRİNDEKİ AYLIK AKIMLARIN YAPAY SİNİR AĞLARI YAKLAŞIMINI İLE TAHMİNİ

Year 2022, , 917 - 928, 30.09.2022

Okan Mert Katipoğlu

https://doi.org/10.21923/jesd.982868

Cited By: 8

Abstract

Bu çalışma çeşitli meteorolojik parametreler kullanarak yapay sinir ağları (ANN) ile akım değerlerinin tahmin edilmesini amaçlanmaktadır. ANN modelinin geliştirilmesinde yağış, hava sıcaklıkları ve potansiyel evapotranspirasyon değerlerinin çeşitli kombinasyonları girdi olarak kullanılmış ve akım değerleri elde edilmiştir. Modelin tasarlanmasında çeşitli eğitim algoritmaları, ağ mimarisi, girdi kombinasyonları ve isterasyon sayıları denenerek en uygun model sınanmıştır. Korelasyon katsayısı (R), belirlilik katsayısı (R2), mutlak hata (AE) ve göreceli mutlak hata (ARE) katsayıları karşılaştırılarak en uygun model seçilmiştir. Analiz sonuçlarına göre 2000 iterasyon, 4-4-1 modelinin mimarisi ve Quasi-Newton algoritması kullanılarak optimal model elde edilmiştir. ANN modellerinin yağış-akış ilişkisini modellemede ve güvenilir tahminler üretmede başarılı olduğu tespit edilmiştir. Ayrıca Thornthwaite yöntemi ile elde edilen potansiyel evapotranspirasyon değerlerinin modele dâhil edilmesinin modelin başarısını arttırdığı ortaya konmuştur.

Keywords

Yapay Sinir Ağı, Yağış-Akış İlişkisi, Hava sıcaklığı, Potansiyel Evapotranspirasyon, Karasu Aşağıkağdariç

MONTHLY STREAM FLOWS ESTIMATION IN THE KARASU RIVER OF EUPHRATES BASIN WITH ARTIFICIAL NEURAL NETWORKS APPROACH

Abstract

This study aims to estimate streamflow values with artificial neural networks (ANN) using various meteorological parameters. In developing the ANN model, various combinations of precipitation, air temperatures, and potential evapotranspiration values were used as inputs, and streamflow values were obtained. Meteorological data is divided into 70% train, 15% test, and 15% validation. In the model's design, various training algorithms, network architecture, input combinations, and the number of iterations were tried, and the most suitable model was tested. Correlation coefficient (R), coefficient of determination (R2), absolute error (AE), and absolute relative error (ARE) coefficients were compared, and the most suitable model was selected. According to the analysis results, the optimal model was obtained using 2000 iterations, the architecture of the 4-4-1 model, and the Quasi-Newton algorithm. It was determined that the ANNs successfully modeled the rainfall-runoff relationship and produced reliable estimates. In addition, it was revealed that the inclusion of potential evapotranspiration values obtained by the Thornthwaite method into the model increases the model's success.

Keywords

Artificial Neural Network, Rainfall-Runoff Relationship, Air Temperature, Potential Evapotranspiration, Karasu Aşağıkağdariç

References

• Adnan, R. M., Liang, Z., Parmar, K. S., Soni, K., and Kisi, O., 2021. Modeling monthly streamflow in mountainous basin by MARS, GMDH-NN and DENFIS using hydroclimatic data. Neural Computing and Applications, 33(7):2853-2871.
• Agatonovic-Kustrin, S., and Beresford, R. 2000. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. Journal of pharmaceutical and biomedical analysis, 22(5):717-727.
• Ahmad, S., Simonovic, S. P., 2005. An artificial neural network model for generating hydrograph from hydro-meteorological parameters. Journal of Hydrology, 315(1-4):236-251.
• Anilan, T., Nacar, S., Kankal, M., and Yuksek, O. 2020., Prediction of maximum annual flood discharges using artificial neural network approaches. Građevinar, 72(03.):215-224. Asadi, H., Shahedi, K., Jarihani, B., and Sidle, R. C., 2019. Rainfall-runoff modelling using hydrological connectivity index and artificial neural network approach. Water, 11(2):212.
• Asadi, H., Shahedi, K., Jarihani, B., and Sidle, R. C., 2019. Rainfall-runoff modelling using hydrological connectivity index and artificial neural network approach. Water, 11(2):212.
• Aytek, A., Guven, A., Yuce, M. I., Aksoy, H., 2008. An explicit neural network formulation for evapotranspiration. Hydrological sciences journal, 53(4):893-904.
• Banadkooki, F. B., Singh, V. P., and Ehteram, M., 2021. Multi-timescale drought prediction using new hybrid artificial neural network models. Natural Hazards, 106(3):2461-2478.
• Bayazıt, M., 1995. Hidroloji. Istanbul Technical University.
• Bayazit, M., 1998. Hydrological models. ITU Faculty of Civil Engineering, Istanbul.
• Bölük, O., 2020. Prediction of rainfall-runoff relationship using artificial intelligence techniques, Iskenderun Technical University Engineering and Science Instiute, M. Sc. Thesis, Iskenderun.
• Campolo, M., Soldati, A., Andreussi, P., 2003. Artificial neural network approach to flood forecasting in the River Arno. Hydrological Sciences Journal, 48(3):381-398.
• Damla, Y., Temiz, T., and Keskin, T. 2020., Estimation of Water Level by Using Artificial Neural Network: Example of Yalova Gökçe Dam. Kırklareli University Journal of Engineering and Science 6-1:32-49.
• Dawson, C. W., Wilby, R., 1998. An artificial neural network approach to rainfall-runoff modelling. Hydrological Sciences Journal, 43(1):47-66.
• Dawson, C.W., Abrahart, R.J., Shamseldin, A.Y., Wilby, R.L., 2006. Flood estimation at ungauged sites using artificial neural networks. Journal of hydrology, 319(1-4):391-409.
• Dounia, M., Yassine, D. Yahia, H., 2016. Calibrating Conceptual Rainfall Runoff Models using Artificial Intelligence. Journal of Environmental Science and Technology, 9(3):257.
• Elbeltagi, A., Nagy, A., Mohammed, S., Pande, C. B., Kumar, M., Bhat, S. A., ... and Juhász, C., 2022. Combination of Limited Meteorological Data for Predicting Reference Crop Evapotranspiration Using Artificial Neural Network Method. Agronomy, 12(2):516.
• Elshorbagy, A., Corzo, G., Srinivasulu, S., Solomatine, D., 2010. Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology part 1: concepts and methodology. Hydrol. Earth Syst. Sci. 14(10):1931–1941
• Guimaraes Santos, C. A., Silva, G. B. L. D., 2014. Daily streamflow forecasting using a wavelet transform and artificial neural network hybrid models. Hydrological Sciences Journal, 59(2):312-324.
• Hall, M. Minns, A., 1998. Regional flood frequency analysis using artificial neural network. Ed. Hydroinformatics Conference, Copenhagen.
• Haykin, S., 1994. Neural networks: a comprehensive foundation. Prentice Hall PTR.
• Haykin, S., 1999. Neural networks: a comprehensive foundation, Prentice-Hall, 2nd Ed, Upper Saddle River, New Jersey
• Ilie, C., Ilie, M., Melnic, L., Topalu, A. M., 2012. Estimating the Romanian Economic Sentiment Indicator Using Artificial Intelligence Techniques. Journal of Eastern Europe Research in Business & Economics.
• Jimeno-Sáez, P., Senent-Aparicio, J., Pérez-Sánchez, J., Pulido-Velazquez, D., 2018. A Comparison of SWAT and ANN Models for Daily Runoff Simulation in Different Climatic Zones of Peninsular Spain, 10(2):192.
• Katipoğlu, O. M., 2020. Analysis of meteorological and hydrological droughts in Euphrates river valley, Doctoral Dissertation. Atatürk University Institute of Science, Erzurum.
• Khan, M. Y. A., Hasan, F., and Tian, F., 2019. Estimation of suspended sediment load using three neural network algorithms in Ramganga River catchment of Ganga Basin, India. Sustainable Water Resources Management, 5(3):1115-1131.
• Khan, M. Y. A., Hasan, F., Panwar, S., Chakrapani, G. J., 2016. Neural network model for discharge and water-level prediction for Ramganga River catchment of Ganga Basin, India, 61(11):2084-2095.
• Krenker, A., Bešter, J., and Kos, A., 2011. Introduction to the artificial neural networks. Artificial Neural Networks: Methodological Advances and Biomedical Applications. InTech, 1-18.
• Krse, B., van der Smagt, P., 1996. An Introduction to Neural Networks, 8th edn. The University of Amsterdam, Amsterdam
• Kumar, D., Sarthı, P. P., Ranjan, P., 2016. Rainfall-runoff modeling using computational intelligence techniques, ed. Advances in Computing, Communications and Informatics (ICACCI), 2016 International Conference on, Jaipur, India.
• Lange, N., 1998. Advantages of unit hydrograph derivation by neural networks. Ed. Hydroinformatics Conference, Copenhagen.
• Lee, S., Cho, S., Wong, P. M., 1998. Rainfall prediction using artificial neural networks, Journal of geographic information and Decision Analysis, 2(2):233-242.
• Machado, F., Mine M., Kaviski, E., Fill, H., 2011. Monthly rainfall–runoff modelling using artificial neural networks. Hydrological Sciences Journal, 56(3):349-361.
• Malik, A., and Bhagwat, A. 2021. Modelling groundwater level fluctuations in urban areas using artificial neural network. Groundwater for Sustainable Development, 12, 100484.
• Meshram, S. G., Singh, V. P., Kisi, O., Karimi, V., and Meshram, C., 2020. Application of artificial neural networks, support vector machine and multiple model-ANN to sediment yield prediction. Water Resources Management, 34(15):4561-4575.
• Mirabbasi, R., Kisi, O., Sanikhani, H., and Gajbhiye Meshram, S., 2019. Monthly long-term rainfall estimation in Central India using M5Tree, MARS, LSSVR, ANN and GEP models. Neural Computing and Applications, 31(10):6843-6862.
• 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.
• Niu, W. J., Feng, Z. K., Feng, B. F., Min, Y. W., Cheng, C. T., Zhou, J. Z., 2019. Comparison of Multiple Linear Regression, Artificial Neural Network, Extreme Learning Machine, and Support Vector Machine in Deriving Operation Rule of Hydropower Reservoir. Water, 11(1):88.
• Ochoa-Rivera, J. C., 2008. Prospecting droughts with stochastic artificial neural networks. Journal of hydrology, 352(1-2):174-180.
• Okkan, U., Mollamahmutoğlu, A., 2010. Daily Runoff Prediction of Coruh River by Artificial Neural Networks. Journal of Natural & Applied Sciences, 14(3).
• Ozan Evkaya, O., and Sevinç Kurnaz, F., 2021. Forecasting drought using neural network approaches with transformed time series data. Journal of Applied Statistics, 48(13-15):2591-2606.
• Ozigis, I. I., Adeyemi, R. A., Ondachi, P. A., and Oodo, S. O., 2021. Performance evaluation of Kainji hydro-electric power plant using artificial neural networks and multiple linear regression. International Journal of Energy and Water Resources, 1-11.
• Qasem, S. N., Samadianfard, S., Kheshtgar, S., Jarhan, S., Kisi, O., Shamshirband, S., and Chau, K. W. (2019). Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates. Engineering Applications of Computational Fluid Mechanics, 13(1):177-187.
• Samantaray, S., and Sahoo, A., 2021. Modelling response of infiltration loss toward water table depth using RBFN, RNN, ANFIS techniques. International Journal of Knowledge-based and Intelligent Engineering Systems, 25(2), 227-234.
• Samantaray, S., Sahoo, A., and Agnihotri, A., 2021. Assessment of Flood Frequency using Statistical and Hybrid Neural Network Method: Mahanadi River Basin, India. Journal of the Geological Society of India, 97(8), 867-880.
• Sert, M., Opan, M. ve Temiz, T. 2007. Çoklu Rezervuar Sistemlerinde Çok Amaçlı Optimal Planlama. Uluslararası Nehir Havzası Yönetimi Kongresi, 554-567.
• Shi, B., Hu, C. H., Yu, X. H., Hu, X. X., 2016. New fuzzy neural network–Markov model and application in mid-to long-term runoff forecast. Hydrological Sciences Journal, 61(6):1157-1169.
• Shin, H. S., Salas, J. D., 2000. Regional drought analysis based on neural networks, Journal of Hydrologic Engineering. 5(2):145-155.
• Sihag, P., Kumar, M., and Singh, B., 2021. Assessment of infiltration models developed using soft computing techniques. Geology, Ecology, and Landscapes, 5(4):241-251.
• Singh, B., Sihag, P., Parsaie, A., and Angelaki, A., 2021. Comparative analysis of artificial intelligence techniques for the prediction of infiltration process. Geology, Ecology, and Landscapes, 5(2):109-118.
• Temiz, T., Damla, Y., and Keskin, T. 2021., Gökçe Dam Chamber Water Level Estimation with Gdm Algorithm. Yalova Research Congress Proceedings Book, October 22-23:136-141.
• Thornthwaite, C. W., 1948. An approach toward a rational classification of climate. Geographical review, 38(1):55-94.
• Tikhamarine, Y., Malik, A., Kumar, A., Souag-Gamane, D., and Kisi, O., 2019. Estimation of monthly reference evapotranspiration using novel hybrid machine learning approaches. Hydrological sciences journal, 64(15):1824-1842.
• Tombul, M., Oğul, E., 2006. Modeling of rainfall-runoff relationship at the semi-arid small catchments using artificial neural networks. Intelligent Control and Automation. Springer, 309-318.
• Uzlu, E., Akpınar, A., Özturk, H. T., Nacar, S., Kankal, M., 2014. Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey. Energy, 69:638-647.
• Vyas, S. K., Mathur, Y. P., Sharma, G., Chandwani, V., 2016. Rainfall-Runoff Modelling: Conventional regression and Artificial Neural Networks approach. Ed. Recent Advances and Innovations in Engineering (ICRAIE), 2016 International Conference on, Jaipur, India.
• Wagena, M. B., Goering, D., Collick, A. S., Bock, E., Fuka, D. R., Buda, A., and Easton, Z. M., 2020. Comparison of short-term streamflow forecasting using stochastic time series, neural networks, process-based, and Bayesian models. Environmental Modelling & Software, 126:104669.
• Wang, Y. Guo, S., Xiong, L., Liu, P., Liu, D., 2015. Daily runoff forecasting model based on ANN and data preprocessing techniques. Water, 7(8):4144-4160.
• Wunsch, A., Liesch, T., and Broda, S., 2021. Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and nonlinear autoregressive networks with exogenous input (NARX). Hydrology and Earth System Sciences, 25(3):1671-1687.
• Yilmaz, A., Imteaz, M., and Jenkıns, G., 2011. Catchment flow estimation using Artificial Neural Networks in the mountainous Euphrates Basin. Journal of Hydrology, 410(1-2):134-140.
• Young, C. C., Liu, W. C., 2015. Prediction and modelling of rainfall–runoff during typhoon events using a physically-based and artificial neural network hybrid model. Hydrological Sciences Journal, 60(12):2102-2116.
• Zemzami, M., Benaabidate, L., 2016. Improvement of artificial neural networks to predict Daily streamflow in a semi-arid area. Hydrological Sciences Journal, 61(10):1801-1812.