Predicting the groundwater level of Chahardoli aquifer in Qorveh city using artificial neural network and support vector machine

Authors

1 M. Sc., of Water Engineering, Islamic Azad University, Sanandaj Branch, Sanandaj, Iran

2 Assist. Prof., Dept., of Water Engineering, Islamic Azad University, Sanandaj Branch, Sanandaj, Iran

Abstract

Estimating water level is one of the most important and basic issues in agricultural planning, water resources management and determining the water needs of plants. In this study, the efficiency of artificial neural network models and support vector machines in estimating the groundwater level of Chahardoli aquifer in Qorveh city was investigated. To run the models, from the data of precipitation, flow and temperature and water level level in the previous month as input variables and water level in the next month as output variable on a monthly time scale during the statistical period (2006-2017) was used. The criteria of correlation coefficient, root mean square error and mean absolute error value were used to evaluate and also compare the performance of the models. For modeling with the mentioned methods, training data from 2006-2015 and model validation data from 2015-2016 were used. The results showed that both models had acceptable accuracy in estimating the water table level. So that the coefficient of determination in the calibration stage in the models of artificial neural network and support vector machine were equal to 0.74 and 0.94. Comparison of the two models showed that the support vector machine model performs better than the artificial neural network and the prediction accuracy has been decreased for one year in this model.

Keywords

References

گویلی، س.، جوادی، س.، ابراهیم بنی حبیب، م.، ثانی خانی، ه (1397) مقایسه مدل­های هوشمند در پیش­بینی نوسانات تراز سطح آب دریاچه زریوار با در نظرگیری تراز آب زیرزمینی. مجله تحقیقات منابع آب ایران، سال چهاردهم. شماره 3 (پیاپی 45)، ص 339-344.
میثاقی، ف.، محمدی، ک (1385) پهنه­بندی اطلاعات بارندگی با استفاده از روش­های آماری کلاسیک و زمین­آمار و مقایسه با شبکه­های عصبی مصنوعی. مجله علمی کشاورزی، دوره 29، شماره 3، ص 1-13.
آخونی­پور حسینی، ف.، اسدی، ا (1396) کاربرد شبکه بیزین و مدل ماشین­بردار پشتیبان در پیش­بینی تغییرات سطح تراز ایستابی (مطالعه موردی: دشت اردبیل)، دوره 11، شماره 36، ص 33-42.
بایزیـدی، م.، کاکـی، م (1400) تغـییرات حـجم ذخـیره و بهره‏­برداری از آبخوان­‏های دشت­ های شرق استان کردستان. مجله اکو هیدرولوژی، دوره 6، شماره 1، ص 57-72.
جلالی، م.، کمانگر، م.، رزمی، ر (1398) پیش‌بینی مدل مکانی سطح ایستابی با استفاده از تابع هایپربولیک تانژانت شبکه­ی عصبی مطالعه­ی موردی: دشت سرخون. مجله هیدروژئومورفولوژی، دوره 6، شماره 20، ص 101-119.
Ahmadi, F., Radmanesh, F., Mirabasi najafabadi, R (2014) Comparison of genetic algorithm and support vector machine to predict daily river flow (Case Study: BarandozChay River). Journal of Soil and Water (Food Science and Industry), 28(6): 1171-1116.
Nash, JE., Sutcliffe, JV (1970) River flow forecasting through conceptual models. Part 1 – a discussion of principles. Journal Hydrology, 192: 211–232.
Kaveh, A (2004) Structural Mechanics: Graph and Matrix Methods, Research Studies Press (John Wiley), Exeter, U.K., 1992 (first edition), 1995 (second edition), 2004 (third edition), 100p.
Suykens, J. A. K., Gestel, T. V., Brabanter, J. D., Moor, B. D., Vandewalle, J (2002) Least Squares Support Vector Machines. Copyright by World Scientific Publishing Co. Pie. Ltd. 58: 72-75, 98-99.
Yu, P. S., Chen, S. T., Chang, I. F (2006) Support vector regression for real-time flood stage forecasting. Journal of Hydrology, 328: 704-716.
Mirzavand, M., Ghazavi, R (2014) A Stochastic Modelling Technique for Groundwater Level Forecasting in an Arid Environment Using Time Series Methods. Water Resources Manage, 29: 1315-1328.
Porte, P., Kumar Isaac, R., Kiran Singh Mahilang, K., Sonboier, K., Minj, P (2018) Groundwater Level Prediction Using Artificial Neural Network Model. International Journal of Current Microbiology and Applied Sciences, 2(7): 2319-7706.
Daliakopoulos, I. N., Coulibaly, P. and Tsanis, I. K (2005) Groundwater level forecasting using artificial neural networks. Journal of Hydrology, 309(4): 229-240.
He, Zh., Wen, X., Liu, H., Du, J (2014) A comparative study of artificial Neural Network, adaptive Neuro Fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region. Journal of Hydrology, 509: 379-386.
Barzegar, R., Fijani, E., Asghari Moghaddam, A., Tziritis, E (2017) Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models. Science of the Total Environment, 599: 20-31.
Sarangi, A., Bhattacharya, A. K (2005) Comparison of Artificial Neural Network and Regression Modelsfor Sediment Loss Prediction from Banha Watershed in India. Agricultural water management, 28(4): 373-385.
Solaimani, K (2009) Rainfall-Runoff Prediction Based on Artificial Neural Network (A Case Study: Jarahi Watershed). American-Eurasian Journal of Agriculture and Environment, Science, 5(6): 856-865.
Dehghani, A. A., Asgari, M., Mosaedi, A (2009) Comparison of Geostatistics, Artifitial Neural Networks and Adaptive Neuro-Fuzzy Inference System Approaches in Groundwater Level Interpolation (Case study: Ghazvin aquifer). Journal of Agriculture Science Natural Resource,16 (1): 517-528.

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History

  • Receive Date: 28 March 2021
  • Revise Date: 26 July 2021
  • Accept Date: 27 July 2021

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