مقایسه کارایی روش‌های شبکه عصبی مصنوعی و برنامه‌ریزی بیان ژن برای پیش بینی سطح ایستابی در مناطق خشک و نیمه خشک ( مطالعه موردی: دشت جیرفت)

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مدیریت و کنترل بیابان، گروه احیاء مناطق خشک و کوهستانی، دانشکده منابع طبیعی، دانشگاه تهران، کرج، ایران

2 دانشیار، گروه احیاء مناطق خشک و کوهستانی، دانشکده منابع طبیعی، دانشگاه تهران، کرج، ایران

چکیده

مدل‌سازی و پیش‌بینی سطح ایستابی چاه‌ها یکی از کار‌های اساسی برایرسیدن به مدیریت بهینه منابع آب می‌باشد. یکی از راه‌های پیش‌بینی سطح آب زیرزمینی استفاده از تکنیک‌های هوش مصنوعی نظیر شبکه عصبی مصنوعی و برنامه‌ریزی بیان ژن می‌باشد. هدف از این پژوهش بررسی کارایی روش شبکه عصبی مصنوعی و برنامه‌ریزی بیان ژن در پیش‌بینی سطح ایستابی آب زیرزمینی آبخوان دشت جیرفت می‌باشد. به این منظور از داده‌های سطح ایستابی 65 چاه موجود در آبخوان دشت جیرفت برای یک دوره یازده ساله استفاده شد. سطح ایستابی چاه‌ها توسط هر یک از تکنیک‌های شبکه عصبی و برنامه‌ریزی بیان ژن به‌طور جداگانه شبیه‌سازی شد و در انتها از آماره‌های ریشه میانگین مربعات خطا، میانگین مطلق خطا، شاخص تطابق و R2 برای تعیین دقت پیش‌بینی هر کدام از روش‌ها استفاده شد. نتایج این پژوهش کارایی و دقت بالای هر دو تکنیک شبکه عصبی و برنامه‌ریزی بیان ژن را در پیش‌بینی سطح ایستابی چاه‌های منطقه نشان داد. ضریب همبستگی در روش شبکه عصبی مصنوعی برابر با 96/0 و در روش برنامه‌ریزی بیان ژن برابر با 72/0 شد که نشان دهنده این است روش شبکه عصبی مصنوعی در این تحقیق دقت بالاتری را در پراکنش داده‌های دشت جیرفت طی سال های 1381-1391 دارا می‌باشد.
 

کلیدواژه‌ها


عنوان مقاله [English]

Comparison of the performance of Artificial Neural Networks and Gene Expression to predict the groundwater level in arid and semi-arid areas (Case study: Jiroft plain)

نویسندگان [English]

  • Bahareh Jebalbarezi 1
  • Arash Malekian 2
1 Ph.D. Student in Desert Management, Department of Rehabilitation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran
2 Associate Professor, Department of Rehabilitation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran
چکیده [English]

    Modeling and prediction of groundwater level is one of the basic tasks to achieve optimal management of water resources. One way to predict the groundwater level is using artificial intelligence techniques such as neural networks and gene expression planning. The aim of this study was to evaluate the effectiveness of artificial neural network (ANN) and gene expression methods in predicting groundwater level of Jiroft plain aquifer. For this purpose, the data from 65 piezometric wells in the Jiroft plain aquifer was used for a period of eleven years. The level of piezometric wells by each of the techniques of gene expression and neural network were simulated separately and at the end, the root mean square, mean absolute error, and R2 were used to determine the accuracy of the predictions of each of the methods.The results of this study showed the higher efficiency and accuracy of both neural network techniques and gene expression in predicting the groundwater level region. The correlation coefficient in the artificial neural network method gene expression method was equal to 0.96 and 0.72, respectively, indicating the higher efficiency of artificial neural network in the simulation of Jiroft plain groundwater data over the period studied.

کلیدواژه‌ها [English]

  • Artificial Neural Networks
  • Groundwater table
  • Gene expression
  • Jiroft Plain

-Aalami, M., Sadeghfam, S., Fazelifard, M. and Taghipour., 2013. Modeling series of data, Tabriz University Press, 301p.

-Afkhami, H., Ekhtesasi, M. R. and Mohammadi, M., 2015. The effect of processing the input variables of the standard rainfall index on prediction of droughts in artificial neural networks using wavelet transform, Iranian Journal of Rangeland and Desert Research, 22(3): 570-582.

-Ahmadian, M., Chavoshian, M. and Darvish, M., 2015. Investigating the fluctuations of groundwater table level as a criterion for land degradation in semi arid regions using ground-level technique. Iranian Journal of Rangeland and Desert Research, 22(1): 109-120.

-Chung, Y. W., 2008.Prediction water table fluctuation using artificial neural network, in partial fulfillment of the requirements for the degree of doctor of philosophy, University of Maryland, 185 pp.

-Coppola, E., Rana, A. J., Poulton, M., Szidarovszky, F. and Uhi, V. W., 2005. Aneural networks model for predicting aquifer water level elevation ground water. Journal of Ground water, 43: 231-241.

-Ferreira, C., 2005. Gene expression programming: A new adaptive algorithm for solving problems. Journal of Complex Systems, 13 (2): 87-129.

-Ghezelbash, Z., Zakerinia, M., Hezar Jaribi, A. and Dehghani, A., 2014. Comparison of the performance of two methods of gene expression planning and artificial neural network in order to estimate the uniformity coefficient of water distribution in rain irrigation. Journal of Water and Soil Conservation Research, 21(6): 95-114.

-Ghobadian, R., Ghorbani, M. and Khalaj, M., 2013. Investigating the function of gene expression planning method in Zangmar province river flow recovery compared to dynamic wave. Journal of Water and Soil Research, 27(3): 592-602.

-Ghorbani Dashtaki, S., Homaee, M., Mahdian, M. H. and Kouchakzadeh, M., 2009. Site-dependence performance of infiltration models. Journal of Water Resource Manage, 23: 2777-2790.

-Ghorbani, M. and Salehi, A., 2011. Application of gene expression planning in investigating the changes in groundwater quality data with fluctuations in the area of Isfahan Borkhar Plain. Sixth National Congress on Civil Engineering. Semnan University, Semnan, Iran, 11p.

-Ghorbani, M. A., Khatibi, R., Hasanpour kashani, M. and Kisi, O., 2010. Comparison of three artificial intelligence techniques for discharge routing. Journal of Hydrology, 403(3-4): 201-212.

-Gorgij, A. D., Kisi, O. and Moghaddam, A., 2016. Groundwater budget forecasting using hybrid wavelet-ANN-GP modelling: a case study of Azarshahr plain, East Azerbaijan, Iran. Journal of Hydrology Research, nh2016202.

-Jamalizadeh Taj Abadi, M. R., Moghaddam Nia, A. R., Piri, J. and Ekhtesasi, M. R., 2010. Prediction of dust storm occurrence using Artificial Neural Networks, case study: Zabol city. Iranian Journal of Rangeland and Desert Research, 17(2): 205-220.

-Johari, A. and Nejad, A. H., 2015. Prediction of soil-water characteristic curve using gene expression programming. Iranian Journal of Science and Technology. Transactions of Civil Engineering, 39(C1): 143.

-Kavehkar, S. H., Ghorbani, M., Ashrafzadeh, A. and Darbandi, S., 2013. Simulation of water balance fluctuations using gene expression planning. Civil Engineering and Environment Journal, 3(79): 69-75.

-Menhaj, M. B., 2005. Fundamentals of neural networks, computational intelligence. Publishing Center of Amir Kabir University of Technology, Iran, 718p.

-Mirzaee, A. and Nazemi, A. H., 2011. Estimation of level of level of the station using intelligent systems (Case study: Shabestar plain).Journal of Water Resource Engineering, 4(8): 10.

-Pour Mohammadi, S., Maleki Nezhad, H. and Pour Shariyati, R., 2013. Comparison of the efficiency of neural network techniques and time series in groundwater forecasting (Case study: Bakhtegan watershed, Fars province). Journal of Water and Soil Conservation Research, 20(4): 251-262.

-Pour Seyedi, A. and Kashkouli, H., 2012. Study of the submarine waters of Jiroft plain using PMWIN model. Journal of Irrigation Science and Engineering, 35(2): 51-63.

-Rahmani, G. H., 2014. Simulation of groundwater resources of Aghili plain using artificial neural networks and its comparison with the results of the finite-difference mathematical model, M.Sc. thesis, Shahid Chamran University of Ahvaz, 138p.

-Zamani, A. and Mahmoudi, R., 2012. Investigating the application of combined ground statistics and neural networks optimized by genetic algorithm in the interplantation of Groundwater Levels of the Plain. M.Sc. thesis, Shahid Chamran University of Ahvaz, 152p.