ارزیابی مدل برنامه ریزی بیان ژن برای برآورد بار رسوب معلق بر اساس پیش پردازش داده ها با روش آزمون گاما (مطالعه موردی: حوزه آبخیز رود زرد)
محورهای موضوعی : مدیریت آب در مزرعه با هدف بهبود شاخص های مدیریتی آبیاریعادله علی جانپور شلمانی 1 , علی رضا واعظی 2 , محمودرضا طباطبایی 3
1 - گروه علوم خاک، دانشگاه زنجان
2 - گروه علوم خاک-دانشکده کشاورزی-دانشگاه زنجان
3 - استادیار، پژوهشکده حفاظت خاک و آبخیزداری، سازمان تحقیقات، آموزش و ترویج کشاورزی،
کلید واژه: آزمون گاما, برنامه‌ریزی بیان ژن, حوزه‌ی آبخیز, رسوب,
چکیده مقاله :
ی. در این پژوهش، داده‌های ایستگاه هیدرومتری ماشین در حوزه آبخیز رود زرد، با طول دوره آماری 36 سال (1356-1391) مورد استفاده قرار گرفت. متغیرهای ورودی به مدل GEP شامل دبی لحظه‌ای (Q)، متوسط دبی روزانه (Qi) و متوسط بارندگی روزانه (Pi) به همراه سه گام تأخیر زمانی و متغیر خروجی به مدل شامل بار رسوب معلق روزانه می-باشد. برای کاهش در وقت و هزینه، پیش‌پردازش داده‌های ورودی به مدل GEP با استفاده از روش آزمون گاما به‌دست آمد و به‌همراه ترکیبات بدون پیش‌پردازش (آزمون و خطا) وارد مدل GEP شد. نتایج مقایسه بین تمامی مدل‌ها نشان داد که برترین ترکیب متغیر ورودی حاصل از آزمون گاما، با کمترین مقدار آمارهی خطای استاندارد برابر صفر، آمارهی گاما برابر 000092/0 و آمارهی Vratio برابر 012/0 و با ترکیب متغیرهای متوسط دبی روزانه به همراه دو گام تأخیر زمانی و متوسط بارندگی روزانه به همراه سه گام تأخیر زمانی، دقیق‌ترین و صحیح‌ترین برآورد را برای بار رسوب معلق داشت. این مدل دارای کمترین مقدار ton/day)) 90/1671RMSE= و ton/day)) 68/475MAE= و بیشترین مقدار 99/0R2= و 99/0NSE= در مقایسه با سایر مدل‌ها بود. بنابراین، استفاده از روش آزمون گاما به‌عنوان یک روش پیش‌پردازش داده‌ها توانست با انتخاب ترکیباتی از متغیرهای ورودی مناسب به مدل‌ها، به‌طور میانگین تا 40 درصد مقدار خطای برآورد (RMSE) بار رسوب معلق روزانه را در مقایسه با ترکیبات ورودی حاصل از آزمون و خطا کاهش دهد و با افزایش تشابه بین مقادیر داده-های مشاهداتی با داده‌های محاسباتی، عملکرد مدل GEP در برآورد بار رسوب معلق را افزایش دهد.
In this research, the data of the machine hydrometric station was used in the Rood Zard watershed with a statistical period of 36 years (1977-2012). In order to reduce time and cost, pre-processing of input data into the GEP model was obtained using gamma test method and entered the GEP model along with non-preprocessing combinations of the test and error method. The results of comparison between all models showed that the best combination of input variable from gamma test with the lowest standard error is zero, gamma statistic is 0.000092 and Vratio statistic is 0.012 and the combination of variables including average daily flow discharge with two steps of time delay and average daily precipitation with three steps of time delay, had the most accurate and correct estimate for suspended sediment load. This model had the lowest value of RMSE=1671.90 (ton/day) and MAE=475.68 (ton/day) and the highest value of R2=0.99 and NSE=0.99 compared to other models. Therefore, the use of gamma test method as a data preprocessing method, by selecting combinations of appropriate input variables to models, an average of up to 40% of the estimated error (RMSE) of daily suspended sediment load compared to the inputs from the test and reduce the error and increase the performance of the GEP model in estimating the suspended sediment load by increasing the similarity between the values of observational data with computational data.
Angabini, S., Ahmadi, H., Feizni, S., Motamed Vaziri, B. and Ershadi, S. 2014. Suspended Sediment Concentration Estimation using Artificial Neural Networks and Fuzzy Rule Base Model Case Study: Jagin Dam. Journal of Applied Sciences Research. 10(14):12-17.
Azamathulla, H. 2013. Gene-expression programming to predict friction factor for Southern Italian Rivers. Neural Computing and Applications. 23:1421–1426.
Bagatur, T. and Onen, F. 2014. A predictive model on air entrainment by plunging water jets using GEP and ANN. KSCE Journal of Civil Engineering. 18(1): 304–314.
Barzegari, F., Yosefi, M. and Talebi, A. 2015. Estimating suspended sediment by Artificial Neural Network (ANN), Decision Trees (DT) and Sediment Rating Curve (SRC) models (Case study: Lorestan Province, Iran). Civil Engineering Infrastructures Journal. 48(2): 373-380.
Demirci, M. and Baltaci, A. 2013. Prediction of suspended sediment in river using fuzzy logic and multilinear regression approaches. Neural Computing and Applications. 23(1): 145-151.
Emamgholizadeh, S. and Karimi Demneh, R. 2018. The comparison of artificial intelligence models for the estimation of daily suspended sediment load: a case study on Telar and Kasilian Rivers in Iran. Water Science and Technology: Water Supply. 1-14. https://doi.org/10.2166/ws.2018.062.
Emamgholizadeh, S., Bateni, S.M., Shahsavani, D., Ashrafi, T. and Ghorbani, H. 2015. Estimation of soil cation exchange capacity using Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS). Journal of Hydrology. 529:1590-1600.
Ferreira, C. 2001. Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. Complex Systems. 13 (2): 87-129.
Galelli, S., Humphrey, G.B., Maier, H.R., Castelletti, A., Dandy, G.C. and Gibbs, M.S. 2014. An evaluation framework for input variable selection algorithms for environmental data-driven models. Environmental Modelling and Software. 62:33-51.
Guven, A., Talu, N.E. 2010. Gene Expression Programing for Estimating Suspended Sediment Yield in Middle Euphrates Basin, Turkey. Clean – Soil Air Water. 38(12):1159–1168.
Jajarmizadeh, M., Kakaei Lafdani, E., Harun, S. and Ahmadi, A. 2015. Application of SVM and SWAT models for monthly streamflow prediction, a case study in south of Iran. KSCE Journal of Civil Engineering. 19(1):345–357.
Jamalizadeh, M.R., Moghaddamnia, A., Piri, J., Arbabi, V., Homayounifar, M. and Shahryari, A. 2008. Dust stormprediction using ANNs techniques (a case study: Zabol city). World Academy of Science, Engineering and Technology. 43:512–.025
Kisi, O. and Ozkan, C. 2017. A new approach for modeling sediment-discharge relationship: Local weighted linear regression. Water Resources Management. 30(2):1-.32
Malik, A., Kumar, A. and Piri, J. 2017. Daily suspended sediment concentration simulation using hydrological data of Pranhita River Basin, India. Computers and Electronics in Agriculture. 138: 20–.82
Melesse, A.M., Ahmad, S., McClain, M.E., Wang, X. and Lim, Y.H. 2011. Suspended sediment load prediction of river systems: An artificial neural network. Agricultural Water Management. 98(5):855-.668
Muzzammil, M., Alama, J. and Danish, M. 2015. Scour prediction at bridge piers in cohesive bed using Gene Expression Programming. Aquatic Procedia. 4:789-.697
Noori, R., Karbassi, A. and Sabahi, M.S. 2009. Evaluation of PCA and gamma test techniques on ANN operation for weekly solid waste prediction. Journal of Environmental Management. 91:767-.177
Ouillon, S. 2018. Why and how do we study sediment transport? Focus on coastal zones and ongoing methods.
Water. 10(4), 390 pp.
Remesan, R., Shamim, M.A. and Han, D. 2008. Model data selection using gamma test for daily solar radiation estimation. Hydrological Processes. 22:4301-.9034
Rashidi, S., Vafakhah, M., Kakaei Lafdani, E. and Javadi, M.R. 2016. Evaluating the support vector machine for suspended sediment load forecasting based on gamma test. Arabian Journal of Geosciences. 9(11). http://dx.doi.org/10.1007/s12517-016-2601-9.
Shamim, M.A., Hassan, M., Ahmad, S. and Zeeshan, M. 2016. A comparison of Artificial Neural Networks (ANN) and Local Linear Regression (LLR) techniques for predicting monthly reservoir levels. KSCE Journal of Civil Engineering. 20(2): 971–977.
Stefansson, A., Koncar, N. and Jones, A.J. 1997. A note on the Gamma test. Neural Computing and Applications. 5:131–133.
Wan Jaafar, W.Z., Liu, J. and Han, A. 2011. Input variable selection for median flood regionalization. Water Resources Research. 47:1-18.
Wu, W., Dandy, G. and Maier, H. 2014. Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modeling. Environmental Modeling and Software. 54:108-127.
Angabini, S., Ahmadi, H., Feizni, S., Motamed Vaziri, B. and Ershadi, S. 2014. Suspended Sediment Concentration Estimation using Artificial Neural Networks and Fuzzy Rule Base Model Case Study: Jagin Dam. Journal of Applied Sciences Research. 10(14):12-17.
Azamathulla, H. 2013. Gene-expression programming to predict friction factor for Southern Italian Rivers. Neural Computing and Applications. 23:1421–1426.
Bagatur, T. and Onen, F. 2014. A predictive model on air entrainment by plunging water jets using GEP and ANN. KSCE Journal of Civil Engineering. 18(1): 304–314.
Barzegari, F., Yosefi, M. and Talebi, A. 2015. Estimating suspended sediment by Artificial Neural Network (ANN), Decision Trees (DT) and Sediment Rating Curve (SRC) models (Case study: Lorestan Province, Iran). Civil Engineering Infrastructures Journal. 48(2): 373-380.
Demirci, M. and Baltaci, A. 2013. Prediction of suspended sediment in river using fuzzy logic and multilinear regression approaches. Neural Computing and Applications. 23(1): 145-151.
Emamgholizadeh, S. and Karimi Demneh, R. 2018. The comparison of artificial intelligence models for the estimation of daily suspended sediment load: a case study on Telar and Kasilian Rivers in Iran. Water Science and Technology: Water Supply. 1-14. https://doi.org/10.2166/ws.2018.062.
Emamgholizadeh, S., Bateni, S.M., Shahsavani, D., Ashrafi, T. and Ghorbani, H. 2015. Estimation of soil cation exchange capacity using Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS). Journal of Hydrology. 529:1590-1600.
Ferreira, C. 2001. Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. Complex Systems. 13 (2): 87-129.
Galelli, S., Humphrey, G.B., Maier, H.R., Castelletti, A., Dandy, G.C. and Gibbs, M.S. 2014. An evaluation framework for input variable selection algorithms for environmental data-driven models. Environmental Modelling and Software. 62:33-51.
Guven, A., Talu, N.E. 2010. Gene Expression Programing for Estimating Suspended Sediment Yield in Middle Euphrates Basin, Turkey. Clean – Soil Air Water. 38(12):1159–1168.
Jajarmizadeh, M., Kakaei Lafdani, E., Harun, S. and Ahmadi, A. 2015. Application of SVM and SWAT models for monthly streamflow prediction, a case study in south of Iran. KSCE Journal of Civil Engineering. 19(1):345–357.
Jamalizadeh, M.R., Moghaddamnia, A., Piri, J., Arbabi, V., Homayounifar, M. and Shahryari, A. 2008. Dust stormprediction using ANNs techniques (a case study: Zabol city). World Academy of Science, Engineering and Technology. 43:512–.025
Kisi, O. and Ozkan, C. 2017. A new approach for modeling sediment-discharge relationship: Local weighted linear regression. Water Resources Management. 30(2):1-.32
Malik, A., Kumar, A. and Piri, J. 2017. Daily suspended sediment concentration simulation using hydrological data of Pranhita River Basin, India. Computers and Electronics in Agriculture. 138: 20–.82
Melesse, A.M., Ahmad, S., McClain, M.E., Wang, X. and Lim, Y.H. 2011. Suspended sediment load prediction of river systems: An artificial neural network. Agricultural Water Management. 98(5):855-.668
Muzzammil, M., Alama, J. and Danish, M. 2015. Scour prediction at bridge piers in cohesive bed using Gene Expression Programming. Aquatic Procedia. 4:789-.697
Noori, R., Karbassi, A. and Sabahi, M.S. 2009. Evaluation of PCA and gamma test techniques on ANN operation for weekly solid waste prediction. Journal of Environmental Management. 91:767-.177
Ouillon, S. 2018. Why and how do we study sediment transport? Focus on coastal zones and ongoing methods.
Water. 10(4), 390 pp.
Remesan, R., Shamim, M.A. and Han, D. 2008. Model data selection using gamma test for daily solar radiation estimation. Hydrological Processes. 22:4301-.9034
Rashidi, S., Vafakhah, M., Kakaei Lafdani, E. and Javadi, M.R. 2016. Evaluating the support vector machine for suspended sediment load forecasting based on gamma test. Arabian Journal of Geosciences. 9(11). http://dx.doi.org/10.1007/s12517-016-2601-9.
Shamim, M.A., Hassan, M., Ahmad, S. and Zeeshan, M. 2016. A comparison of Artificial Neural Networks (ANN) and Local Linear Regression (LLR) techniques for predicting monthly reservoir levels. KSCE Journal of Civil Engineering. 20(2): 971–977.
Stefansson, A., Koncar, N. and Jones, A.J. 1997. A note on the Gamma test. Neural Computing and Applications. 5:131–133.
Wan Jaafar, W.Z., Liu, J. and Han, A. 2011. Input variable selection for median flood regionalization. Water Resources Research. 47:1-18.
Wu, W., Dandy, G. and Maier, H. 2014. Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modeling. Environmental Modeling and Software. 54:108-127.
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