برآورد غلظت رسوب رودخانه گرگانرود با روش‌های هوش‌مصنوعی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش آموخته کارشناسی‌ارشد آبخیزداری دانشگاه گنبدکاووس

2 استادیار گروه آبخیزداری دانشگاه گنبد کاووس

چکیده

در سال­های اخیر، هوش مصنوعی به‌طور گسترده و موفقیت‌آمیز در علوم مختلف استفاده شده است. در این پژوهش، کارایی روش­های نروفازی، ماشین بردار پشتیبان و کمینه مربعات ماشین بردار پشتیبان به‌منظور تخمین غلظت رسوب در خروجی چهار حوضه آبخیز جنگلده، نوده، ارازکوسه و قزاقلی واقع در رودخانه گرگانرود در استان گلستان بررسی شد. برای اجرای مدل‌ها از پنج ترکیب ورودی مختلف با تعداد متفاوت تأخیر جریان و بارش از صفر تا دو روز استفاده شد.  نتایج نشان داد مدل کمینه مربعات ماشین بردار پشتیبان با روش جستجوی ساده شبیه­سازی بهتری نسبت به روش جستجوی شبکه­ای انجام می­دهد. ارزیابی شاخص‌های آماری محاسبه شده نشان داد بهترین مدل‌ شبیه‌ساز غلظت رسوب برای هر چهار حوضه‌ مورد مطالعه مدل نروفازی است که مقدار خطای MEF در ایستگاه‌ جنگلده، نوده، ارازکوسه و قزاقلی به‌ترتیب برابر 3/5، 4/13، 8/4 و 8/2 درصد می­باشد. به‌طور کلی در تمام ایستگاه­ها به‌جز ایستگاه قزاقلی معرفی دبی جریان دو روز قبل به‌عنوان ورودی مدل باعث افزایش خطای پیش­بینی می­شود. علاوه بر آن بارندگی همان روز و روز قبل نیز فقط در ایستگاه ارازکوسه باعث افزایش دقت شده است.

کلیدواژه‌ها


عنوان مقاله [English]

Estimation of Suspended Sediment Concentration in Gorganrood River Using Artificial Intelligence Methods

نویسندگان [English]

  • M Javadi Alinejad 1
  • SM Seyedian 2
  • H Rouhani 1
  • AB Fatabadi 1
1 M.Sc. Graduate Student of Watershed Management, Gonbad Kavous University, Gonbad, Iran
2 Assist. Prof. Watershed Management Department, Gonbad Kavous University, Gonbad, Iran
چکیده [English]

Over the last years, artificial intelligence models have been widely and successfully applied in many fields. In the present study, the efficiency of adaptive neuro-fuzzy inference system (ANFIS), SVM (Support Vector Machine) and Least Squares Support Vector Machines (LS-SVM) have been investigated to estimate the sediment concentration in four gauging stations, namely Jangaldeh, Nodeh, Arazkoosh, and Gazaghly along the Gorganrood River in Golestan province, Iran. The models were defined based on the five different combinations of the river flow and precipitation using time lags from 0 to 2 previous days. The results showed that the LS-SVM model with simplex search procedure had a better performance than the grid search method. Meanwhile, the results obtained from ANFIS model which estimated sediment concentration in Jangaldeh, Nodeh, Arazkoose and Ghazaghli stations with MEF Error of 5.3, 13.4, 4.8 and 2.8 percent, respectively, suggested a higher performance than other models. Overall, at all stations except Gazaghly, considering the antecedent flow with two-day time lag as the input data of the model increased the error magnitudes. Furthermore, the rainfall of the same day and one-day time lag could only enhance the efficiency of the model at Arazkooseh station.

کلیدواژه‌ها [English]

  • Artificial intelligence
  • Gorganroud River
  • Ratig curve
  • Sediment transport
Abe S, 2010. Support Vector Machinesta for Pattern Classification. Springer, London.
Ahmadi H, Tahmoores M and Mohammad Asgari H, 2008. Estimating suspended sediment using fuzzy inference system case study: taleghan watershed. Iranian Journal of Watershed Management Sciences and Engineering 2(1): 5-14.
Asselman NEM, 2000. Fitting and interpretation of sediment rating curves. Journal of Hydrology 23(4): 228-248.
Attarzadeh R and Amini J, 2015. Optimization of backup vector machine classifier using genetic algorithm for classification of polarimetric radar images. Scientific-Research Journal of Science and Technology of Surveying 5(1): 127-138.
Brabanter K, Karsmakers P, Ojeda F, Alzate C, Brabanter J, Pelckmans K, De Moor B, Vandewalle J and Suykens JAK, 2011. LS-SVMlab Toolbox User's Guide version 1.8, Internal Report 10-146, ESAT-SISTA, K.U. Leuven (Leuven, Belgium).
Chang CC and Lin CJ, 2011. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2(3): 1-27.
Cimen M, 2008. Estimation of daily suspended sediment using support vector machines. Hydrological Sciences Journal 53(3): 656-666.
Cobaner M, Unal B, and Kisi O, 2009. Suspended sediment concentration estimation by an adaptive neuro-fuzzzy and neural network approaches using hydromeorological data. Journal of Hydrology 367(1-2): 52-61.
Colby BR, 1956. Relationship of Sediment Discharge to Streamflow, U.S. Department of the Interior, Geological Survey, Water Resources Division, USA.
Cristianini N, and Shawe-Taylor J, 2000. An Introduction to Support Vector Machines. Cambridge University Press, Cambridge.
Cristianini N, Campbell C and Taylor JS, 1999. Dynamically Adapting Kernels in Support Vector Machines. Cambridge, MA: The MIT Press.
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.
Dibike YB, Velickov S, Solomatine DP and Abbott M, 2001. Model induction with support vector machines: introduction and applications. Journal of Computing Civil Engineering 15(3): 208-216.
Firat M and Gungor M, 2010. Monthly total sediment forecasting using adaptive neuro fuzzy inference system. Stocastic Environmental Research Risk Assessment 24: 259-270.
Glysson GD, 1987. Sediment-Transport Curves. U.S. Geology Survey Open File Report. Microfiche, Denver.
Halecki W, Kruk E and Ryczek M, 2018. Estimations of nitrate nitrogen, total phosphorus flux and suspended sediment concentration (SSC) as indicators of surface-erosion processes using an ANN (Artificial Neural Network) based on geomorphological parameters in mountainous catchments. Ecological Indicators 91: 461-469.
Hamzeabad AJ, Khadkhodahosseini M, Akhavan S and Nozari H, 2016. Evaluation of SWAT and SVM Models to Simulate the Runoff of Lighvanchay River. Water and Soil Science- University of Tabriz 26(4.1): 137-150.
Han D and Yang Z, 2001. River flow modeling using support vector machines. Pp. 494-499. Proceding of 29th IAHR. 17–21 September, Beijing, China.
He J, Valeo C, Chu A and Neumann NF, 2011. Prediction of event-based stormwater runoff quantity and quality by ANNs developed using PMI-based input selection. Journal of Hydrology 400: 10-23.
Jacquin AP and Shamseldin AY, 2006. Development of rainfall- runoff models using takagi-sugeno fuzzy Inference System. Journal of Hydrology 329: 154-173.
Jafari Miyanai S and Keshavarzi A, 2008. Comparison of Fuzzy Method and Statistical Regression to Estimate Sediment Load in Rivers. Pp. 41-53. Fourth National Congress on Civil Engineering, Tehran University, Tehran.
Jang JSR, Sun CT and Mizutani E, 1997. Neuro-Fuzzy and Soft Computing, a Computational Approach to Learning and Machine Intelligence. Prentice Hall, London.
Jie LC and Yu ST, 2011. Suspended sediment load estimate using support vector machines in Kaoping River basin. Pp. 494-499. Proceding of International Conference on Consumer Electronics, Communications and Networks. 16-18 April, XianNing, China.
Kakaei Lafdani E, Moghaddam Nia A and Ahmadi A, 2013. Daily suspended sediment load prediction using artificial neural networks and support vector machines. Journal of Hydrology 478: 50-62.
Kecman V, 2001. Learning and Soft Computing: Support Vector Machines. Neural Networks and Fuzzy Logic Models. MIT Press, Cambridge, Massachusetts, London, England.
Khorshiddoost AM, Feyzallohpour M and Sadrafshari S, 2015. Evaluation of the functionality of the neural fuzzy inference system model (anfis) in estimating sediment load and comparison with two types of artificial neural network models case study: Zarinehroud river, southeast basin, Urmia lake. Geography and Development Quarterly 13(41): 185-200.
Kisi O and Shiri J, 2012. River suspended sediment estimation by climatic variables implication comparative study among soft computing techniqques. Computers & Geosciences 43: 73-82.
Kisi O, 2005. Suspended sediment estimation using neuro-fuzzy and neural network approaches. Hydrological Sciences Journal 50(4): 683-696.
Kisi O, 2012. Modelling discharge-suspended sediment relationship using least square support vector machine. Journal of Hydrology 456: 110-120.
Kisi O, Haktanir T, Ardiclioglu M, Ozturk O, Yalcin E and Uludag S, 2009. Adaptive neuro-fuzzy computing technique for suspended sediment estimation. Advances in Engineering Software 40(6): 438-444.
Kisi O, Karahan ME and Sen Z, 2006. River suspended sediment modeling using fuzzy logic approach. Hydrological Processes 20(20): 4351-4362.
Koch RW and Smillie GM, 1986. Comment on "River loads underestimated by rating curves" by R. I. Ferguson. Water Resource Research 22: 2121-2122.
Kumar D, Pandey A, Sharma N and Flugel WA, 2016. Daily suspended sediment simulation using machine learning approach. Catena 138: 77-90.
Kumar M and Kar IN, 2002. Non-linear HVAC computations using least square support vector machines. Energy Conversion Management 50: 1411–1418.
Lohani A K, Goel NK and Bhatia KKS, 2007. Deriving Stage- discharge- sediment concentration relationships using fuzzy logic. Hydrological Sciences Journal 52(4): 793-807.
Maheswaran R and Khosa R, 2012. Wavelet–Volterra coupled model for monthly stream flow forecasting. Journal of Hydrology 450: 320-335.
Misra D, Oommen T, Agarwal A, Mishra SK and Thompson AM, 2009. Application and analysis of support vector machine based simulation for runoff and sediment yield. Biosystems Engineering 103: 527-535.
Moayeri MM, Nikpoor MR, Hoseinzadehdalir A and Farsadizadeh D, 2010. Comparison of artificial neural networks, adaptive neuro-fuzzy and sediment rating curve models for estimating suspended sediment load of Ajichay river. Water and Soil Science- University of Tabriz 20:1(2): 71-82.
Nourani V and Andalib G, 2015. Daily and monthly suspended sediment load predictions using wavelet based artificial intelligence approaches. Journal of Mountain Science 12(1): 85-100.
Nourani V, Alizadeh F and Roushangar K, 2015. Evaluation of a two-Stage SVM and spatial statistics methods for modeling monthly river suspended sediment load. Water Resource Management 30(1): 393-407.
Rezaeibanafshe M, Feyzallohpour M and Sadrafshari S, 2013. Estimating sediment load using of neural fuzzy inference system and comparing with MLR and SRC models in the area of the Qaranko River. Natural Geography Research 45(2): 77-90.
Roshangar K and Hakimi D, 2017. Estimation of bed load rate of a gravel bed river using evalutionary systems and classic methods. Water and Soil Science- University of Tabriz 27(1): 187-197.
Samantaray S and Ghose D, 2018. Evaluation of suspended sediment concentrarion using descent neural networks. Procedia Computer Science 132: 1824-1831.
Seyedian M and Rouhani H, 2015. Assessing ANFIS accuracy in estimation of suspended sediments. Gradevinar 67(12): 1165-1176.
Sharma N, Zakaullah MD, Tiwari H and Kumar D, 2015. Runoff and sediment yield modeling using ANN and support vector machines: a case study from Nepal watershed. Modeling Earth Systems and Environment 1(23): 1-8.
Shu-gang C, Yan-bao L and Yan-ping W, 2008. A forecasting and forewarning model for methane hazard in working face of coal mine based on LS-SVM. Journal of China University Mining Technology 18: 0172–0176.
Sivapragasam C, Liong SY and Pasha MFK, 2001. Rainfall and runoff forecasting with SSA–SVM approach. Journal of Hydroinformatic 3(3): 141–152.
Smola A, 1996. Regression Estimation with Support Vector Learning Machines. Technics Universitat Munchen: Munich, Germany.
Suykens JAK and Vandewalle J, 1999. Least square support vector machine classifiers. Neural Processing Letters 9(3): 293–300.
Tananaev N, 2015. Fitting sediment rating curves using regression analysis: a case study of Russian arctic rivers. Pp. 213-224. Proceedings of Sediment Dynamics from the Summit to the Sea. 3-5 December, Louisiana, USA.
Ulke A, Tayfur G and Ozkul S, 2009. Predicting suspended sediment loads and missing data for Gediz river, Turkey. Journal of Hydrologic Engineering 14(9): 954-965.
Vapnik VN, 1995. The Nature of Statistical Learning Theory. Springer-Verlag, New York.
Varvani J and Khalighisigaroodi Sh, 2007. Investigating the error rate of sediment gauge curves for estimating sediment load of flood events in Gharacheh. New Agricultural Findings 1(3): 201-214.
Varvani J, Najafinejad A and Mirmoeinikarheroodi A, 2008. Modification of sediment scale curve using minor variance unbiased method. Journal of Agricultural Science and Natural Resources 15(1): 27-41.
Walling DE and Webb BW, 1988. The reliability of rating curve estimates of suspended sediment yield: some further comments. Pp. 337-350. Proceedings of the Sediment Budgets (Proceedings of the Porto Alegre Symposium). 5-7 December, Devon, UK.
Yang CT, 1996. Sediment Transport: Theory and Practice. McGraw-hill, New York.
Zounemat-Kermani M, Kisi O, Adamowski J and Ramezani A, 2016. Evaluation of data driven models for river suspended sediment concentration modeling. Journal of Hydrology 535: 457-472.