شبیه‌سازی پیوسته بارش- رواناب با استفاده از شبکه‌های عصبی مصنوعی برمبنای انتخاب متغیرهای موثر ورودی با الگوریتم اطلاعات متقابل جزئی(PMI)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش‌آموخته کارشناسی ارشد مهندسی منابع آب/ واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران.

2 گروه علوم و مهندسی آب/ واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران.

3 گروه علوم و مهندسی آب/ واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران

چکیده

آگاهی ازتوان طبیعی تولید رواناب درحوضه‌های آبریز یکی از نیازهای اساسی برای برنامه‌ریزی اصولی جهت بهره‌برداری بهینه از رواناب می باشد. از اینرو شبیه‌سازی بارش –رواناب در حوضه‌های آبریز از اهمیت زیادی برخوردار می‌باشد. در این مقاله به شبیه‌سازی پیوسته بارش-رواناب در حوضه سد مارون با شبکه های عصبی مصنوعی پرداخته شد تا توانایی و دقت این شبکه‌ در برآورد رواناب نیز ارزیابی گردد. با توجه با اینکه تعداد روزهای بارندگی در هر سال کمتر از روزهای غیر بارندگی می‌باشد بنابراین رواناب خروجی از حوضه ناشی از دو مکانیسم متفاوت می‌باشد. در زمانهای همراه با وقوع بارش و چند روز بعد از آن، رواناب خروجی از حوضه عمدتا به صورت سیلابهای با دبی زیاد و تداوم کم می باشد. ولی در اکثر روزهای سال که بارندگی وجود ندارد، رواناب خروجی بصورت جریان پایه با مقادیر دبی کم و با تداوم زیاد می‌باشد. بنابراین در این تحقیق سعی بر ارائه یک مدل بارش-رواناب دو ضابطه‌ای شامل مدل مربوط به روزهای بارانی و مدل مربوط به روزهای غیربارانی شده است. همچنین متغیر‌های ورودی موثر در دبی جریان در حوضه مارون با استفاده از الگوریتم اطلاعات متقابل جزیی (PMI) تعیین شده‌اند. مقایسه مقادیر شاخص‌های آماری بین مدل تک‌ضابطه‌ای و مدل دوضابطه‌ای نشان می‌دهد که دقت مدل دوضابطه‌ای در برآورد دبی جریان در ایستگاه ایدنک بیشتر از دقت مدل تک‌ضابطه‌ای می‌باشد. بطوری که ضریب ناش-ساتکلایف برای مدل تک‌ضابطه‌ای و دوضابطه‌ای به ازای مرحله آزمون شبکه به ترتیب برابر با 86/0 و 94/0 می‌باشد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Continuous rainfall-runoff simulation by artificial neural networks based on efficient input variables selection using partial mutual information (PMI) algorithm

نویسندگان [English]

  • Mehrdad Shafeizadeh 1
  • Hosein Fathian 2
  • Alireza Nikbakht Shahbazi 3
1 M.Sc, Graduate of Water Resources Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran.
2 Department of Water Sciences and Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran.
3 Department of Water Sciences and Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran
چکیده [English]

Knowledge on the natural ability of basins is one of fundamental needs to optimal utilization of runoff. Thus, rainfall-runoff simulation in basins is of utmost importance. Continuous simulation of rainfall-runoff in Maroun basin performed using Artificial Neural Networks (ANNs) in order to evaluate the ability and accuracy of ANN for runoff estimation. Considering the fact that the number of rainy days per year less than sunny days, so runoff is caused by two different mechanisms. In continuous rainfall time and a few days later, runoff mainly is from high discharge and low base time. But on most days when there is no rainfall, baseflow has low discharge and long base time .Thus, in this research a double criterion model of rainfall-runoff includes model on rainy days and non rainy days were examined. Also efficient input variables on runoff in the Maroun basin are determined using the partial mutual information (PMI) algorithm. Comparison of statistical criteria between the single criterion model and double criterion model indicated that the double criterion model were more accurate. Therefore, the Nash-Sutcliff coefficient of single criterion model and double criterion model for test stage of network were 0.86 and 0.94 respectively.

کلیدواژه‌ها [English]

  • Artificial Neural Networks
  • Input variable selection
  • PMI algorithm
  • Continuous Rainfall-Runoff simulation
A
brahart R, Kneale PE, and See LM (2004) Neural networks for hydrological modeling. CRC Press, 316p
Akaike H (1974) A new look at the statistical model identification. IEEE Transactions on Automatic Control 19:716-723
Anusree K and Varghese KO (2016) Streamflow prediction of Karuvannur river basin using ANFIS, ANN and MNLR models. Procedia Technology 24:101-108
Araghinejad S and Karamouz M (2005) Long-lead streamflow forecasting using artificial neural networks and fuzzy inference system. Iran Water Resources Research 1(2):29-41 (In Persian)
Bowden GJ, Maier HR, and Dandy GC (2005) Input determination for neural network models in water resources applications. Part 2. Case study: Forecasting salinity in a river. Journal of Hydrology 301(1-4):93-107
Chang TK, Talei A, Alaghmand S and Ooi MPL (2017) Choice of rainfall inputs for event-based rainfall-runoff modeling in a catchment with multiple rainfall stations using data-driven techniques. Journal of Hydrology 545:100-108
Cover TM and Thomas JA (1991) Elements of information theory. John Wiley & Sons, Inc., New York, 776p
Dastorani MT, Sharifi Darani H, Talebi A, Moghadam Nia A (2011) Evaluation of the application of artificial neural networks and adaptive neuro-fuzzy inference systems for rainfall-runoff modelling in Zayandehrood dam basin. Iranian Journal of Water and Wastewater 22:114-125 (In Persian)
David FN (1966) Tables of the correlation coefficient. In: Pearson ES, Hartley HO (Eds.) Biometrika tables for statisticians, 3rd ed., vol. 1. Cambridge University Press, Cambridge
Davies L and Gather U (1993) The identification of multiple outliers. Journal of the American Statistical Association 88(423):782-792
Dehghani M, Morid S, and Norouzi A (2010) Runoff simulation in snowbound catchments using SRM and ANN models to estimate hydropower potentials in data scarcity situations. Iran-Water Resources Research 6(3):12-24 (In Persian)
El-Shafie A, Mukhlisin M, Najah AA and Taha MR (2011) Performance of artificial neural network and regression techniques for rainfall runoff prediction. International Journal of Physical Sciences 6(8):1997-2003
Fang W, Huang S, Huang Q, Huang G, Meng E, and Luan J (2018) Reference evapotranspiration forecasting based on local meteorological and global climate information screened by partial mutual information. Journal of Hydrology 561:764-779
Ghafari GA and Vafakhah M (2013) Simulation of rainfall-runoff process using artificial neural network and adaptive neuro-fuzzy inference system (Case study: Hajighoshan watershed). Journal of Watershed Management Research 4(8):120-136
Ghorbani, MA, Azani A, and Mahmoudi Vanolya S (2015) Rainfall-runoff modeling using hybrid intelligent models. Iran-Water Resources Research 11(2):146-150 (In Persian)
Goebel B, Dawy Z, Hagenauer J, and Mueller JC (2005) An approximation to the distribution of finite sample size mutual information estimates. In: IEEE International Conference on Communications (ICC-05), Seoul, South Korea
Granger CW, Maasoumi E, and Racine J (2004) A dependence metric for possibly nonlinear processes. Journal of Time Series Analysis 25(5):649-669
Haghizadeh A, Mohammadlou M, and Noori F (2015) Rainfal-runoff simulation using ANN and ANFIS and MLR (Case study: Khorramabad watershed). Iranian Journal of Ecohydrology 2(2):233-243 (In Persian)
Kalra R, Deo MC, Kumar R, and Agarwal VK (2005) RBF network for spatial mapping of wave heights. Marine Structures 18(3):289-300
Khaleghi M, Ghodosi J, Ahmadi H, and Kamyar M (2010) Hydrograph methods for performance evaluation Geomorphological instantaneous unit estimate peak flood discharge. Journal of Science and Technology of Agriculture and Natural Resources, Water and Soil Sciences 5:89-100 (In Persian)
Kisi O (2007) Streamflow forecasting using different artificial neural network algorithms. Journal of Hydrologic Engineering 12(5):532-539
Kisi O (2010) Wavelet regression model for short-term streamflow forecasting. Journal of Hydrology 389(3-4):344-353
Kurtulus B and Razack M (2010) Modeling daily discharge responses of a large karstic aquifer using soft computing methods: Artificial neural network and neuro-fuzzy. Journal of Hydrology 381(1-2):101-111
Lee SC, Lin HT, and Yang TY (2010) Artificial neural network analysis for reliability prediction of regional runoff utilization. Environmental Monitoring and Assessment 161(1-4):3150-326
May RJ, Dandy GC, Maier HR, and Fernando TMKG (2006) Critical values of a kernel-density based mutual information estimator. In: Proc. of the IEEE International Joint Conference on Neural Networks, Vancouver, 4898-4903
May RJ, Maier HR, Dandy GC, and Fernando TMKG (2008) Non-linear variable selection for artificial neural networks using partial mutual information. Environmental Modelling & Software 23(10):1312-1326
May RJ, Dandy GC, Maier HR, and Nixon JB (2008) Application of partial mutual information variable selection to ANN forecasting of water quality in water distribution systems. Environmental Modelling and Software 23(10-11):1289-1299
Miguélez M, Puertas J, and Rabuñal JR (2009) Artificial neural networks in urban runoff forecast. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5517 LNCS(PART 1):1192-1199
Nabizadeh M, Mosaedi A, Dehghani AA (2012) Intelligent estimation of streamflow by adaptive neuro-fuzzy inference system. Journal of Water and Irrigation Management 2:69-80 (In Persian)
Nash JE and Sutcliffe JV (1970) River flow forecasting through conceptual models; part I: A discussion of principles. Journal of Hydrology 10:282-290
Nourani V, Keynezhad M, and Makani L (2009) Using adaptive neuro-fuzzy inference system rainfall-runoff modeling. Journal of Civil and Environmental Engineering 39:75-81 (In Persian)
Pearson RK (2002) Outliers in process modeling and identification. IEEE Transactions on Control Systems Technology 10(1):55-63
Salajegheh A, Fathabadi A, and Mahdavi M (2009) Investigation on the efficiency of neuro-fuzzy method and statistical models in simulation of rainfall-runoff process. Journal of Range and Watershed Management. Iranian Journal of Natural Resources 62:65-79 (In Persian)
Shahverdi K and Samani JMV (2010) Automated simulation of basin characteristics using HEC-HMS, genetic algorithm, and AutoIt on observed hydrograph properties. Iran-Water Resources Research 6(3):96-99 (In Persian)
Shannon CE (1948) A mathematical theory of communication. Bell System Technical Journal 27:379-423
Sharma A (2000) Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: part 1: A strategy for system predictor identification. Journal of Hydrology 239:232-239
Tan Q-f, Lei X-h, Wang X, Wang H, Wen X, Ji Y, and Kang A-q (2018) An adaptive middle and long-term runoff forecast model using EEMD-ANN hybrid approach. Journal of Hydrology 567:767-780
Toker AS and Markus M (2000) Precipitation-runoff modeling using artificial neural network and conceptual models. Journal of Hydrologic Engineering 5:156-161
Zareazadeh-Mehrizi M and Bozorg Hadad O (2010) Inflow simulation and forecasting optimization using hybrid ANN-GA algorithm. Journal of Water and Soil 24(5):942-954 (In Persian)