پیش بینی مقادیر دبی روزانه رودخانه تالار با استفاده از مدل یادگیری کندرو

نوع مقاله : مقالات پژوهشی

نویسندگان

1 دانشگاه کشاورزی و منابع طبیعی ساری

2 دانشگاه صائب

3 دانشگاه آزاد اسلامی واحد زنجان

چکیده

توسعه تکنیک های مختلف جهت پیش بینی کوتاه مدت و بلند مدت دبی ساعتی، روزانه، ماهانه و سالانه جریان به منظور مدیریت منابع آب و پروژه‌های عمرانی سابقه طولانی دارد. در دهه های اخیر، تکنیک های یادگیری متعددی به طور گسترده جهت پیش بینی دبی و سایر متغیرهای هیدرولوژیک مورد توجه قرار گرفته اند. پژوهش حاضر با هدف پیش بینی مقادیر دبی روزانه با استفاده از بهینه ترین تعداد داده های ورودی در رودخانه منتهی به رود تالار واقع در حوزه آبخیز کسیلیان انجام گرفت. بدین منظور از سه مدل ثابت (conll.c)، خطی ((linll.c و درجه دو ((quall.c الگوریتم یادگیری محلی کندرو که از کارایی قابل توجهی در شبیه سازی پارامترهای متغیر با داده های ورودی کم برخوردار هستند و هم چنبن با استفاده از سری داده های ورودی 6، 8، 10، 15، 20 روز قبل، یک و دو ماه قبل، یک، دو و سه فصل قبل و در نهایت یک و دو سال قبل استفاده گردید. نتایج به دست آمده از چندین هزار مدل تعلیمی تهیه شده نشان داد که مدل ثابت با داده های دبی روزانه ی 60 روز گذشته با حداقل خطای 001/0 به مقدار واقعی نزدیک تر بوده است. نتایج به دست آمده از مقادیر محاسبه شده RMSE و MAE حاکی از آن است که الگوریتم یادگیری محلی کندرو علی-رغم محدودیت های موجود از جمله حساسیت بسیار بالا به همسایگی، از کارایی بالایی در شبیه سازی سری های زمانی با خطای نسبتاً پایین (RMSE کمتر از 06/0) برخوردار می باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Daily Discharge Estimation in Talar River Using Lazy Learning Model

نویسندگان [English]

  • Zahra Abdollahi 1
  • Ataollah Kavian 1
  • kaka shahedi 1
  • neda Abdollahi 2
  • Mohammad Jafari 3
1 College of Agricultural Sciences and Natural Resources, Sari University
2 Saeb University
3 Islamic Azad University, Zanjan
چکیده [English]

Introduction: River discharge as one of the most important hydrology factors has a vital role in physical, ecological, social and economic processes. So, accurate and reliable prediction and estimation of river discharge have been widely considered by many researchers in different fields such as surface water management, design of hydraulic structures, flood control and ecological studies in spetialand temporal scale. Therefore, in last decades different techniques for short-term and long-term estimation of hourly, daily, monthly and annual discharge have been developed for many years. However, short-term estimation models are less sophisticated and more accurate.Various global and local algorithms have been widely used to estimate hydrologic variables. The current study effort to use Lazy Learning approach to evaluate the adequacy of input data in order to follow the variation of discharge and also simulate next-day discharge in Talar River in KasilianBasinwhere is located in north of Iran with an area of 66.75 km2. Lazy learning is a local linear modelling approach in which generalization beyond the training data is delayed until a query is made to the system, as opposed to in eager learning, where the system tries to generalize the training data before receiving queries
Materials and Methods: The current study was conducted in Kasilian Basin, where is located in north of Iran with an area of 66.75 km2. The main river of this basin joins to Talar River near Valicbon village and then exit from the watershed. Hydrometric station located near Valicbon village is equipped with Parshall flume and Limnogragh which can record river discharge of about 20 cubic meters per second.In this study, daily data of discharge recorded in Valicbon station related to 2002 to 2012 was used to estimate the discharge of 19 September 2012. The mean annual discharge of considered river was also calculated by using available data about 0.441 cubic meters per second. To estimate the discharge of considered day, three methods of constant, linear and quadratic functionscontrollers based on the local linearization provided by the lazy learning algorithm were considered. Lazy learning is a memory-based linear technique for local modeling approach which is reported as a high-efficient algorithm for simulating variables with low input data.The series of input data was categorized into previous 6, 8, 10, 15 and 20 days, 1 and 2 months, 1, 2 and 3 seasons and also 1 and 2 years to evaluate which series is appropriately enough to predict next-day discharge inthe river. Then, mean absolute error and root-mean square error were calculated for all series and modelsin order to find the best estimator model and the most appropriate series of input data.
Results: Results showed that constant and linear model had the minimum root-mean square error of 0.001 and 0.057 respectivelywith previous 60 days’ data series. Whilethe quadratic model had its best estimation with previous 2 season data series with the minimum root-mean square error of 0.059. The result indicated that the more input data increase, the best quadratic model estimate until 60 days. But after 60 days, estimation error gradually increased. Consequently, not more data but adequate areneeded for accurate estimation. Also, RMSE in linear model had less fluctuation and therefore less sensitivity compared with other models. And quadratic model had less fluctuation and sensitivity to neighborhoods. Also, according to results, the more variation in each period increase, the better estimation is accrued by lazy learning algorithm. Hence, it was expected that next-day discharge prediction in low-water period needs longer data series than high-water period.
Conclusion: Regarding to thousands of prepared training models, constant model with previous 60 days’ data and minimum error of 0.0001 was selected as the most accurate estimatefor next-day river discharge in Talar River. Results showed that despite of some limitation and demerits, the local Lazy Learning algorithm has significant efficiency in time series simulating. Although the accuracy of simulation increase with more input data, but this algorithm can runby at least 5 training data. However we find lazy learning to be the best performing approach on average goodness indicators (such as mean absolute error and Root-mean square error). On the other hand, the lazy learning predictor can be quickly developed and easily kept up-to-date by adding new data to its database. Also, it does not face with overfitting problems which are common in global modeling approaches.According to some noteworthy features of lazy learning noticed in this regards, this approach will have good performance for time-series studies.

کلیدواژه‌ها [English]

  • Daily discharge
  • Kasilian Basin
  • Local lazy learning model
  • Nearest Neighborhood
1- Bhagwat P., and Maity R. 2012. Multistep-ahead river flow prediction using LS-SVR at daily scale. Journal of Water Resource and Protection, 4:528-539.
2- Birattari M., and Bontempi G. 1999. Lazy learning VS. Speedy Gonzales: A fast algorithm for recursive identification and recursive validation of local constant models. Technical reports: Iridia. Universiteˋ Libre de Bruxelles. TR/IRIDIA/99-6.
3- Birattari M., Bontempi G., and Bersini H. 1998. Lazy learning for iterated time-series prediction. In: J.A.K. Suykens, J. Vandewalle (ed.). Proceedings of the International Workshop on Advanced Black-Box.Techniques for Nonlinear Modeling.
4- Bontempi G. 1999. Local learning techniques for modeling, prediction and control. Ph.D. thesis.Universite’ Libre de Bruxelles, Belgium.
5- Bontempi G., Birattari M., and Bersini H. 1999. Lazy learning for local modeling and control design. International Journal of Control, 72(7/8): 643-658.
6- Cannas B., Fanni A., See L., and Sias G. 2006. Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning.Physics and Chemistry of the Earth, 31(18):1164-1171.
7- Chang F-J., and Chen Y-C. 2001. A Counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction. Journal of Hydrology, 245:153-164.
8- Corani G. 2005. Air quality prediction in Milan: feed-forward neural networks, pruned neural networks and lazy learning. Ecological Modelling, 185:513–529.
9- Jafari M. 2011. Predicting the position of special-purpose nodes in mobile networks using Lazy Learning Model. MSc Thesis. Islamic Azad University. 107 Pages (In Persian with English abstract).
10- Jafari M., and Khanteymoori A.R. 2011. Lazy Learning in optimization problems using PRESS statistics. Proceedings of the the application of ai in industry. Bardsir.
11- Jafari M., Abdollahi N., and Mohammadi H. 2011. Predicating the location of nodes in AD Hoc network by lazy learning method. Innovative Computing Technology, Communications in Computer and Information Science, 241:336-345.
12- Karamuz M., and Araghinejad Sh. 2005. Advanced hydrology. AmirKabir University. Iran.
13- Kişi O. 2004. River flow modeling using artificial neural networks. ASCE Journal of Hydrologic Engineering, 9(1):60-63.
14- Kişi O. 2008. Stream flow forecasting using neuro-wavelet technique. Journal of Hydrological Process, 22:4142-4152.
15- Myer R.H. 1990. Classical and modern regression with applications. Boston. M.A: PWS-KENT.
16- Pulido-Calvo I., and Portela. M.M. 2007.Application of neural approaches to one-step daily flow forecasting in Portuguese water sheds, Journal of Hydrology, 332: 1 – 15.
17- Sivakumar B., Jayawardena A.W., and Fernando T.M.K.G. 2002. River flow forecasting: use of phase space reconstruction and artificial neural networks approaches. Journal of Hydrology, 265:225-245.
18- Suri A. 2014. Advanced econometrics. Farhangshenasi. Iran.
19- Turan M.E., and Yurdusev M.A. 2009. River flow estimation from upstream flow records by artificial intelligence methods. Journal of Hydrology, 369:71–77.
20- Valls J.M., Galvan I.M., and Isasi P. 2004. Lazy learning in radial basis neural networks: a way of achieving more accurate models. Neural Processing Letters, 20(2):105-124.
21- Wang W., and Ding J. 2003. Wavelet network model and its application to the prediction of hydrology. Nature and Science, 1:67-71.
22- Zealand C.M., Burn D.H., and Simonovic S.P. 1999. Short-term streamflow forecasting using artificial neural networks. Journal of Hydrology, 214:32–48.