Soft Computing-Based Workable Flood Forecasting Model for Ayeyarwady River Basin of Myanmar

AbstractIt is a challenging task for working hydrologists of Myanmar to get information from all gauge and discharge sites during a flood to model the forecast properly. In such a case, the concept of this work is very useful for real-time flood forecasting, particularly when data of all the gauge sites are not available regularly or timely. In that context, one has to rely on some accessible sites to get a workable forecast. Additionally, the best combination of the available data can be selected for making the flood forecast. The study is done for the establishment of a flood forecasting model with maximum efficiency using very little information. Three upstream sites named as Sagaing, Monywa, and Chauk of the Ayeyarwady river are selected as the base station and the downstream Pyay as the forecasting station in this study. The artificial neural network (ANN) multilayered feed forward (MLFF) network along with the Takagi-Sugeno (TS) fuzzy inference model are applied in this paper. The developed model is...

[1]  R. Shibata.  Selection of the order of an autoregressive model by Akaike's information criterion , 1976 .

[2]  Teresa Bernarda Ludermir,et al.  Hybrid Systems for River Flood Forecasting Using MLP, SOM and Fuzzy Systems , 2009, ICANN.

[3]  Sevket Durucan,et al.  River flow prediction using artificial neural networks: generalisation beyond the calibration range. , 2000 .

[4]  Demetris F. Lekkas,et al.  APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR FLOOD FORECASTING , 2004 .

[5]  Christian W. Dawson,et al.  Hydrological modelling using artificial neural networks , 2001 .

[6]  Yu,et al.  52. Updating Real-Time Flood Forecasting Using a Fuzzy Rule-Based Model , 2005 .

[7]  Yan Li,et al.  Comparison of Several Flood Forecasting Models in Yangtze River , 2005 .

[8]  Keith W. Hipel,et al.  Fuzzy Relation Analysis for Multicriteria Water Resources Management , 1999 .

[9]  Dimitri P. Solomatine,et al.  M5 Model Trees and Neural Networks: Application to Flood Forecasting in the Upper Reach of the Huai River in China , 2004 .

[10]  K. P. Sudheer,et al.  Fuzzy model identification based on cluster estimation for reservoir inflow forecasting , 2008 .

[11]  A. Soldati,et al.  River flood forecasting with a neural network model , 1999 .

[12]  Kuolin Hsu,et al.  Artificial Neural Network Modeling of the Rainfall‐Runoff Process , 1995 .

[13]  K. Chau,et al.  Predicting monthly streamflow using data‐driven models coupled with data‐preprocessing techniques , 2009 .

[14]  Marnik Vanclooster,et al.  Comparison of Fuzzy and Nonfuzzy Optimal Reservoir Operating Policies , 2002 .

[15]  R. Shumway.  Applied Statistical Time Series Analysis , 1988 .

[16]  Min Min Swe Zin,et al.  Bayesian Decision Approach to Protection on the Flood Event in Upper Ayeyarwady River, Myanmar , 2008 .

[17]  Narendra Singh Raghuwanshi,et al.  Flood Forecasting Using ANN, Neuro-Fuzzy, and Neuro-GA Models , 2009 .

[18]  Wayne Woldt,et al.  Fuzzy rule-based approach to describe solute transport in the unsaturated zone , 1999 .

[19]  M. Chung,et al.  Identification of fuzzy relational model and its application to control , 1993 .

[20]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[21]  W. Pedrycz.  An identification algorithm in fuzzy relational systems , 1984 .

[22]  Soichi Nishiyama,et al.  A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff , 2007 .

[23]  A. W. Minns,et al.  Artificial neural networks as rainfall-runoff models , 1996 .

[24]  Chuntian Cheng,et al.  A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series , 2009 .

[25]  Bernard Bobée,et al.  Daily reservoir inflow forecasting using artificial neural networks with stopped training approach , 2000 .

[26]  Chandranath Chatterjee,et al.  Development of an accurate and reliable hourly flood forecasting model using wavelet–bootstrap–ANN (WBANN) hybrid approach , 2010 .

[27]  Paulin Coulibaly,et al.  Nonstationary hydrological time series forecasting using nonlinear dynamic methods , 2005 .

[28]  Paresh Deka,et al.  A fuzzy neural network model for deriving the river stage—discharge relationship , 2003 .

[29]  K. P. Sudheer,et al.  Short‐term flood forecasting with a neurofuzzy model , 2005 .

[30]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[31]  Nachimuthu Karunanithi,et al.  Neural Networks for River Flow Prediction , 1994 .

[32]  Kevin L. Priddy,et al.  Artificial neural networks - an introduction , 2005, Tutorial text series.

[33]  A. K. Lohani,et al.  Reply to comments provided by Z. Şen on “Takagi–Sugeno fuzzy system for modeling stage-discharge relationship” by A.K. Lohani, N.K. Goel and K.K.S. Bhatia , 2007 .

[34]  E. H. Mamdani,et al.  Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis , 1976, IEEE Transactions on Computers.

[35]  Kevin L. Priddy,et al.  Artificial Neural Networks: An Introduction (SPIE Tutorial Texts in Optical Engineering, Vol. TT68) , 2005 .

[36]  Robert Babuska,et al.  Fuzzy Modeling for Control , 1998 .

[37]  Ulrich Anders,et al.  Model selection in neural networks , 1999, Neural Networks.

[38]  Chuntian Cheng,et al.  Using support vector machines for long-term discharge prediction , 2006 .

[39]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[40]  Timothy K. Gates,et al.  Planning Reservoir Operations with Imprecise Objectives , 1997 .

[41]  N. K. Goel,et al.  Takagi–Sugeno fuzzy inference system for modeling stage–discharge relationship , 2006 .

[42]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[43]  Kurt Hornik,et al.  FEED FORWARD NETWORKS ARE UNIVERSAL APPROXIMATORS , 1989 .

[44]  A. Soldati,et al.  Artificial neural network approach to flood forecasting in the River Arno , 2003 .

[45]  Tiesong Hu,et al.  River flow time series prediction with a range-dependent neural network , 2001 .

[46]  K. Sasikumar,et al.  Application of fuzzy probability in water quality management of a river system , 2000, Int. J. Syst. Sci..

[47]  P. C. Nayak,et al.  Improving peak flow estimates in artificial neural network river flow models , 2003 .

[48]  P. P. Mujumdar,et al.  Reservoir Operation Modelling with Fuzzy Logic , 2000 .

[49]  Michael Bruen,et al.  Functional networks in real-time flood forecasting—a novel application , 2005 .

[50]  Holger R. Maier,et al.  Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..

[51]  K. P. Sudheer,et al.  A data‐driven algorithm for constructing artificial neural network rainfall‐runoff models , 2002 .

[52]  P. C. Nayak.  Explaining Internal Behavior in a Fuzzy If-Then Rule-Based Flood-Forecasting Model , 2010 .

[53]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[54]  K. P. Sudheer,et al.  Rainfall‐runoff modelling using artificial neural networks: comparison of network types , 2005 .

[55]  A. Bárdossy,et al.  Analysis of a karstic aquifer management problem by fuzzy composite programming , 1992 .

[56]  A. Tokar,et al.  Rainfall-Runoff Modeling Using Artificial Neural Networks , 1999 .

[57]  A. K. Lohani,et al.  DEVELOPMENT OF FUZZY LOGIC BASED REAL TIME FLOOD FORECASTING SYSTEM FOR RIVER NARMADA IN CENTRAL INDIA , 2005 .

[58]  Chuntian Cheng,et al.  Optimizing Hydropower Reservoir Operation Using Hybrid Genetic Algorithm and Chaos , 2008 .

[59]  Chuntian Cheng,et al.  Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall–runoff model calibration , 2002 .