Forecasting the Level of Reservoirs Using Multiple Input Fuzzification in ANFIS

Estimation the Level of water is one of the crucial subjects in reservoir management influencing on reservoir operation and decision making. One of the most accurate artificial intelligence model used broadly in water resource aspects is adaptive neuro-fuzzy interface system (ANFIS) taking in to account the membership functions (MF) on the basis of the smoothness characteristics and mathematical components each for set of input data. All researches in hydrological estimation used ANFIS, merely a type of MF has been noticed for all sets of inputs without considering the response of each of them. This study is applying a specified certain MFs for each type of input to improve the accuracy of ANFIS model in forecasting the water level in Klang Gates Dam in Malaysia. On the basis of the previous studies, two most popular MFs, Generalized Bell Shape MF and, Gaussian MF, are employed for examine the new pattern in two inputs ANFIS architecture resulted less stress in error performance, and higher accuracy in estimation, compare to the traditional ANFIS model. The aim is achieved by evaluating the performance in and fitness of the model in daily reservoir estimation.

[1]  Mohammad Ali Ghorbani,et al.  Comparison of three artificial intelligence techniques for discharge routing , 2011 .

[2]  Juan A. Lazzús,et al.  Neural network-particle swarm modeling to predict thermal properties , 2013, Math. Comput. Model..

[3]  Tommy S. W. Wong,et al.  Evaluation of rainfall and discharge inputs used by Adaptive Network-based Fuzzy Inference Systems (ANFIS) in rainfall–runoff modeling , 2010 .

[4]  Nariman Valizadeh.  Daily water level forecasting using adaptive neuro-fuzzy interface system with different scenarios: Klang Gate, Malaysia , 2011 .

[5]  Ahmed El-Shafie,et al.  Adaptive neuro-fuzzy inference system based model for rainfall forecasting in Klang River, Malaysia , 2011 .

[6]  Ozgur Kisi,et al.  Intermittent Streamflow Forecasting by Using Several Data Driven Techniques , 2011, Water Resources Management.

[7]  Li-Chiu Chang,et al.  Intelligent control for modelling of real‐time reservoir operation , 2001 .

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

[9]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.

[10]  Mehdi Vafakhah,et al.  Application of Several Data-Driven Techniques for Predicting Groundwater Level , 2012, Water Resources Management.

[11]  Mohammad Ali Ghorbani,et al.  Sea water level forecasting using genetic programming and comparing the performance with Artificial Neural Networks , 2010, Comput. Geosci..

[12]  Ozgur Kisi,et al.  Daily pan evaporation modelling using a neuro-fuzzy computing technique , 2006 .

[13]  Paulo Chaves,et al.  Deriving reservoir operational strategies considering water quantity and quality objectives by stochastic fuzzy neural networks , 2007 .

[14]  Han Yan,et al.  Adaptive neuro fuzzy inference system for classification of water quality status. , 2010, Journal of environmental sciences.

[15]  Fi-John Chang,et al.  Adaptive neuro-fuzzy inference system for prediction of water level in reservoir , 2006 .

[16]  Taesoon Kim,et al.  Monthly Precipitation Forecasting with a Neuro-Fuzzy Model , 2012, Water Resources Management.

[17]  Ahmed El-Shafie,et al.  A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam , 2007 .

[18]  Ralf Wieland,et al.  A new library to combine artificial neural networks and support vector machines with statistics and a database engine for application in environmental modeling , 2010, Environ. Model. Softw..

[19]  Asaad Y. Shamseldin,et al.  A non-linear neural network technique for updating of river flow forecasts , 2001 .

[20]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[21]  Jan-Tai Kuo,et al.  A hybrid neural-genetic algorithm for reservoir water quality management. , 2006, Water research.

[22]  Ozgur Kisi,et al.  Estimation of Daily Pan Evaporation Using Two Different Adaptive Neuro-Fuzzy Computing Techniques , 2012, Water Resources Management.

[23]  M. Cobaner.  Evapotranspiration estimation by two different neuro-fuzzy inference systems , 2011 .

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

[25]  Momcilo Markus,et al.  PRECIPITATION-RUNOFF MODELING USING ARTIFICIAL NEURAL NETWORKS AND CONCEPTUAL MODELS , 2000 .