Intelligent Decision Support Model Based on Neural Network to Support Reservoir Water Release Decision

Reservoir is one of the emergency environments that required fast an accurate decision to reduce flood risk during heavy rainfall and contain water during less rainfall. Typically, during heavy rainfall, the water level increase very fast, thus decision of the water release is timely and crucial task. In this paper, intelligent decision support model based on neural network (NN) is proposed. The proposed model consists of situation assessment, forecasting and decision models. Situation assessment utilized temporal data mining technique to extract relevant data and attribute from the reservoir operation record. The forecasting model utilize NN to perform forecasting of the reservoir water level, while in the decision model, NN is applied to perform classification of the current and changes of reservoir water level. The simulations have shown that the performances of NN for both forecasting and decision models are acceptably good.

[1]  Keith Smith,et al.  Floods: Physical Processes and Human Impacts , 1998 .

[2]  Jack Dongarra,et al.  Computational Science - ICCS 2005, 5th International Conference, Atlanta, GA, USA, May 22-25, 2005, Proceedings, Part I , 2005, International Conference on Computational Science.

[3]  M Ghodsian,et al.  River flow forecasting using artificial neural networks , 2004 .

[4]  Guido Calenda,et al.  The role of the Corbara Reservoir on the Tiber River in the flood protection of the Town of Rome, Italy , 1999 .

[5]  Dimitri P. Solomatine,et al.  Control of water levels in polder areas using neural networks and fuzzy adaptive systems , 1999 .

[6]  Ku Ruhana Ku-Mahamud,et al.  Flood Pattern Detection Using Sliding Window Technique , 2009, 2009 Third Asia International Conference on Modelling & Simulation.

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

[8]  Akira Kawamura,et al.  Neural Networks for Rainfall Forecasting by Atmospheric Downscaling , 2004 .

[9]  Renu Sinha.  Impact of experience on decision making in emergency situation , 2005 .

[10]  Fi-John Chang,et al.  Intelligent reservoir operation system based on evolving artificial neural networks , 2008 .

[11]  J. C. Jia,et al.  Assessments of neural network output codings for classification of multispectral images using Hamming distance measure , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5).

[12]  Slobodan P. Simonovic,et al.  DECISION SUPPORT SYSTEM FOR FLOOD MANAGEMENT IN THE RED RIVER BASIN , 1999 .

[13]  W Grabs,et al.  Integrated flood management. , 2007, Water science and technology : a journal of the International Association on Water Pollution Research.

[14]  Bingru Yang,et al.  Overview of the New Types of Intelligent Decision Support System , 2008, 2008 3rd International Conference on Innovative Computing Information and Control.

[15]  Yen-Chang Chen,et al.  A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction , 2001 .

[16]  Ozgur Kisi,et al.  River Flow Modeling Using Artificial Neural Networks , 2004 .

[17]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[18]  Warren S. Sarle,et al.  Stopped Training and Other Remedies for Overfitting , 1995 .

[19]  Gloria Phillips-Wren,et al.  Adaptive Decision Support for Dynamic Environments , 2009 .

[20]  A. Pritchett,et al.  Design of Support Systems for Dynamic Decision Making in Airline Operations , 2006, 2006 IEEE Systems and Information Engineering Design Symposium.

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

[22]  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 .

[23]  Donald E. Brown,et al.  Intelligent decision support systems , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[24]  Ralph A. Wurbs.  Reservoir‐System Simulation and Optimization Models , 1993 .

[25]  Margo I. Seltzer,et al.  A Dynamic, Data-Driven, Decision Support System for Emergency Medical Services , 2005, International Conference on Computational Science.

[26]  Tiao J. Chang,et al.  Use of Flood-Control Reservoirs for Drought Management , 1995 .

[27]  Vijay P. Singh,et al.  Chapter 11 - Reservoir Operation , 2003 .

[28]  B. Bobée,et al.  Multivariate Reservoir Inflow Forecasting Using Temporal Neural Networks , 2001 .

[29]  K. Lam,et al.  River flow time series prediction with a range-dependent neural network , 2001 .

[30]  Red Crescent Societies.  Stay Safe: International Federation of Red Cross and Red Crescent Societies (IFRC) guide to a safer mission , 2007 .