Data Mining In Reservoir Operation And Flood Control Using Artificial Neural Networks
Ramani Bai. V, and Woo, Chaw Seng, and Faridha Othman, and Ramadas, Gopinath, (2007) Data Mining In Reservoir Operation And Flood Control Using Artificial Neural Networks. In: Research Excellence and Knowledge Enrichment in ICT: Proceeding of the 2nd International Conference on Informatics, 27th - 28th November 2007, Petaling Jaya, Selangor, Malaysia.
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University of Malaya, Faculty of Engineering, Dept. of Civil and Environmental Engg.
University of Malaya, Faculty of Computer Science & Information Technology
Artificial neural networks have strong data fitting capability. In domains where explaining rules are critical, such as release of water from dam, denying loan applications etc., classical neural networks are not the tools of choice. ‘Neural Networks Cannot Explain Results’. This is the biggest criticism directed at neural networks or a challenge directed at using neural networks in water resources engineering. The main goal of this research is through processing of data (records from the past) to describe the underlying dynamics of the complex systems and predict its future. One of the solutions is data mining that is sorting through data to identify patterns and establish relationships. Using the best represented data from several previous time steps, a more complex data-driven model on artificial neural network can be built. A problem related to operation of water reservoir is selected to provide a better data representation to the network to evolve better results and continuity of the system performance compared to earlier works. This paper
illustrates an artificial developmental system that is a computationally efficient technique for the automatic generation of complex Artificial Neural Network (ANN), which can better represent all the features of the data. MLFFNN models of operation of dam are developed by representing the training set to the network in following four different forms viz. 1)Historical data without optimization given as monthly (HANN), 2)Classical method of optimized results given as monthly (ANN), 3)Implicit method with optimized results given as yearly (IANN), and 4) Explicit method with optimized results given as monthly (EANN). Results have shown that neural network estimates are sensitive to sample representation, but are robust in terms of network architecture. Also the comparison to conventional statistical models, show the superiority of this approach of using ANN. In addition, this research offers an effective and reliable approach that can point out the best direction for maintaining continuity course of operation and hence with significant benefit to the decision makers on water release from the dam. The presented approach to model approximation may be used in
various schemes of water resources optimization.
|Item Type:||Conference or Workshop Item (Paper)|
|Keywords:||Optimization, reservoir operation, multi-layer feed forward network, data mining, training, validation, training set and flood control.|
|Subjects:||Q Science, Computer Science|
T Technology, Engineering
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