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Mining temporal reservoir data using sliding window technique


Wan Ishak, Wan Hussain and Ku-Mahamud, Ku Ruhana and Md Norwawi, Norita (2011) Mining temporal reservoir data using sliding window technique. CiiT International Journal of Data Mining and Knowledge Engineering, 3 (8). pp. 473-478. ISSN 0974–9683 (Print); 0974–9578 (Online)

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Abstract

Decision on reservoir water release is crucial during both intense and less intense rainfall seasons. Even though reservoir water release is guided by the procedures, decision usually made based on the past experiences. Past experiences are recorded either hourly, daily, or weekly in the reservoir operation log book. In a few years this log book will become knowledge-rich repository, but very difficult and time consuming to be referred. In addition, the temporal relationship between the data cannot be easily identified.In this study window sliding technique is applied to extract information from the reservoir operational database: a digital version of the reservoir operation log book.Several data sets were constructed based on different sliding window size. Artificial neural network was used as modelling tool.The findings indicate that eight days is the significant time lags between upstream rainfall and reservoir water level.The best artificial neural network model is 24-15-3.

Item Type: Article
Uncontrolled Keywords: Neural Network, Sliding Window, Reservoir Management, Reservoir Water Level, Temporal Data Mining
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: College of Arts and Sciences
Depositing User: Prof. Dr. Ku Ruhana Ku Mahamud
Date Deposited: 02 Oct 2011 01:33
Last Modified: 16 Jan 2013 02:52
URI: http://repo.uum.edu.my/id/eprint/3988

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