Mining Temporal Reservoir Data Using Sliding Window Technique

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.

[1]  Gustavo Rossi,et al.  An approach to discovering temporal association rules , 2000, SAC '00.

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

[3]  L. Sheremetov,et al.  Association networks in time series data mining , 2005, NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society.

[4]  Mireille Samia.  A Representation of Time Series for Temporal Web Mining , 2004, Grundlagen von Datenbanken.

[5]  Chin-Feng Juang.  Construction of dynamic fuzzy if-then rules through genetic reinforcement learning for temporal problems solving , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[6]  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).

[7]  Ari Jolma,et al.  Fuzzy Model for Real-Time Reservoir Operation , 2002 .

[8]  Laura Firoiu,et al.  Clustering Time Series with Hidden Markov Models and Dynamic Time Warping , 1999 .

[9]  Slobodan P. Simonovic,et al.  Reservoir Systems Analysis: Closing Gap between Theory and Practice , 1992 .

[10]  Richard J. Povinelli,et al.  Identifying Temporal Patterns for Characterization and Prediction of Financial Time Series Events , 2000, TSDM.

[11]  Cláudia Antunes,et al.  Temporal Data Mining: an overview , 2001 .

[12]  Andrew R. Post,et al.  Temporal data mining. , 2008, Clinics in laboratory medicine.

[13]  Mehmet A. Orgun,et al.  An Overview Of Temporal Data Mining , 2002, AusDM.

[14]  P. S. Sastry,et al.  A survey of temporal data mining , 2006 .

[15]  Richard J. Povinelli,et al.  A Temporal Pattern Approach for Predicting Weekly Financial Time Series , 2003 .

[16]  Berthe Y. Choueiry,et al.  A new effcient algorithm for solving the simple temporal problem , 2003, 10th International Symposium on Temporal Representation and Reasoning, 2003 and Fourth International Conference on Temporal Logic. Proceedings..

[17]  Richard J. Povinelli,et al.  Time series classification using Gaussian mixture models of reconstructed phase spaces , 2004, IEEE Transactions on Knowledge and Data Engineering.

[18]  Ku Ruhana Ku-Mahamud,et al.  Conceptual model of intelligent decision support system based on naturalistic decision theory for reservoir operation during emergency situation , 2011 .

[19]  Raul Luís Monteiro Moisão.  Prediction Model , Based on Neural Networks , for Time Series with Origin in Chaotic Systems , 2001 .

[20]  Andreas D. Lattner,et al.  Unsupervised Learning of Sequential Patterns , 2004 .

[21]  Francisco Guil,et al.  TSET MAX : An Algorithm for Mining Frequent Maximal Temporal Patterns , 2004 .

[22]  B. Kinney,et al.  A Computational Model for Simulating Spatial and Temporal Aspects of Crime in Urban Environments , 2022 .

[23]  Richard J. Povinelli,et al.  A New Temporal Pattern Identification Method for Characterization and Prediction of Complex Time Series Events , 2003, IEEE Trans. Knowl. Data Eng..

[24]  John F. Roddick,et al.  A Survey of Temporal Knowledge Discovery Paradigms and Methods , 2002, IEEE Trans. Knowl. Data Eng..

[25]  Komal Singh,et al.  A computational model for simulating spatial aspects of crime in urban environments , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[26]  Suh-Yin Lee,et al.  Mining frequent itemsets over data streams using efficient window sliding techniques , 2009, Expert Syst. Appl..

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

[28]  Theophano Mitsa,et al.  Temporal Data Mining , 2010 .

[29]  Richard J. Povinelli,et al.  TEMPORAL PATTERN IDENTIFICATION OF TIME SERIES DATA USING PATTERN WAVELETS AND GENETIC ALGORITHMS , 1999 .

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

[31]  Margaret H. Dunham,et al.  Data Mining: Introductory and Advanced Topics , 2002 .

[32]  Younès Bennani,et al.  New Self-organizing Maps for Multivariate Sequences Processing , 2005, Int. J. Comput. Intell. Appl..

[33]  John F. Roddick,et al.  Discovering Richer Temporal Association Rules from Interval-Based Data , 2005, DaWaK.