Degree Granting Department
Ali Yalcin, Ph.D.
Bruce Lindsey, Ph.D.
José L. Zayas-Castro, Ph.D.
Phase space reconstruction, Flood prediction, Nonlinear time series prediction, Clustering, Genetic algorithm, Event prediction
Earthquakes, floods, rainfall represent a class of nonlinear systems termed chaotic, in which the relationships between variables in a system are dynamic and disproportionate, however completely deterministic. Classical linear time series models have proved inadequate in analysis and prediction of complex geophysical phenomena. Nonlinear approaches such as Artificial Neural Networks, Hidden Markov Models and Nonlinear Prediction are useful in forecasting of daily discharge values in a river. The focus of these methods is on forecasting magnitudes of future discharge values and not the prediction of floods. Chaos theory provides a structured explanation for irregular behavior and anomalies in systems that are not inherently stochastic. Time Series Data Mining methodology combines chaos theory and data mining to characterize and predict complex, nonperiodic and chaotic time series. Time Series Data Mining focuses on the prediction of events. Floods constitute the events in a river daily discharge time series. This research focuses on application of the Time Series Data Mining to prediction of floods.
Scholar Commons Citation
Damle, Chaitanya, "Flood Forecasting Using Time Series Data Mining" (2005). Graduate Theses and Dissertations.