Time Series Regression for Forecasting Flood Events in Schenectady, New York
| Autor: | Katerina Tsakiri, Thomas A. Plitnick, Antonios E Marsellos |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
multiple linear regression
Hydrogeology 010504 meteorology & atmospheric sciences Flood myth 0208 environmental biotechnology lcsh:QE1-996.5 Regression analysis 02 engineering and technology flood prediction 01 natural sciences 020801 environmental engineering lcsh:Geology Climatology Linear regression General Earth and Planetary Sciences Environmental science Kolmogorov-Zurbenko filter Precipitation Time series time series decomposition time series regression Kolmogorov–Zurbenko filter Decomposition of time series 0105 earth and related environmental sciences |
| Zdroj: | Geosciences, Vol 8, Iss 9, p 317 (2018) Geosciences Volume 8 Issue 9 |
| ISSN: | 2076-3263 |
| Popis: | Floods typically occur due to ice jams in the winter or extended periods of precipitation in the spring and summer seasons. An increase in the rate of water discharge in the river coincides with a flood event. This research combines the time series decomposition and the time series regression model for the flood prediction in Mohawk River at Schenectady, New York. The time series decomposition has been applied to separate the different frequencies in hydrogeological and climatic data. The time series data have been decomposed into the long-term, seasonal-term, and short-term components using the Kolmogorov-Zurbenko filter. For the application of the time series regression model, we determine the lags of the hydrogeological and climatic variables that provide the maximum performance for the model. The lags applied in the predictor variables of the model have been used for the physical interpretation of the model to strengthen the relationship between the water discharge and the climatic and hydrogeological variables. The overall model accuracy has been increased up to 73%. The results show that using the lags of the variables in the time regression model, and the forecasting accuracy has been increased compared to the raw data by two times. |
| Databáze: | OpenAIRE |
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