Next Article in Journal
Objective Regolith-Landform Mapping in a Regolith Dominated Terrain to Inform Mineral Exploration
Previous Article in Journal
Forensic Investigations of Geohazards: The Norcia 2016 Earthquake
Open AccessArticle

Time Series Regression for Forecasting Flood Events in Schenectady, New York

1
Department of Geology, Environment and Sustainability, Hofstra University, Hempstead, NY 11549, USA
2
Department of Information Systems and Supply Chain Management, Rider University, Lawrenceville, NJ 08648, USA
*
Author to whom correspondence should be addressed.
Geosciences 2018, 8(9), 317; https://doi.org/10.3390/geosciences8090317
Received: 26 July 2018 / Revised: 14 August 2018 / Accepted: 20 August 2018 / Published: 24 August 2018
(This article belongs to the Section Natural Hazards)
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. View Full-Text
Keywords: flood prediction; time series regression; multiple linear regression; time series decomposition; Kolmogorov-Zurbenko filter flood prediction; time series regression; multiple linear regression; time series decomposition; Kolmogorov-Zurbenko filter
Show Figures

Figure 1

MDPI and ACS Style

Plitnick, T.A.; Marsellos, A.E.; Tsakiri, K.G. Time Series Regression for Forecasting Flood Events in Schenectady, New York. Geosciences 2018, 8, 317.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop