Abstract
This research introduces a hybrid model for forecasting river flood events with an example of the Mohawk River in New York. Time series analysis and artificial neural networks are combined for the explanation and forecasting of the daily water discharge using hydrogeological and climatic variables. A low pass filter (Kolmogorov–Zurbenko filter) is applied for the decomposition of the time series into different components (long, seasonal, and short-term components). For the prediction of the water discharge time series, each component has been described by applying the multiple linear regression models (MLR), and the artificial neural network (ANN) model. The MLR retains the advantage of the physical interpretation of the water discharge time series. We prove that time series decomposition is essential before the application of any model. Also, decomposition shows that the Mohawk River is affected by multiple time scale components that contribute to the hydrologic cycle of the included watersheds. Comparison of the models proves that the application of the ANN on the decomposed time series improves the accuracy of forecasting flood events. The hybrid model which consists of time series decomposition and artificial neural network leads to a forecasting up to 96% of the explanation for the water discharge time series.
Highlights
Worldwide, flood events are the most significant natural hazards yielding severe consequences to the socio-economic structure with damage up to billions of dollars [1,2,3]
An increase in the frequency of irregular flood events has been observed with climatic changes [5,6,7,8,9], and prediction of the flood events is required in highly occupied regions such as in New York State
To evaluate the effectiveness of the multiple linear regression (MLR) model in river flood prediction, we estimate the coefficient of determination, R2, for the raw time series data and the components of the time series
Summary
Flood events are the most significant natural hazards yielding severe consequences to the socio-economic structure with damage up to billions of dollars [1,2,3]. In upstate New York along the Mohawk River and other tributaries, flood events are widespread and persistent. Flood forecasting research has been conducted in the past at Schenectady in which flood forecasting and damage evaluation has been surveyed [12,13,14,15,16,17]
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