Artificial Neural Network and Multiple Linear Regression for Flood Prediction in Mohawk River, New York
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....
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-08-01
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| Series: | Water |
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| Online Access: | http://www.mdpi.com/2073-4441/10/9/1158 |
| _version_ | 1811235024404480000 |
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| author | Katerina Tsakiri Antonios Marsellos Stelios Kapetanakis |
| author_facet | Katerina Tsakiri Antonios Marsellos Stelios Kapetanakis |
| author_sort | Katerina Tsakiri |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T11:45:16Z |
| format | Article |
| id | doaj.art-9c3c620de0ee48eebc7c12eba3a4a3c5 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-04-12T11:45:16Z |
| publishDate | 2018-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-9c3c620de0ee48eebc7c12eba3a4a3c52022-12-22T03:34:24ZengMDPI AGWater2073-44412018-08-01109115810.3390/w10091158w10091158Artificial Neural Network and Multiple Linear Regression for Flood Prediction in Mohawk River, New YorkKaterina Tsakiri0Antonios Marsellos1Stelios Kapetanakis2Department of Information Systems and Supply Chain Management, Rider University, Lawrenceville, NJ 08648, USADepartment of Geology, Environment, and Sustainability, Hofstra University, Hempstead, NY 11549, USASchool of Computing, Engineering, and Mathematics, University of Brighton, Brighton BN24GJ, UKThis 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.http://www.mdpi.com/2073-4441/10/9/1158artificial neural networktime series decompositionflood predictionKolmogorov–Zurbenko filterMohawk River |
| spellingShingle | Katerina Tsakiri Antonios Marsellos Stelios Kapetanakis Artificial Neural Network and Multiple Linear Regression for Flood Prediction in Mohawk River, New York Water artificial neural network time series decomposition flood prediction Kolmogorov–Zurbenko filter Mohawk River |
| title | Artificial Neural Network and Multiple Linear Regression for Flood Prediction in Mohawk River, New York |
| title_full | Artificial Neural Network and Multiple Linear Regression for Flood Prediction in Mohawk River, New York |
| title_fullStr | Artificial Neural Network and Multiple Linear Regression for Flood Prediction in Mohawk River, New York |
| title_full_unstemmed | Artificial Neural Network and Multiple Linear Regression for Flood Prediction in Mohawk River, New York |
| title_short | Artificial Neural Network and Multiple Linear Regression for Flood Prediction in Mohawk River, New York |
| title_sort | artificial neural network and multiple linear regression for flood prediction in mohawk river new york |
| topic | artificial neural network time series decomposition flood prediction Kolmogorov–Zurbenko filter Mohawk River |
| url | http://www.mdpi.com/2073-4441/10/9/1158 |
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