Seasonal flow forecasting of Newfoundland rivers
The general purpose of forecasting is to provide the best estimates of what will happen at specified points in time in the future. In hydrology, for example, forecasts of riverflows are often used for operational planning of reservoir and flood control systems. Since, even modest improvements in the...
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Memorial University of Newfoundland
1995
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ftmemorialuniv:oai:research.library.mun.ca:5412 2023-10-01T03:57:34+02:00 Seasonal flow forecasting of Newfoundland rivers Sidhu, Anjana 1995 application/pdf https://research.library.mun.ca/5412/ https://research.library.mun.ca/5412/1/Sidhu_Anjana.pdf https://research.library.mun.ca/5412/2/Sidhu_Anjana.pdf en eng Memorial University of Newfoundland https://research.library.mun.ca/5412/1/Sidhu_Anjana.pdf https://research.library.mun.ca/5412/2/Sidhu_Anjana.pdf Sidhu, Anjana <https://research.library.mun.ca/view/creator_az/Sidhu=3AAnjana=3A=3A.html> (1995) Seasonal flow forecasting of Newfoundland rivers. Masters thesis, Memorial University of Newfoundland. thesis_license Thesis NonPeerReviewed 1995 ftmemorialuniv 2023-09-03T06:45:17Z The general purpose of forecasting is to provide the best estimates of what will happen at specified points in time in the future. In hydrology, for example, forecasts of riverflows are often used for operational planning of reservoir and flood control systems. Since, even modest improvements in the operation of a large reservoir system can result in multi-million dollar savings per year, choosing a model which produces reliable and accurate forecasts is therefore essential to the efficient operation of the system. In this study, monthly and quarterly discharge data of Newfoundland rivers were used to forecast future flows using four different statistical approaches: conventional Box and Jenkins's autoregressive integrated moving average (ARIMA), exponential smoothing, periodic autoregressive (PAR), and Harvey's new structural time series (NSM). Each monthly riverflow data was divided into three short term series to study forecasting accuracy. Ten quarterly series were used to predict flows for three forecasting scenarios and thirty monthly series were considered for 3 month, 6 month, 9 month and 12 month ahead forecast horizons. Forecast performance was assessed using the mean absolute percentage error (MAPE) criterion. -- Based on the MAPE criterion, it is concluded that forecasts using the NSM approach for short term monthly riverflow data in general are better than ARIMA, exponential smoothing and PAR approaches. For quarterly data, forecasts using the exponential smoothing approach in general are better than NSM, ARIMA and PAR approaches. Thesis Newfoundland Memorial University of Newfoundland: Research Repository |
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Memorial University of Newfoundland: Research Repository |
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English |
description |
The general purpose of forecasting is to provide the best estimates of what will happen at specified points in time in the future. In hydrology, for example, forecasts of riverflows are often used for operational planning of reservoir and flood control systems. Since, even modest improvements in the operation of a large reservoir system can result in multi-million dollar savings per year, choosing a model which produces reliable and accurate forecasts is therefore essential to the efficient operation of the system. In this study, monthly and quarterly discharge data of Newfoundland rivers were used to forecast future flows using four different statistical approaches: conventional Box and Jenkins's autoregressive integrated moving average (ARIMA), exponential smoothing, periodic autoregressive (PAR), and Harvey's new structural time series (NSM). Each monthly riverflow data was divided into three short term series to study forecasting accuracy. Ten quarterly series were used to predict flows for three forecasting scenarios and thirty monthly series were considered for 3 month, 6 month, 9 month and 12 month ahead forecast horizons. Forecast performance was assessed using the mean absolute percentage error (MAPE) criterion. -- Based on the MAPE criterion, it is concluded that forecasts using the NSM approach for short term monthly riverflow data in general are better than ARIMA, exponential smoothing and PAR approaches. For quarterly data, forecasts using the exponential smoothing approach in general are better than NSM, ARIMA and PAR approaches. |
format |
Thesis |
author |
Sidhu, Anjana |
spellingShingle |
Sidhu, Anjana Seasonal flow forecasting of Newfoundland rivers |
author_facet |
Sidhu, Anjana |
author_sort |
Sidhu, Anjana |
title |
Seasonal flow forecasting of Newfoundland rivers |
title_short |
Seasonal flow forecasting of Newfoundland rivers |
title_full |
Seasonal flow forecasting of Newfoundland rivers |
title_fullStr |
Seasonal flow forecasting of Newfoundland rivers |
title_full_unstemmed |
Seasonal flow forecasting of Newfoundland rivers |
title_sort |
seasonal flow forecasting of newfoundland rivers |
publisher |
Memorial University of Newfoundland |
publishDate |
1995 |
url |
https://research.library.mun.ca/5412/ https://research.library.mun.ca/5412/1/Sidhu_Anjana.pdf https://research.library.mun.ca/5412/2/Sidhu_Anjana.pdf |
genre |
Newfoundland |
genre_facet |
Newfoundland |
op_relation |
https://research.library.mun.ca/5412/1/Sidhu_Anjana.pdf https://research.library.mun.ca/5412/2/Sidhu_Anjana.pdf Sidhu, Anjana <https://research.library.mun.ca/view/creator_az/Sidhu=3AAnjana=3A=3A.html> (1995) Seasonal flow forecasting of Newfoundland rivers. Masters thesis, Memorial University of Newfoundland. |
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thesis_license |
_version_ |
1778529169345347584 |