Version 1
: Received: 4 September 2024 / Approved: 10 September 2024 / Online: 10 September 2024 (13:35:34 CEST)
How to cite:
Kamal, S.; Wang, J.; Dewan, M. A. A. Assessing Stream Flows and Dynamics of the Athabasca River Basin Using Machine Learning. Preprints2024, 2024090810. https://doi.org/10.20944/preprints202409.0810.v1
Kamal, S.; Wang, J.; Dewan, M. A. A. Assessing Stream Flows and Dynamics of the Athabasca River Basin Using Machine Learning. Preprints 2024, 2024090810. https://doi.org/10.20944/preprints202409.0810.v1
Kamal, S.; Wang, J.; Dewan, M. A. A. Assessing Stream Flows and Dynamics of the Athabasca River Basin Using Machine Learning. Preprints2024, 2024090810. https://doi.org/10.20944/preprints202409.0810.v1
APA Style
Kamal, S., Wang, J., & Dewan, M. A. A. (2024). Assessing Stream Flows and Dynamics of the Athabasca River Basin Using Machine Learning. Preprints. https://doi.org/10.20944/preprints202409.0810.v1
Chicago/Turabian Style
Kamal, S., Junye Wang and M. Ali Akber Dewan. 2024 "Assessing Stream Flows and Dynamics of the Athabasca River Basin Using Machine Learning" Preprints. https://doi.org/10.20944/preprints202409.0810.v1
Abstract
Streamflow forecasting is of great importance in water resources management and flood warnings. Machine learning techniques can be utilized to assist with river flow forecasting. By analyzing historical time series data on river flows, weather patterns, and other relevant factors, machine-learning models can learn patterns and relationships to present predictions about future river flows. In this study, an Autoregressive Integrated Moving Average (ARIMA) model is constructed to predict the monthly flows of the Athabasca River at three monitoring stations: Hinton, Athabasca, and Fort MacMurray, in Alberta, Canada. The three monitoring stations upstream, midstream, and downstream were selected to represent the different climatological regimes of the Athabasca River. Time series data were used for the model training to identify patterns and correlations using moving averages, exponential smoothing, and Holt-Winters' method. The model's forecasting was compared against the observed data. The results show that the determination coefficients were 0.99 at all three stations, indicating strong correlations. The root mean square errors (RMSEs) were 26.19 at Hinton, 61.1 at Athabasca, and 15.703 at Fort MacMurray, respectively, and the mean absolute percentage errors (MAPEs) were 0.34%, 0.44%, and 0.14%, respectively. Therefore, the ARIMA model captured the seasonality patterns and trends in the stream flows at all three stations and demonstrated a robust performance for hydrological forecasting. This provides insights and predictions for water resources management and flood warnings.
Keywords
river flow model; machine learning; modeling; and simulation
Subject
Computer Science and Mathematics, Computational Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
