Harnessing Deep Learning and Snow Cover Data for Enhanced Runoff Prediction in Snow-Dominated Watersheds
Predicting streamflow is essential for managing water resources, especially in basins and watersheds where snowmelt plays a major role in river discharge. This study evaluates the advanced deep learning models for accurate monthly and peak streamflow forecasting in the Gilgit River Basin. The models...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/15/12/1407 |
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| author | Rana Muhammad Adnan Wang Mo Ozgur Kisi Salim Heddam Ahmed Mohammed Sami Al-Janabi Mohammad Zounemat-Kermani |
| author_facet | Rana Muhammad Adnan Wang Mo Ozgur Kisi Salim Heddam Ahmed Mohammed Sami Al-Janabi Mohammad Zounemat-Kermani |
| author_sort | Rana Muhammad Adnan |
| collection | DOAJ |
| description | Predicting streamflow is essential for managing water resources, especially in basins and watersheds where snowmelt plays a major role in river discharge. This study evaluates the advanced deep learning models for accurate monthly and peak streamflow forecasting in the Gilgit River Basin. The models utilized were LSTM, BiLSTM, GRU, CNN, and their hybrid combinations (CNN-LSTM, CNN-BiLSTM, CNN-GRU, and CNN-BiGRU). Our research measured the model’s accuracy through root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and the coefficient of determination (R<sup>2</sup>). The findings indicated that the hybrid models, especially CNN-BiGRU and CNN-BiLSTM, achieved much better performance than traditional models like LSTM and GRU. For instance, CNN-BiGRU achieved the lowest RMSE (71.6 in training and 95.7 in testing) and the highest R<sup>2</sup> (0.962 in training and 0.929 in testing). A novel aspect of this research was the integration of MODIS-derived snow-covered area (SCA) data, which enhanced model accuracy substantially. When SCA data were included, the CNN-BiLSTM model’s RMSE improved from 83.6 to 71.6 during training and from 108.6 to 95.7 during testing. In peak streamflow prediction, CNN-BiGRU outperformed other models with the lowest absolute error (108.4), followed by CNN-BiLSTM (144.1). This study’s results reinforce the notion that combining CNN’s spatial feature extraction capabilities with the temporal dependencies captured by LSTM or GRU significantly enhances model accuracy. The demonstrated improvements in prediction accuracy, especially for extreme events, highlight the potential for these models to support more informed decision-making in flood risk management and water allocation. |
| format | Article |
| id | doaj-art-0168e59f45f340e0bac281ed1822d1b4 |
| institution | Kabale University |
| issn | 2073-4433 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-0168e59f45f340e0bac281ed1822d1b42024-12-27T14:09:39ZengMDPI AGAtmosphere2073-44332024-11-011512140710.3390/atmos15121407Harnessing Deep Learning and Snow Cover Data for Enhanced Runoff Prediction in Snow-Dominated WatershedsRana Muhammad Adnan0Wang Mo1Ozgur Kisi2Salim Heddam3Ahmed Mohammed Sami Al-Janabi4Mohammad Zounemat-Kermani5College of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, ChinaCollege of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, ChinaDepartment of Civil Engineering, Lübeck University of Applied Science, 23562 Lübeck, GermanyHydraulics Division, Agronomy Department, Faculty of Science, University20 Août 1955 Skikda, Route El Hadaik, BP 26, Skikda 21024, AlgeriaDepartment of Civil Engineering, Cihan University-Erbil, Kurdistan Region, Erbil 44001, IraqDepartment of Water Engineering, Shahid Bahonar University of Kerman, Kerman 76169-14111, IranPredicting streamflow is essential for managing water resources, especially in basins and watersheds where snowmelt plays a major role in river discharge. This study evaluates the advanced deep learning models for accurate monthly and peak streamflow forecasting in the Gilgit River Basin. The models utilized were LSTM, BiLSTM, GRU, CNN, and their hybrid combinations (CNN-LSTM, CNN-BiLSTM, CNN-GRU, and CNN-BiGRU). Our research measured the model’s accuracy through root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and the coefficient of determination (R<sup>2</sup>). The findings indicated that the hybrid models, especially CNN-BiGRU and CNN-BiLSTM, achieved much better performance than traditional models like LSTM and GRU. For instance, CNN-BiGRU achieved the lowest RMSE (71.6 in training and 95.7 in testing) and the highest R<sup>2</sup> (0.962 in training and 0.929 in testing). A novel aspect of this research was the integration of MODIS-derived snow-covered area (SCA) data, which enhanced model accuracy substantially. When SCA data were included, the CNN-BiLSTM model’s RMSE improved from 83.6 to 71.6 during training and from 108.6 to 95.7 during testing. In peak streamflow prediction, CNN-BiGRU outperformed other models with the lowest absolute error (108.4), followed by CNN-BiLSTM (144.1). This study’s results reinforce the notion that combining CNN’s spatial feature extraction capabilities with the temporal dependencies captured by LSTM or GRU significantly enhances model accuracy. The demonstrated improvements in prediction accuracy, especially for extreme events, highlight the potential for these models to support more informed decision-making in flood risk management and water allocation.https://www.mdpi.com/2073-4433/15/12/1407streamflow forecastinglong short-term memorybidirectional long short-term memorygated recurrent unitbidirectional gated recurrent unitconvolutional neural network |
| spellingShingle | Rana Muhammad Adnan Wang Mo Ozgur Kisi Salim Heddam Ahmed Mohammed Sami Al-Janabi Mohammad Zounemat-Kermani Harnessing Deep Learning and Snow Cover Data for Enhanced Runoff Prediction in Snow-Dominated Watersheds Atmosphere streamflow forecasting long short-term memory bidirectional long short-term memory gated recurrent unit bidirectional gated recurrent unit convolutional neural network |
| title | Harnessing Deep Learning and Snow Cover Data for Enhanced Runoff Prediction in Snow-Dominated Watersheds |
| title_full | Harnessing Deep Learning and Snow Cover Data for Enhanced Runoff Prediction in Snow-Dominated Watersheds |
| title_fullStr | Harnessing Deep Learning and Snow Cover Data for Enhanced Runoff Prediction in Snow-Dominated Watersheds |
| title_full_unstemmed | Harnessing Deep Learning and Snow Cover Data for Enhanced Runoff Prediction in Snow-Dominated Watersheds |
| title_short | Harnessing Deep Learning and Snow Cover Data for Enhanced Runoff Prediction in Snow-Dominated Watersheds |
| title_sort | harnessing deep learning and snow cover data for enhanced runoff prediction in snow dominated watersheds |
| topic | streamflow forecasting long short-term memory bidirectional long short-term memory gated recurrent unit bidirectional gated recurrent unit convolutional neural network |
| url | https://www.mdpi.com/2073-4433/15/12/1407 |
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