Leveraging advanced deep learning and machine learning approaches for snow depth prediction using remote sensing and ground data
Study regions: The study area encompasses two distinct sub-basins within the High Atlas Mountains: Oukaimeden in the Rheraya and Tichki in the Mgoun Valley. Study focus: The research integrates remote sensing data, particularly the Normalized-Difference Snow Index (NDSI) from the MODIS Sensor, with...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Journal of Hydrology: Regional Studies |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581824004348 |
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| author | Haytam Elyoussfi Abdelghani Boudhar Salwa Belaqziz Mostafa Bousbaa Karima Nifa Bouchra Bargam Abdelghani Chehbouni |
| author_facet | Haytam Elyoussfi Abdelghani Boudhar Salwa Belaqziz Mostafa Bousbaa Karima Nifa Bouchra Bargam Abdelghani Chehbouni |
| author_sort | Haytam Elyoussfi |
| collection | DOAJ |
| description | Study regions: The study area encompasses two distinct sub-basins within the High Atlas Mountains: Oukaimeden in the Rheraya and Tichki in the Mgoun Valley. Study focus: The research integrates remote sensing data, particularly the Normalized-Difference Snow Index (NDSI) from the MODIS Sensor, with machine learning (ML) and deep learning (DL) models to predict daily snow depth (DSD) at a local scale. The models evaluated include two ML approaches: Support Vector Regression (SVR) and eXtreme Gradient Boosting (XGBoost) and four DL models: 1-Dimensional Convolutional Neural Network (1D-CNN), Long Short-Term Memory Networks (LSTM), Gated Recurrent Unit (GRU), and Bi-directional Long Short-Term Memory Network (Bi-LSTM). The dataset was processed and normalized for optimal performance, and hyperparameters were fine-tuned using a randomized search method. New hydrological insights for the region: The Results highlight the efficacy of AI-based approaches for snow depth prediction, with SVR achieving the best performance (Root Mean Square Error of 2–5 cm and an average coefficient of determination of 0.97). This study reveals that incorporating lag times of snow depth data significantly enhances predictive accuracy. These findings underscore the potential of integrating remote sensing with AI techniques to improve hydrological modeling and water resource planning in data-scarce regions like the Atlas Mountains. |
| format | Article |
| id | doaj-art-53908f3f1e4e46069714f5ed7ea67cc2 |
| institution | Kabale University |
| issn | 2214-5818 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj-art-53908f3f1e4e46069714f5ed7ea67cc22025-01-22T05:41:58ZengElsevierJournal of Hydrology: Regional Studies2214-58182025-02-0157102085Leveraging advanced deep learning and machine learning approaches for snow depth prediction using remote sensing and ground dataHaytam Elyoussfi0Abdelghani Boudhar1Salwa Belaqziz2Mostafa Bousbaa3Karima Nifa4Bouchra Bargam5Abdelghani Chehbouni6Center for Remote Sensing Applications (CRSA), Mohammed VI Polytechnic University (UM6P), Benguerir 43150, Morocco; Corresponding author.Center for Remote Sensing Applications (CRSA), Mohammed VI Polytechnic University (UM6P), Benguerir 43150, Morocco; Data4Earth Laboratory, Faculty of Sciences and Technics, Sultan Moulay Slimane University, Beni Mellal 23000, Morocco; Faculty of Sciences and Techniques, Cadi Ayyad University, Marrakech, MoroccoCenter for Remote Sensing Applications (CRSA), Mohammed VI Polytechnic University (UM6P), Benguerir 43150, Morocco; Laboratory of Computer Systems and Vision (LabSIV), Department of Computer Science, Faculty of Science, Ibn Zohr University, Agadir 80000, MoroccoCenter for Remote Sensing Applications (CRSA), Mohammed VI Polytechnic University (UM6P), Benguerir 43150, MoroccoData4Earth Laboratory, Faculty of Sciences and Technics, Sultan Moulay Slimane University, Beni Mellal 23000, MoroccoCenter for Remote Sensing Applications (CRSA), Mohammed VI Polytechnic University (UM6P), Benguerir 43150, MoroccoCenter for Remote Sensing Applications (CRSA), Mohammed VI Polytechnic University (UM6P), Benguerir 43150, MoroccoStudy regions: The study area encompasses two distinct sub-basins within the High Atlas Mountains: Oukaimeden in the Rheraya and Tichki in the Mgoun Valley. Study focus: The research integrates remote sensing data, particularly the Normalized-Difference Snow Index (NDSI) from the MODIS Sensor, with machine learning (ML) and deep learning (DL) models to predict daily snow depth (DSD) at a local scale. The models evaluated include two ML approaches: Support Vector Regression (SVR) and eXtreme Gradient Boosting (XGBoost) and four DL models: 1-Dimensional Convolutional Neural Network (1D-CNN), Long Short-Term Memory Networks (LSTM), Gated Recurrent Unit (GRU), and Bi-directional Long Short-Term Memory Network (Bi-LSTM). The dataset was processed and normalized for optimal performance, and hyperparameters were fine-tuned using a randomized search method. New hydrological insights for the region: The Results highlight the efficacy of AI-based approaches for snow depth prediction, with SVR achieving the best performance (Root Mean Square Error of 2–5 cm and an average coefficient of determination of 0.97). This study reveals that incorporating lag times of snow depth data significantly enhances predictive accuracy. These findings underscore the potential of integrating remote sensing with AI techniques to improve hydrological modeling and water resource planning in data-scarce regions like the Atlas Mountains.http://www.sciencedirect.com/science/article/pii/S2214581824004348Water resource researchSemi-arid mountainsDaily snow depth predictionMachine learningDeep learningMorocco |
| spellingShingle | Haytam Elyoussfi Abdelghani Boudhar Salwa Belaqziz Mostafa Bousbaa Karima Nifa Bouchra Bargam Abdelghani Chehbouni Leveraging advanced deep learning and machine learning approaches for snow depth prediction using remote sensing and ground data Journal of Hydrology: Regional Studies Water resource research Semi-arid mountains Daily snow depth prediction Machine learning Deep learning Morocco |
| title | Leveraging advanced deep learning and machine learning approaches for snow depth prediction using remote sensing and ground data |
| title_full | Leveraging advanced deep learning and machine learning approaches for snow depth prediction using remote sensing and ground data |
| title_fullStr | Leveraging advanced deep learning and machine learning approaches for snow depth prediction using remote sensing and ground data |
| title_full_unstemmed | Leveraging advanced deep learning and machine learning approaches for snow depth prediction using remote sensing and ground data |
| title_short | Leveraging advanced deep learning and machine learning approaches for snow depth prediction using remote sensing and ground data |
| title_sort | leveraging advanced deep learning and machine learning approaches for snow depth prediction using remote sensing and ground data |
| topic | Water resource research Semi-arid mountains Daily snow depth prediction Machine learning Deep learning Morocco |
| url | http://www.sciencedirect.com/science/article/pii/S2214581824004348 |
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