Leveraging advanced deep learning and machine learning approaches for snow depth prediction using remote sensing and ground data

Abstract

The study area encompasses two distinct sub-basins within the High Atlas Mountains: Oukaimeden in the Rheraya and Tichki in the Mgoun Valley. 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. 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. • ML and DL models predict daily snow depth in the Atlas Mountains using remote sensing data. • Combining meteorological and remote sensing data enhances model accuracy. • SVR performed best with RMSE of 2-5 cm and R² of 0.97.