Transfer Learning Based CNN And LSTM Models For Water Body Identification And Water Level Forecasting

Abstract

Water bodies identification and Water level prediction is very important for water resource management to lower the risk of flood, and to protect the environment. This research tries to explore how deep learning and transfer learning can be used to perform these tasks by using satellite images. The primary challenge for accurately predicting water levels and identifying water bodies in satellite images is the temporal variations in weather conditions. Convolutional neural networks (CNN) are efficient while working with images. Long-Short-Term Memory (LSTM) models are also good at prediction of water levels. For water body identification, CNN-based models achieved upto 95% accuracy and for Water level prediction LSTM networks also reached upto 95% accuracy. Transfer learning is a machine learning technique where a model trained on one task can be reused as the foundation for a another task. By using Transfer learning methods, the accuracy of these models can be improved. This method minimizes the cost of computing resources and the training time. The results highlight that combining deep learning methods with transfer learning methods can improve the accuracy for these tasks. The proposed method suggests a strong direction to accurately predict water levels and identify bodies of water, which will help to water resource management. Use of satellite images with deep learning and transfer learning models offers a better solution to explore its uses in hydrological and environmental science.