Lake Titicaca water level forecasting using data augmentation and recurrent neural networks

Lake Titicaca, located on the border between Peru and Bolivia, is the highest navigable lake in the world and holds great environmental, cultural, social, and economic importance, both for Peru and Bolivia as well as for the Andean region in general. In this study, various artificial intelligence models based on recurrent neural networks, including LSTM, BiLSTM, GRU, and BiGRU with data augmentation, are analyzed to predict the water levels of Lake Titicaca. Data augmentation enriches the historical records and enhances the quality of the model predictions. The accuracy of the forecasts is crucial, as it contributes to proper water resource management, community safety, and ecosystem preservation. The experimental results show that all the implemented models benefit significantly from data augmentation, outperforming models reported in the literature, with GRU achieving the highest accuracy in its predictions, obtaining its best RMSE of 0.0182 m and an R 2 of 0.9986, surpassing LSTM, BiLSTM, and BiGRU.