Lake Water Level Forecasting Using LSTM and GRU: A Deep Learning Approach

Abstract

Artificial Intelligence and Deep Learning-based methods show constant promise in addressing time series forecasting challenges. Lake water level forecasting is an essential & significant environmental and societal impact related time series forecasting problem, with climate change connections. Lakes’ diverse hydrological characteristics make high-performance forecasting models to be lake-specific. Thus, comprehensive experiments are necessary to develop accurate deep learning-based forecasting models. We propose an approach for effective and efficient systematic search conduction for high-performance Deep Learning forecasting models. The method is applicable across various time series forecasting challenges. The research was structured around three experimental groups, each focusing on predicting the water levels of Lake Vesijärvi in Lahti, Finland, over periods of 1 day, 3 days, and 7 days, respectively with Long Short-Term Memory and Gated Recurrent Unit. The results are highly promising. All models achieved a Nash-Sutcliffe Efficiency above 0.95 and a Root Mean Squared Error below 0.025. The best-performing model achieved a Nash-Sutcliffe Efficiency above 0.99 and a Root Mean Squared Error below 0.0011. All evaluation metrics were calculated from testing data without signs of overfitting. This research provides insights into Deep Learning-based time series forecasting and a replicable method to conduct such studies effectively.