A systematic literature review on lake water level prediction models

Global climate change has led to large fluctuations in lake levels in recent years as meteorological and hydrological parameters have changed and water use has been intense. Water scientists use various computer models to analyze the hydrological variables recorded in the past and make projections for all future scenarios. Based on the technological progress, six different types of algorithms were studied in this review to predict the water level in lakes. The prediction results show that Deep Learning (DL) has the highest accuracy in terms of the evaluation metrics. Since the Artificial Intelligence (AI) field is still emerging and continue to improve, this study highlights better comprehension of current applications and the problems that need to be investigated more for LWL forecasting techniques. It reveals that the studies mainly focused on lakes either in USA or China and there is room for improvement for other locations that are scarcely investigated.