Forecasting of water level in multiple temperate lakes using machine learning models

Abstract

Due to global climate change and growing population, fresh water resources are becoming more vulnerable to pollution. Protecting fresh water resources, especially lakes and the associated environment, is one of the key challenges faced by policy makers and water managers. Lake water level is an important physical indicator of lakes, and its fluctuation may significantly impact lake ecosystems. Therefore, reliable forecasting of lake water level is vital for a proper assessment of the health of lake ecosystems and their management. In this study, two machine learning models, including feed forward neural network (FFNN) and Deep Learning (DL) technique, were used to predict monthly lake water level. The two models were employed for one month ahead forecasting of lake water level in 69 temperate lakes in Poland. The results show that both the FFNN and the DL models performed generally well for forecasting of lake water level of the 69 lakes, with only marginal differences. The results also indicate that the DL model did not show significant superiority over the traditional FFNN model; indeed, the FFNN model slightly outperformed the DL model for 33 of the 69 lakes. These results seem to suggest that traditional machine learning models may just be sufficient for forecasting of lake water level when they are properly trained. The outcomes of the present study have important implications for water level forecasting and water resources management of lakes, especially from the perspective of machine learning models and their complexities.