River system understanding through machine learning in Digital Twins

There is a fast development in technical solutions for water management. Digital Twins are among these fast-developing technologies, offering a platform for scientists, policy makers, and other stakeholders to exchange knowledge. However, the Digital Twins require also efficient hydrological information gain. New measurement techniques are causing a rapid growth of data, often resulting in scattered or incomplete datasets. Machine learning can be used to detect patterns, identify relations between variables, and fill data gaps. Typically, machine learning needs high-quality and long-term data for training. This is not always available, especially for variables that are obtained from short-term field campaigns.

This study explores traditional machine learning algorithms to optimize hydrological information gain from large datasets. Data from four study sites in three intensively studied Finnish rivers are used as a case study. The rivers are in the south, middle, and north of Finland and cover climatic conditions from boreal to sub-arctic. The approach involves the development of a simple application that enables users to gain maximum understanding with minimal user input. The main goals of the application are to detect patterns, recognize different river conditions and seasonality, fill data gaps, identify variable importance under different environmental conditions, and provide insights on variable relationships. The case study shows differences in seasonality, and therefore, differences in variable importance between the different rivers.