Application of Machine Learning for Analysing and Assessing the Suitability of Specific Habitat Conditions

The analysis of specific habitat conditions involves a systematic assessment of environmental variables such as temperature, hydrology, vegetation, etc., to clarify species’ ecological requirements and develop conservation strategies. Common approaches include statistical modelling, various Habitat Suitability Index (HSI) models, and GIS based spatial analyses, which quantifies factors like topography, land cover and anthropogenic disturbance. Today, machine learning (ML) methods are widely applied across engineering disciplines, including water resources management. In this study, ML methods, particularly model trees, are employed to model and predict key abiotic factors relevant to fish communities. The research focuses on the bioindicator species Barbus balcanicus (brook barbel), which inhabits the middle part of the Sutla River (transboundary river basin between Croatia and Slovenia) and serves as an indicator of ecological conditions in this system. Using ML, models for water depth, water velocity, and water temperature were developed and applied together with SWAT (Soil and Water Assessment Tool) data for determination of HSI for future scenarios to support habitat assessment and water management planning.