Importance of Temporal Information in Fish Habitat Assessment Using Multiobjective Fuzzy Genetics-Based Machine Learning

Rivers and agricultural canals form a network that serves as an important habitat for aquatic ecosystems. Urbanization, field development, and water pollution have led to the deterioration of aquatic environments, resulting in the loss of bio-diversity therein, making ecosystem conservation and restoration a pressing issue. Freshwater fish that inhabit agricultural channel networks are significantly affected by changes in flow regimes due to irrigation, and it is essential to consider seasonal changes in both natural and anthropogenic flow dynamics for effective ecosystem conservation. Species distribution models are valuable tools for quantitatively assessing species-habitat relationships. This study examines the applicability of multiobjective fuzzy genetics-based machine learning (MoFGBML) to fish habitat assessment for major species recorded in an ecohydraulic survey dataset of an irrigation system located in a suburb of Tokyo. MoFGBML generates fuzzy if-then rule based species distribution models with different tradeoffs between accuracy and interpretability. Our focus is the model interpretability and knowledge extraction from the species distribution model, specifically feature importance and rules in relation to the model accuracy. We also show the importance of the temporal information in fish habitat assessment.