MacroSimply: An Intuitive Rule-Based Classifier to Predict Biotic Status in Supporting Ecological Restoration

We address the challenging problem of predicting the biological quality of a water body based on a set of environmental and management-related drivers. While modelling approaches for predicting water quality from hydrological and pollution-load variables are well established, analogous tools for predicting biological status remain less consolidated. Both components are nevertheless required within the Water Framework Directive to assess ecological status (ES) and support restoration planning. This paper concentrates on the latter challenge by presenting an experience developed over four heavily impacted rivers in Regione Lombardia (Northern Italy). Rutinary environmental, hydromorphological and biological data were systematized to develop a predictive framework linking management-related pressures to the macroinvertebrate component of ecological status. A rule-based classifier, denominated MacroSimply, was developed and compared with alternative statistical and machine learning approaches: logistic regression, a classification tree (CHAID), and a multilayer perceptron neural network. The tested models exhibited different strengths and weaknesses: Logistic regression provided a good balance between predictive performance and interpretability; the classification tree generated transparent threshold-based decision rules; and the neural network captured potentially complex non-linear relationships, albeit with reduced interpretability. MacroSimply achieved predictive performance comparable to the alternative approaches, and performing even higher reliability, while maintaining full transparency of the structure and a direct connection between predictors and management actions. The proposed framework was subsequently implemented in a spreadsheet-based tool to be easily used in restoration planning exercises. Although the obtained model should not be considered the definitive modelling solution even for our particular case, the results suggest that the adopted rule-based approach represents a working, valuable option preferrable to more complex data-driven methods when interpretability, reproducibility and practical applicability are key requirements for environmental management.