Using machine learning for the assessment of ecological status of unmonitored waters in Poland - Andrzej Martyszunis; Małgorzata Loga; Karol Przeździecki (Scientific Reports)
Abstract Advancements in Artificial Intelligence (AI) technology allow for development of new tools for analytics and management which present new opportunities in field of environmental protection. The following study showcases usage of Machine Learning (ML) techniques as a complementary method for water status assessment of water bodies. Since the main goal of Water Framework Directive (WFD) is to improve the quality of water and reach good status in all the water bodies across Europe intensive monitoring program was launched together with water status assessment procedure. Based on requirements of the European Union’s WFD concerning ecological status assessment it is presented how ML can be used for assessment of Polish unmonitored river water bodies. Due to the absence of monitoring data, the foremost challenge lay in securing relevant alternative data which was set to be anthropogenic pressures. The pivotal solution was implementation of ML techniques which enable processing of seemingly unrelated information concerning pressures in the catchment. Decision Tree, Random Forest, KNN, Support Vector Machine, Multinomial Naive Bayes, XGBoost models have been tested and the results indicated most suitable techniques. Study shows highest efficiency of XGBoost and Random Forest algorithms for classification of unmonitored water bodies. The models were compared by their overall accuracy (OA) reaching approximately 93% for binary classification and 72% for comprehensive classification as well as partial class accuracies and the Probability of Misclassification (PoM) parameter. The analyses demonstrates a practical application of AI in assessment of unmonitored water bodies in case of binary classification used for reporting water status objectives of WFD as well as possible usage of full classification for planning and operational uses. OA and PoM are postulated as the best measures of goodness of classification.