TROPHIC STATE CLASSIFICATION OF KERALA LAKES USING AUTO-TUNED HYBRID AI MODELS

Abstract

The trophic state is one of the significant environmental impacts that must be monitored and controlled in any aquatic environment. This phenomenon due to nutrient imbalance in water strengthened with global warming, inhibits the natural system to progress. With eutrophication, the mass of algae in the water surface increases and results to lower dissolved oxygen in the water that is essential for fishes. Numerous limnological and physical features affect the trophic state and thus require extensive analysis to asses it. The univariate ap proach for trophic state classification is based on specified ranges of the cause (Nitrogen, Phosphorus) or response (Chlorophyll-a [Chl-a] and Secchi Depth [SD]) variables or on the variable information expressed in the form of indices. Therefore, in this project, an attempt is made to study about the accurate prediction of TSI using effective combination of differ ent features using Machine Learning(ML), Deep Learning(DL) and hybrid techniques. The framework was applied to a dataset of 11 lakes, 4 reservoirs and 2 ponds in Kerala for the period, 2012-2018. In this study, four different Artificial Intelligence(AI) models were devel oped for the prediction of Multivariate Trophic State Index(MTSI) and propose the usage of random forest as an effective model. Auto-tuned hybrid models are also proposed for effective Trophic state classification and they show better accuracy than their corresponding stand-alone models