Examining the effectiveness of artificially replicated lake systems in predicting eutrophication indicators: a comparative data-driven analysis

Abstract

Data-driven models for the prediction of lake eutrophication essentially rely on water quality datasets for a longer duration. If such data are not readily available, lake management through data-driven modeling becomes impractical. So, a novel approach is presented here for the prediction of eutrophication indicators, such as dissolved oxygen, Secchi depth, total nitrogen, and total phosphorus, in the waterbodies of Assam, India. These models were developed using water quality datasets collected through laboratory investigation in artificially simulated lake systems. Two artificial prototype lakes were eutrophied in a controlled environment with the gradual application of wastewater. A periodic assessment of water quality was done for model development. Data-driven modeling in the form of multilayer perceptron (MLP), time-delay neural network (TDNN), support vector regression (SVR), and Gaussian process regression (GPR) were utilized. The trained model's accuracy was evaluated based on statistical parameters and a reasonable correlation was observed between targeted and model predicted values. Finally, the trained models were tested against some natural waterbodies in Assam and a satisfactory prediction accuracy was obtained. TDNN and GPR models were found superior compared to other methods. Results of the study indicate feasibility of the adopted modeling approach in predicting lake eutrophication when periodic water quality data are limited for the waterbody under consideration.