Evaluating the Performance of ANN and ANFIS Models for the Prediction of Chlorophyll in the Ashtamudi Estuary, India

Accurate prediction of chlorophyll-a is crucial for assessing eutrophication dynamics in reservoirs, lakes, estuaries, etc. This study focuses on developing and comparing two models (1. Artificial neural network, ANN; 2. Adaptive neuro-fuzzy inference system, ANFIS) for chlorophyll-a prediction in the Ashtamudi estuary, Kerala, India. Total Dissolved Solids, Turbidity, and Total phosphates were taken as input parameters for predicting chlorophyll-a for both ANN and ANFIS models considering their considerable influence on chlorophyll-a. The ANN model achieved a remarkable coefficient of determination (R2) of 1.0, indicating a perfect fit between the observed and predicted chlorophyll-a values. The performance of the ANFIS model was also good, with an R2 exceeding 0.95. In ANFIS, the membership functions, gbellmf, trimf, and gaussmf over-predicted the chlorophyll-a contents, while trapmf, gauss2mf, pimf, dsigmf, and psigmf under-predicted the chlorophyll-a contents. The ANN model performed better compared to the ANFIS model on chlorophyll-a prediction in the Ashtamudi estuary and can be used for future predictions of chlorophyll-a. The models generated can capture the complex relationships between turbidity, TDS, total phosphates, and chlorophyll-a concentrations. Moreover, these provide valuable insights into the temporal and spatial variations of chlorophyll-a concentrations, allowing for proactive management strategies to mitigate the impacts of eutrophication. The models generated find applications among water resource planners, environmental agencies, and policymakers.