Classifying eutrophication spatio-temporal dynamics in river systems using deep learning technique

Abstract

Eutrophication is a major cause of water quality degradation in South Korea, owing to severe algal blooms. To manage eutrophication, the South Korean government provided the Trophic State Index (TSIko), which was revised according to Carlson's TSI. The TSIko levels were simulated using mechanistic water quality modeling. However, the computational complexity of model parameter calibration and the nonlinearity of water quality kinetics complicate analyzing accurate eutrophication conditions. Deep learning models have been considered alternatives to numerical model approaches because they directly extract water quality variables without prior knowledge. In particular, the convolutional neural network (CNN) model showed robust feature extraction from the complex datasets. This study constructed and optimized a CNN model using water quality data from the Han, Guem, Yeongsan, and Nakdong Rivers in South Korea over nine years from 2014 to 2022 to classify the TSIko. The CNN model provided validation results using the statistical measurement of classification accuracy, known as the F1 score, which is the harmonic mean of precision and recall. The F1 scores were 0.922, 0.950, 0.964, and 0.896 for oligotrophic, mesotrophic, eutrophic, and hypertrophic statuses, respectively. The CNN model outperformed conventional machine learning models. Subsequently, a eutrophication map for the four major rivers was generated using the CNN model to simulate the spatial and temporal variations of the eutrophication index, mimicking high spatio-temporal eutrophic dynamics with respect to the mainstream and tributaries of the Yeongsan and Nakdong Rivers. Therefore, this study demonstrates the capability of the CNN model to analyze eutrophication conditions at various spatial and temporal scales of major rivers in South Korea.