Integrated explainable deep learning prediction of harmful algal blooms

Abstract

Harmful algal blooms (HABs) can cause serious problems for aquatic ecosystems and human health, as well as massive social costs. Therefore, continuous monitoring and prevention are required. Water quality management is an important task to minimize such algae, and future occurrences can be accurately predicted through optimal water resource management. In this study, we developed a convolutional neural network model using eight water quality variables and four weather variables to predict the concentration of chlorophyll-a in four major Korean rivers. In addition, Deep SHAP was applied to aid in policy decision-making and identify the influence on variables affecting chlorophyll-a. This integrated prediction model showed a 38.01 % reduction in root mean square error and 36.16 % improvement in R-squared compared to the long short-term memory (LSTM) model. This demonstrated the effectiveness of the proposed integrated prediction approach. Furthermore, despite simultaneously predicting HABs at all monitoring stations and training 394 times faster than LSTM-based models, the proposed method exhibited a significant improvement in efficiency and elucidated variable influences that existing models failed to explain. The proposed integrated prediction model can predict HAB spread, identify variable influences to aid decision-makers, and effectively implement preemptive responses, thus reducing economic losses and preserving aquatic ecosystems.