Inland harmful algal blooms (HABs) modeling using internet of things (IoT) system and deep learning

Abstract

Harmful algal blooms (HABs) have been frequently occurred with releasing toxic substances, which typically lead to water quality degradation and health problems for humans and aquatic animals. Hence, accurate quantitative analysis and prediction of HABs should be implemented to detect, monitor, and manage severe algal blooms. However, the traditional monitoring required sufficient expense and labor while numerical models were restricted in terms of their ability to simulate the algae dynamic. To address the challenging issue, this study evaluates the applicability of deep learning to simulate chlorophyll-a (Chl-a) and phycocyanin (PC) with the internet of things (IoT) system. Our research adopted LSTM models for simulating Chl-a and PC. Among LSTM models, the attention LSTM model achieved superior performance by showing 0.84 and 2.35 (μg/L) of the correlation coefficient and root mean square error. Among preprocessing methods, the z-score method was selected as the optimal method to improve model performance. The attention mechanism highlighted the input data from July to October, indicating that this period was the most influential period to model output. Therefore, this study demonstrated that deep learning with IoT system has the potential to detect and quantify cyanobacteria, which can improve the eutrophication management schemes for freshwater reservoirs.