Impacts of Missing Buoy Data on LSTM-Based Coastal Chlorophyll-a Forecasting

Abstract

Harmful algal blooms (HABs) pose significant threats to coastal ecosystems and public health. Accurately predicting the chlorophyll-a (Chl) concentration, a key indicator of algal biomass, is crucial for mitigating the impact of algal blooms. Long short-term memory (LSTM) networks, as deep learning tools, have demonstrated significant potential in time series forecasting. However, missing data, a common occurrence in environmental monitoring systems, can significantly degrade model performance. This study examines the impact of missing input parameters, particularly the absence of Chl data, on the predictive performance of LSTM models. To evaluate the model’s performance and the effectiveness of different imputation techniques under various missing data scenarios, we used data collected from 2008 to 2018 for training and data from 2020 and 2021 for testing. The results indicated that missing Chl data can significantly reduce predictive accuracy compared to other parameters such as temperature or dissolved oxygen. Edge-missing data had a more pronounced negative effect on the model than non-edge missing data, and the model’s performance declined more steeply with longer periods of missing data. The prediction of high Chl concentrations was relatively more negatively affected by missing data than by low Chl concentrations. Although LSTM imputation methods help mitigate the impact of missing data, ensuring data completeness remains critical. This study underscores the importance of reliable data collection and improved imputation strategies for accurate forecasting of algal blooms.