Ecological Indicators (Jul 2022)
Single-variable method for predicting trends in chlorophyll a concentration based on the similarity of time series
Abstract
Algal blooms are increasing in global water bodies, which threatens social development. Predicting the chlorophyll a (Chla) concentration is useful for water environmental management. Most previous studies of Chla prediction required multiple variables and considerable computation. Therefore, a similarity-based prediction algorithm (SPA) was developed for this prediction. The mean relative error (MRE) and dynamic time warping (DTW) algorithms are similarity measures used to create SPAs (SPA-MRE, SPA-DTW and SPA-MRE&DTW).Model validation was performed at the Yuqiao Reservoir north of Tianjin, China. The Chla concentration dataset of the Yuqiao Reservoir contains hourly data for 745 days from 2017 to 2020. According to the model validation results, SPA methods performed better than traditional time-series analysis methods (ARIMA (−9.7%) and Holt-Winters (−21.2%)). The MRE predicted by the SPA method at 24 h was 0.187.These three SPA methods showed different characteristics and applicability. SPA-MRE was suitable for predicting low Chla concentrations. SPA-DTW was suitable for predicting high Chla concentrations with obvious fluctuations. SPA-MRE combined with DTW exhibited the best comprehensive performance. Moreover, the SPA maintained high robustness and reliability even in the absence of 9% of the data.SPA is a single-variable prediction method that is more cost-effective than traditional regression-based and mechanism-based methods. SPA can be used with adjacent data for forecasting as in traditional time-series analysis models, and time series similar to the current series can be retrieved from historical data and used for predictions based on similarity. This study provides a robust and reliable prediction method for algal bloom management. SPA can be used to deeply mine characteristic information from historical data.
