用LSTM模型预测蓝藻水华，即使数据不全也能提前28天预警。
LSTM networks provide efficient cyanobacterial blooms forecasting even with incomplete spatio-temporal data
- 用LSTM处理不完整时空数据，构建适合蓝藻预报的时间序列。
- 多变量LSTM模型在28天内预测准确率达90%，比原始信号提升明显。
- 适合水质管理、环境监测人员用于长期蓝藻风险预警。
蓝藻是内陆水体藻华最常见的优势物种，尤其当其产生毒素时，会严重威胁生态系统和水质。在人为活动与全球变暖的推动下，蓝藻水华的发生频率和分布范围预计将进一步上升。早期预警系统（EWS）可为管理措施提供及时响应窗口，降低风险。本文提出一种高效的蓝藻水华预警系统，基于6年来自多参数探头的高频不完整时空数据，包括藻蓝蛋白（PC）荧光作为蓝藻的代理指标。提出一种无需依赖探头、可复现的数据预处理方法，生成专用于蓝藻预报的时间序列。在此基础上，比较了六种非站点/非物种特异性预测模型：线性回归、随机森林及长短期记忆（LSTM）神经网络的自回归与多变量版本。评估覆盖4至28天共七个预报时间窗，采用混合评价体系：以均方误差（MSE）、决定系数（R²）、平均绝对百分比误差（MAPE）衡量PC值预测性能；以准确率（Accuracy）、F1分数、卡帕系数评估10 μg PC/L报警阈值的分类表现；另引入预报专用指标（技能值），衡量预测结果相对于延迟信号的改进程度。结果显示，多变量LSTM在所有时间窗与指标上表现最优且最稳定，对10 μg PC/L警戒值的预测准确率最高达90%。正向技能值表明其在16至28天前预报蓝藻水华具有显著有效性。
原文摘要 · Abstract (English)
Cyanobacteria are the most frequent dominant species of algal blooms in inland waters, threatening ecosystem function and water quality, especially when toxin-producing strains predominate. Enhanced by anthropogenic activities and global warming, cyanobacterial blooms are expected to increase in frequency and global distribution. Early warning systems (EWS) for cyanobacterial blooms development allow timely implementation of management measures, reducing the risks associated to these blooms. In this paper, we propose an effective EWS for cyanobacterial bloom forecasting, which uses 6 years of incomplete high-frequency spatio-temporal data from multiparametric probes, including phycocyanin (PC) fluorescence as a proxy for cyanobacteria. A probe agnostic and replicable method is proposed to pre-process the data and to generate time series specific for cyanobacterial bloom forecasting. Using these pre-processed data, six different non-site/species-specific predictive models were compared including the autoregressive and multivariate versions of Linear Regression, Random Forest, and Long-Term Short-Term (LSTM) neural networks. Results were analyzed for seven forecasting time horizons ranging from 4 to 28 days evaluated with a hybrid system that combined regression metrics (MSE, R2, MAPE) for PC values, classification metrics (Accuracy, F1, Kappa) for a proposed alarm level of 10 ug PC/L, and a forecasting-specific metric to measure prediction improvement over the displaced signal (skill). The multivariate version of LSTM showed the best and most consistent results across all forecasting horizons and metrics, achieving accuracies of up to 90% in predicting the proposed PC alarm level. Additionally, positive skill values indicated its outstanding effectiveness to forecast cyanobacterial blooms from 16 to 28 days in advance.
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