基于遗传算法改进的一阶滞后滤波和长短期记忆网络的蓝藻水华预测方法

河湖藻类水华形成过程中所具有的突发性和不确定性，导致对藻类水华爆发预测准确性不高。为解决此问题，以叶绿素a的浓度值作为蓝藻水华演化过程表征指标，提出基于长短期记忆（LSTM）循环神经网络（RNN）蓝藻水华预测模型。首先，用遗传算法改进的一阶滞后滤波（GF）优化算法对数据进行平滑滤波处理；然后，搭建GF-LSTM网络的蓝藻水华预测模型，实现对水华发生的精准预测；最后，以太湖水域梅梁湖区域的采样数据为样本，对预测模型进行检验，并与传统的RNN和LSTM网络进行对比。仿真结果表明，提出的GF-LSTM网络模型平均相对误差控制在16%~18%，而RNN模型的预测平均相对误差为28%~32%，LSTM网络模型的平均相对误差为19%~22%，对采用数据的平滑性处理效果较好，预测精度更高，对样本具有更好的适应性，克服了传统RNN模型在长期训练时出现的梯度消失与梯度爆炸缺点。

Cyanobacterial bloom forecast method based on genetic algorithm-first order lag filter and long short-term memory network

The process of algal bloom evolution in rivers or lakes has characteristics of suddenness and uncertainty, which leads to low prediction accuracy of algal bloom. To solve this problem, chlorophyll a concentration was used as the surface index of cyanobacteria bloom evolution process, and a cyanobacterial bloom forecast model based on Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) was proposed. Firstly, the improved Genetic algorithm-First order lag filter (GF) optimization algorithm was taken as data smoothing filter. Secondly, a GF-LSTM network model was built to accurately predict the cyanobacterial bloom. Finally, the data sampled from Meiliang Lake in Taihu area were used to test the forecast model, and then the model was compared with the traditional RNN and LSTM network. The experimental results show that, the mean relative error of the proposed GF-LSTM network model is 16%-18%, lower than those of RNN model (28%-32%) and LSTM network model (19%-22%). The proposed model has good effect on data smoothing filtering, higher prediction accuracy and better adaptability to samples. It also avoids two widely known issues of gradient vanishing and gradient exploding when using traditional RNN model during long term training.