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.

Industrial waste gas has accounted for about 70% of the atmospheric pollution sources. It is crucial to establish a full-scale and reasonable monitoring mechanism. However, the monitoring area is so large and monitoring devices can not be set up in some special areas. Besides, it is difficult to model the gas distribution according with the actual. To solve the practical and theoretical problems, an analysis method of industrial waste gas distribution for non-detection zone was proposed based on a Bi-directional Error Multi-Layer Neural Network (BEMNN). Firstly, the monitoring mechanism was introduced in the thought of "monitoring in boundary and inference of dead zone", which aimed to offset the lack of monitoring points in some areas. Secondly, a multi-layer combination neural network was proposed in which the errors propagate in a bi-directional mode. The network was used to model the gas distribution relationship between the boundary and the dead zone. Then the gas distribution in the dead zone could be predicted with the boundary monitoring data. Finally, an experiment was conducted based on the actual monitoring data of an industrial park. The mean absolute error was less than 28.83 μg and the root-mean-square error was less than 45.62 μg. The relative error was between 8% and 8.88%. The results prove the feasibility of the proposed method, which accuracy can meet the practical requirement.