Wetland effluent ammonia nitrogen concentration prediction method and system based on hybrid neural network

Abstract

The invention provides a wetland effluent ammonia nitrogen concentration prediction method and system based on a hybrid neural network, and the method comprises the steps: carrying out the principal component analysis and moving average processing of the obtained water quality environment data of the water inlet and outlet section of a constructed wetland, and obtaining input data; the number of indexes of the water quality environment data is not less than 8; constructing a training set and a test set by adopting input data, and constructing a prediction model by adopting a BP neural network; optimizing the weight and the threshold value of the BP neural network by adopting a genetic algorithm to obtain an optimized weight value and an optimized threshold value, and substituting the optimized weight value and the optimized threshold value into the neural network to complete the optimization of the neural network to obtain an optimized model; training the optimized model by adopting an LM algorithm to obtain an optimal water quality parameter prediction model; and predicting the ammonia nitrogen concentration of the constructed wetland to be predicted by using the test set. By adopting the technical scheme of the invention, the robustness and generalization ability of the model are improved, and the accuracy of effluent ammonia nitrogen concentration prediction is improved.

Description

technical field [0001] The invention belongs to the technical field of calculators, and in particular relates to a method and system for predicting the concentration of ammonia nitrogen in wetland effluent based on a hybrid neural network. Background technique [0002] At present, the multi-dimensional data-driven model has achieved remarkable results in the prediction of effluent water quality, among which the neural network technology has the best prediction effect. Neural network is an algorithm model extracted from the simplification of biological nervous system. In recent years, it is widely used in the field of urban sewage treatment due to its high robustness and good fitting effect on multivariate nonlinear relationships. At present, the neural networks commonly used in sewage treatment include error back propagation neural network, radial basis function neural network, genetic algorithm, recurrent neural network, long short-term memory neural network and so on. How...

AI Technical Summary

Problems solved by technology

However, these methods generally have the problem of being easily trapped in a local minimum and difficult to find a global optimal solution.

Moreover, for water quality prediction, there are many input water quality indicators, more than 8 items, how to process the input multiple indicators, put forward useful information, use for subsequent processing and make accurate predictions, and reduce prediction errors. difficulty

Images