ANN based on SFLA for surface water quality evaluation model and its application

In this article, for researching the rationality and operability of the optimization in Artificial neural network model with Shuffled Frog Leaping Algorithm, a combined water quality assessment model was constructed based on ANN and SFLA. SFLA was applied to train the initialized data from the water quality criteria for optimizing the connection weights and thresholds of the neural network. The model was applied in surface water quality assessment of JinJiang river. The case study shows that the model possesses objectivity and practicability in surface water quality assessment. Besides, it can provide information for decision makers. This model provides a new way for water quality assessment.