Utilizing artificial neural network to simulate and predict the hydraulic performance of free water surface constructed wetlands

Abstract

Optimizing the hydraulic performance of free water surface constructed wetlands (FWS CWs) is of great economic and ecological value. However, there is a complex nonlinear relationship between the hydraulic performance and design parameters of FWS CWs. In this study, an artificial neural network (ANN) was applied to simulate and predict the hydraulic performance corresponding to different combinations of design parameters, and orthogonal design L₉ (3⁴) was used to determine the optimal combination of the important hyperparameters of the ANN. Based on the convenient scenario prediction ability of ANN, sensitivity analysis of different design parameters was carried out by the control variate method and full factor experiment. The results showed that the combination of 3 hidden layers, 15 neural nodes in each hidden layer, 0.001 learning rate, and 8 batch sizes was optimal for the established ANN model, achieving a coefficient of determination of 0.828 in the validation set and a satisfactory prediction effect in the test set. The narrow feature distribution interval in the training set restricted the generalization ability of the ANN model to some extent. Of the four continuous design parameters, the water depth and aspect ratio had an important influence on the effective volume ratio. The layout of inlet and outlet was the most influential design parameter, as confirmed by the full factor experiment of five factors and four levels. The established ANN allowed real-time implementation in an extended scenario at a low cost. This study suggests that the ANN can simultaneously project complex and uncertain effects of several design parameters on wetland performance. In future research, acquiring further comprehensive, impartial, and unbiased experimental datasets is necessary to establish a more robust and generalizing ANN model that can guide the optimal design of FWS CWs.

Keywords: Artificial neural network; Hyperparameter selection; Orthogonal design; Performance prediction; Sensitivity analysis.