Application of RBF and GRNN Neural Network Model in River Ecological Security Assessment—Taking the Middle and Small Rivers in Suzhou City as an Example

Abstract

The analytic hierarchy process is used to construct the health evaluation index system and grading standard of small- and medium-sized rivers in the region. Based on the principles of RBF and GRNN neural network algorithms, the river health evaluation models of radial basis function neural network (RBF) and general regression neural network (GRNN) algorithms are constructed, respectively. The network training samples are constructed by the interpolation method. The standard value of river health classification evaluation is taken as the “prediction” sample to “predict”. Then the results are applied as the division basis of the river health classification evaluation, which is to evaluate and analyze the health status of small and medium rivers in Suzhou Prefecture. The results indicate that: (1) the RBF and GRNN neural network models have exactly the same results in evaluating the health of small and medium rivers in the region, and are basically the same as the back propagation (BP) neural network evaluation results. RBF and GRNN neural network models have the advantages of fast convergence speed, high prediction accuracy, harder to fall into local minima, less adjustment parameters, and only one spread parameter, which can predict and evaluate the network faster, which is a large calculation advantage. (2) The health evaluation level of the main rivers in Suzhou Prefecture is from grades II to III, that is, between healthy and sub-healthy. This grade objectively reflects the health status of small- and medium-sized rivers in the region, which can provide a reference for the sustainable management of regional rivers and ecological environment construction.