A Dynamic Neural Network Optimization Model for Heavy Metal Content Prediction in Farmland Soil

Abstract

To improve the accuracy of soil heavy metal content prediction, this paper proposes a dynamic neural network optimization model (DNNOM). The model is based on a radial basis function neural network (RBFNN). The weights and bias of the output layer of the RBFNN were generated using the adaptive dynamic genetic optimization algorithm (ADGOA), and the center point of the hidden layer of the RBFNN was determined using an efficient density peak clustering algorithm (EDPC). An adaptive variance measure (AVM) was then used to generate the width vector of RBFNN hidden layer. The model was applied to the prediction soil heavy metal content in six new urban areas in Wuhan. Through comparison with support vector machine(SVM), light gradient boosting machine(LightGBM), RBFNN, and genetic algorithm optimizes the radial basis function neural network(GA-RBFNN), the experimental results demonstrate that the DNNOM is closer to the real value than the other four models, and the four error indicator values are also significantly lower than those of the other comparison models, which have higher prediction accuracy. Especially when compared with RBFNN, the MAPE and SMAPE of DNNOM have dropped by 3.98% and 3.9%, respectively.INDEX TERMS Dynamic neural network optimization model, soil heavy metal content prediction, radial basis function neural network, adaptive dynamic genetic optimization algorithm.

Research on artificial neural networks to accurately predict element concentrations in nutrient solutions

“…Mahadeva et al [19] used ANN to accurately model, simulate, and analyze the performance of reverse osmosis desalination processes, among which the ANN model optimized by PSO was widely used. Furthermore, to improve the accuracy of predicting soil heavy metal content, Cao et al [20] used adaptive dynamic GA, efficient density peak clustering algorithm, and adaptive variance measure to optimize the RBF model.…”