Research and application of simulation system for reduction and stabilization process of chromium contaminated soil based on intelligent model

Reducing the impact of chromium-contaminated soil on humans and organisms is a worldwide problem, and it is urgent to treat and evaluate it. Stabilization efficiency is a key factor for screening and evaluating reduction stabilization process of chromium contaminated soil. In this study, various data-driven models were constructed to predict the reduction and stabilization efficiency of chromium contaminated soil, which has very important practical significance. The results show that both models can predict the stability efficiency, and the model constructed by multi-model combination modeling method has better prediction performance than the model established by single neural network. The prediction effect of multi-model nonlinear combination modeling method is better than that of linear combination modeling method, and the nonlinear combination model based on BPNN combiner combination SVM sub-model has the best performance. Our research provides a good idea for the modeling and prediction of stabilization efficiency, which greatly reduces the time and economic cost required for the reduction and stabilization process of chromium contaminated soil.