Credibility-based random forest soil heavy metal risk evaluation method and system

Abstract

The invention provides a credibility-based random forest soil heavy metal risk evaluation method and system, and the method comprises the steps: carrying out the data preprocessing: carrying out the preprocessing of an original data set, and obtaining an unmarked data set; marking data, namely marking whether the samples in the data set have pollution risks or not by adopting a soil pollution evaluation method; dividing a data set, namely performing stratified sampling on the data set according to proportions of different categories, and dividing a training set and a test set; model training: learning the training set by using a random forest algorithm based on true positive rate weighted voting to obtain a risk evaluation model, and inputting the test set into the model to obtain a risk evaluation result, using a Bayesian optimization algorithm to find a parameter combination with the highest accuracy rate by taking the accuracy rate as an optimization target, and and carrying out soil heavy metal risk evaluation and evaluation by utilizing the trained model. According to the method, the recall rate of few types of samples is improved on the unbalanced data set, and the pollution-free risk of the samples can be accurately distinguished.

Description

technical field [0001] The invention belongs to the field of artificial intelligence, and mainly relates to a reliability-based random forest soil heavy metal risk assessment method. Background technique [0002] Among the soil inorganic pollutants, heavy metals are more prominent, mainly because heavy metals cannot be decomposed by soil microorganisms, but are easy to accumulate and transform into more toxic methyl compounds, and some even accumulate in the human body at harmful concentrations through the food chain, seriously endangering human health. Soil heavy metal pollution seriously threatens the safety of ecosystems and agricultural products. [0003] At present, there are studies that use the random forest algorithm to evaluate the impact factors of soil heavy metal content, such as the patent document with the application number CN201610997260.X. However, the classification accuracy of the random forest algorithm is still insufficient: [0004] Random Forest alg...

AI Technical Summary

Problems solved by technology

[0006] Aiming at the defect that the existing technology cannot deal with unbalanced data sets, the technical soluti...

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