In order to mine geological mineral energy and study on geological mineral energy classification, a method based on a wireless sensor was proposed. Of logistic regression, artificial neural networks, random forests, and main wireless sensor algorithms of support vector machine (SVM) with the model in the application of the energy mineral resource prediction practice effects are reviewed and discuss the practical application in the process of sample selection, the wrong points existing in the cost, the uncertainty evaluation, and performance evaluation of the model using wireless sensor algorithm, random forest of the probability distribution of mineralization in the study area is calculated, and five prospecting potential areas are delineated. The results show that the ratio of ore-bearing unit and non-ore-bearing unit is 1 : 1, and the best random forest training model is obtained. 70% of the training sample set was randomly selected as the training set, and the remaining 30% was used as the test set to construct the random forest model. The training accuracy of the model is 96.7%, and the testing accuracy is 96.5%. Both model training accuracy and model testing accuracy are very high, which proves the accuracy of RF model construction and achieves satisfactory results. In this study, a wireless sensor is successfully applied to 3D mineral energy prediction, which makes a positive exploration for mineral resource prediction and evaluation in the future. Finally, the prediction of mineral resource energy based on a wireless sensor is an important trend of future development.
Geological Mineral Energy and Classification Based on Machine Learning
