Optimization of neural network parameters in improvement of particulate matter concentration prediction of open-pit mining

Abstract

The prediction of particulate matter (PM) concentration around open-pit mining is crucial for its control. To achieve this, machine learning (ML) techniques have been attempted in PM estimation without reaching their full potential. In the current study, an artificial neural network (ANN) is employed for the prediction of PM2.5 concentration (PM < 2.5 μm), PM10 concentration (PM < 10 μm), and total suspended particulate (TSP). The influence of training algorithms and epochs on the modeling accuracy of ANN is investigated. Moreover, the convergence of ANN modeling is studied using Monte Carlo simulations. Compared with the quasi-Newton method (QNM) and conjugate gradient backpropagation (CGB), the one-step secant algorithm (OSSM) is the most suitable training algorithm for PM concentration prediction. Using the 0.1% bound as a convergence criterion, 500 Monte Carlo simulations are enough to get the converged results. The ANN-OSSM achieves high accuracy in PM concentration prediction. The R values on the testing set of PM2.5, PM10, and TSP are 0.887, 0.943, 0.886, respectively. Compared with the results in the literature, accuracy improvement is achieved using ANN-OSSM, with R increases from 0.86 to 0.92 (TSP dataset), from 0.91 to 0.94 (PM2.5 dataset), and from 0.84 to 0.89 (PM10 dataset). The present ML model is higher in accuracy and more straightforward in methodology compared with the hybridized model in the literature, which will promote its industrial application in the near future.