Intelligent ground vibration prediction in surface mines using an efficient soft computing method based on field data.

Ground vibration induced by blasting operations is considered one of the most common environmental effects of mining projects. A strong ground vibration can destroy buildings and structures, hence its prediction and minimization are of high importance. The aim of this study is to estimate the ground vibration through a hybrid soft computing (SC) method, called RSM-SVR, which comprises two main regression techniques: the response surface model (RSM) and support vector regression (SVR). The RSM-SVR model applies an RSM in the first calibrating process and an SVR in the second calibrating process to improve the accuracy of the ground vibration predictions. The predicted results of an RSM, which are obtained using the input data of problems, are used as the input dataset for the regression process of an SVR. The effectiveness and agreement of the RSM-SVR model were compared to those of an SVR optimized with the particle swarm optimization (PSO) and genetic algorithm (GA), RSM, and multivariate linear regression (MLR) based on several statistical factors. The findings confirmed that the RSM-SVR model was considerably superior to other models in terms of accuracy. The amounts of coefficient of determination (R 2) were 0.896, 0.807, 0.782, 0.752, 0.711, and 0.664 obtained from the RSM-SVR, PSO-SVR, GA-SVR, MLR, SVR, and RSM models, respectively.

Microplastics in atmospheric dust samples of Sistan: sources and distribution.

In this work, the amount and physical and chemical characteristics of airborne microplastics (MPs) pollution in dust samples in Sistan, located in the eastern part of Iran, is reported. Sampling stations were selected according to the wind direction and population density. MPs were collected by a static dust sampler and analyzed by optical microscopy and FT-IR spectroscopy. Results showed that the distribution frequency of MPs in residential and non-residential areas was 6 to 11 pieces per 100 g (pcs/100 g) with an average abundance of 9.8 pcs/100 g. Fragmented MPs were approximately consisted 64% of total MPs and their sizes were in the range of 0.9-3.8 mm. Polyethylene (49%), polystyrene (21%) and polyester (18%) were the main MPs presented in the dust samples. It was observed that population density and wind direction were the most important parameters affecting MPs pollution in dust. Supplementary Information: The online version contains supplementary material available at 10.1007/s40201-022-00833-y.

Application of ANN and ANFIS models for reconstructing missing flow data.

Hydrological yearbooks, especially in developing countries, are full of gaps in flow data series. Filling missing records is needed to make feasibility studies, potential assessment, and real-time decision making. In this research project, it was tried to predict the missing data of gauging stations using data from neighboring sites and a relevant architecture of artificial neural networks (ANN) as well as adaptive neuro-fuzzy inference system (ANFIS). To be able to evaluate the results produced by these new techniques, two traditionally used methods including the normal ratio method and the correlation method were also employed. According to the results, although in some cases all four methods presented acceptable predictions, the ANFIS technique presented a superior ability to predict missing flow data especially in arid land stations with variable and heterogeneous data. Comparing the results, ANN was also found as an efficient method to predict the missing data in comparison to the traditional approaches.