Monitoring of water quality through accurate predictions provides adequate information about water management. In the present study, three different modelling approaches: Gaussian process regression (GPR), backpropagation neural network (BPNN) and principal component regression (PCR) models were used to predict the total dissolved solids (TDS) as water quality indicator for the water quality management. The performance of each model was evaluated based on three different sets of inputs from groundwater (GW), surface water (SW) and drinking water (DW). The GPR, BPNN and PCR models used in this study gave an accurate prediction of the observed data (TDS) in GW, SW and DW, with the R² consistently greater than 0.850. The GPR model gave a better prediction of TDS concentration, with an average R², MAE and RMSE of 0.987, 4.090 and 7.910, respectively. For the BPNN, an average R², MAE and RMSE of 0.913, 9.720 and 19.137, respectively, were achieved, while the PCR gave an average R², MAE and RMSE of 0.888, 11.327 and 25.032, respectively. The performance of each model was assessed using efficiency based indicators such as the Nash and Sutcliffe coefficient of efficiency (E_NS) and the index of agreement (d). The GPR, BPNN and PCR models, respectively, gave an E_NS of (0.967, 0.915, 0.874) and d of (0.992, 0.977, 0.965). It is understood from this study that advanced machine learning approaches (e.g. GPR and BPNN) are appropriate for the prediction of water quality indices and would be useful for future prediction and management of water quality parameters of various water supply systems in mining communities where artificial intelligence technology is yet to be fully explored.

There is growing tension between high-performance machine-learning (ML) models and explainability within the scientific community. In arsenic modelling, understanding why ML models make certain predictions, for instance, “high arsenic” instead of “low arsenic”, is as important as the prediction accuracy. In response, this study aims to explain model predictions by assessing the relationship between influencing input variables, i.e., pH, turbidity (Turb), total dissolved solids (TDS), and electrical conductivity (Cond), on arsenic mobility. The two main objectives of this study are to: (i) classify arsenic concentrations in multiple water sources using novel boosting algorithms such as natural gradient boosting (NGB), categorical boosting (CATB), and adaptive boosting (ADAB) and compare them with other existing representative boosting algorithms, and (ii) introduce a novel SHapley Additive exPlanation (SHAP) approach for interpreting the performance of ML models. The outcome of this study indicates that the newly introduced boosting algorithms produced efficient performances, which are comparable to the state-of-the-art boosting algorithms and a benchmark random forest model. Interestingly, the extreme gradient boosting (XGB) proved superior over the remaining models in terms of overall and single-class performance metrics measures. Global and local interpretation (using SHAP with XGB) revealed that high pH water is highly correlated with high arsenic water and vice versa. In general, high pH, high Cond, and high TDS were found to be the potential indicators of high arsenic water sources. Conversely, low pH, low Cond, and low TDS were the main indicators of low arsenic water sources. This study provides new insights into the use of ML and explainable methods for arsenic modelling.

The occurrence of premature rockbolt failure in underground mines has remained one of the most serious challenges facing the industry over the years. Considering the complex mechanism of rockbolts’ failure and the large number of influencing factors, the prediction of rockbolts’ failure from laboratory testing may often be unreliable. It is therefore essential to develop new models capable of predicting rockbolts’ failure with high accuracy. Beyond the predictive accuracy, there is also the need to understand the decisions made by these models in order to convey trust and ensure safety, reliability, and accountability. In this regard, this study proposes an explainable risk assessment of rockbolts’ failure in an underground coal mine using the categorical gradient boosting (Catboost) algorithm and SHapley Additive exPlanations (SHAP). A dataset (including geotechnical and environmental features) from a complex underground mining environment was used. The outcomes of this study indicated that the proposed Catboost algorithm gave an excellent prediction of the risk of rockbolts’ failure. Additionally, the SHAP interpretation revealed that the “length of roadway” was the main contributing factor to rockbolts’ failure. However, conditions influencing rockbolts’ failure varied at different locations in the mine. Overall, this study provides insights into the complex relationship between rockbolts’ failure and the influence of geotechnical and environmental variables. The transparency and explainability of the proposed approach have the potential to facilitate the adoption of explainable machine learning for rockbolt risk assessment in underground mines.

The microtremor HVSR, also known as Nakamuras approach, is an empirical technique that is becoming more suitable for site response analysis. The main goal of this research is to develop microtremor zonation (microzonation) of the study area to examine the potential risk of blast and other vibration sources, determine the seismic vulnerability indices of the area based on the site response parameters, and develop the distribution of the site response parameters. Surface waves were recorded at 34 locations using a three-component geophone to obtain the microtremor data. According to the site response parameters, the natural peak frequency varied from 2 to 5Hz, the maximum period was less than 0.5seconds, and the maximum amplification factor (H/V ratio) was 5.50. Of the 34 locations, 26 recorded low vulnerability indices (K_g) in the range of 0.27 to 4.82 and, thus, classified as Zone 1 materials. This indicates that K_g is typically low throughout the research area, except for a few areas that had extremely high vulnerability. The high vulnerabilities can be attributed to dense, loose soils in the vicinity, which amplify ground vibrations. Since the average natural period is less than 0.5seconds, a seismic events impact would not be felt for a longer time unless it occurred in a region with extremely low frequencies (F₀1.0Hz).