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Soft computing-based technique as a predictive tool to estimate blast-induced ground vibration

Arthur Clement Kweku, Temeng Victor Amoako, Ziggah Yao Yevenyo

Journal of Sustainable Mining

2019

Vol. 18, iss. 4

287--296

The safety of workers, the environment and the communities surrounding a mine are primary concerns for the mining industry. Therefore, implementing a blast-induced ground vibration monitoring system to monitor the vibrations emitted due to blasting operations is a logical approach that addresses these concerns. Empirical and soft computing models have been proposed to estimate blast-induced ground vibrations. This paper tests the efficiency of the Wavelet Neural Network (WNN). The motive is to ascertain whether the WNN can be used as an alternative to other widely used techniques. For the purpose of comparison, four empirical techniques (the Indian Standard, the United State Bureau of Mines, Ambrasey-Hendron, and Langefors and Kilhstrom) and four standard artificial neural networks of backpropagation (BPNN), radial basis (RBFNN), generalised regression (GRNN) and the group method of data handling (GMDH) were employed. According to the results obtained from the testing dataset, the WNN with a single hidden layer and three wavelons produced highly satisfactory and comparable results to the benchmark methods of BPNN and RBFNN. This was revealed in the statistical results where the tested WNN had minor deviations of approximately 0.0024 mm/s, 0.0035 mm/s, 0.0043 mm/s, 0.0099 and 0.0168 from the best performing model of BPNN when statistical indicators of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Root Mean Square Error (RRMSE), Correlation Coefficient (R) and Coefficient of determination (R2) were considered.

Reservoir water level forecasting using group method of data handling

Zaji A. H., Bonakdari H., Gharabaghi B.

Acta Geophysica

2018

Vol. 66, no. 4

717--730

Accurately forecasted reservoir water level is among the most vital data for efficient reservoir structure design and management. In this study, the group method of data handling is combined with the minimum description length method to develop a very practical and functional model for predicting reservoir water levels. The models’ performance is evaluated using two groups of input combinations based on recent days and recent weeks. Four different input combinations are considered in total. The data collected from Chahnimeh#1 Reservoir in eastern Iran are used for model training and validation. To assess the models’ applicability in practical situations, the models are made to predict a non-observed dataset for the nearby Chahnimeh#4 Reservoir. According to the results, input combinations (L, L-1) and (L, L-1, L-12) for recent days with root-mean-squared error (RMSE) of 0.3478 and 0.3767, respectively, outperform input combinations (L, L-7) and (L, L-7, L-14) for recent weeks with RMSE of 0.3866 and 0.4378, respectively, with the dataset from https://www. typingclub.com/st. Accordingly, (L, L-1) is selected as the best input combination for making 7-day ahead predictions of reservoir water levels.