Conference Paper

Review of computational models using to subsidence prediction due to fluid withdrawal

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... Therefore, an attempt to mathematically describe the process of subsidence due to water withdrawal in order to determine possible vertical displacements due to compression of the aquifers is of key importance. Existing models for predicting surface deformations for porous medium are used mainly in issues relating to fluid and gaseous deposits (Geertsma and Van Opstal, 1973;Hejmanowski, 1993;Galloway and Burbey, 2011;Witkowski, 2014). A more detailed review of the literature on the existing forecasting models is presented among others in (Hejmanowski, 1993;Witkowski, 2014). ...
... Existing models for predicting surface deformations for porous medium are used mainly in issues relating to fluid and gaseous deposits (Geertsma and Van Opstal, 1973;Hejmanowski, 1993;Galloway and Burbey, 2011;Witkowski, 2014). A more detailed review of the literature on the existing forecasting models is presented among others in (Hejmanowski, 1993;Witkowski, 2014). In one of the earlier works, attention was drawn to the possibility of research into the use of this new approachthrough the use of artificial intelligence tools -towards the issue of forecasting of indirect influence on mining areas (Witkowski, 2014). ...
... A more detailed review of the literature on the existing forecasting models is presented among others in (Hejmanowski, 1993;Witkowski, 2014). In one of the earlier works, attention was drawn to the possibility of research into the use of this new approachthrough the use of artificial intelligence tools -towards the issue of forecasting of indirect influence on mining areas (Witkowski, 2014). Neural networks -which constitute one of the tools of artificial intelligence -have been applied in many fields of technical sciences which proves their potential and abilities (Ambrozic and Turk, 2003;Osowski, 2006;Kim, Lee and Oh, 2008;Zhi-xiang, Pei-xian, Li-li and Ka-zhong, 2009;Kumar, Raghuwanshi and Singh, 2010;Lee, Park and Choi, 2012). ...
... In the world literature a variety of approaches to modelling this problem, not only in mining areas can be found. A thorough discussion of the existing methods of computation has been included in the publication "Review of computational models using to subsidence prediction due to fluid withdrawal" (Witkowski, 2014). An overview of the existing solutions and their usefulness in the prediction process of ground surface and rock mass displacements caused by drainage of waterbearing horizons is presented there. ...
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Based on the previous studies conducted by the authors, a new approach was proposed, namely the tools of artificial intelligence. One of neural networks is a multilayer perceptron network (MLP), which has already found applications in many fields of science. Sequentially, a series of calculations was made for different MLP neural network configuration and the best of them was selected. Mean square error (MSE) and the correlation coefficient R were adopted as the selection criterion for the optimal network. The obtained results were characterized with a considerable dispersion. With an increase in the amount of hidden neurons, the MSE of the network increased while the correlation coefficient R decreased. Similar conclusions were drawn for the network with a small number of hidden neurons. The analysis allowed to select a network composed of 24 neurons as the best one for the issue under question. The obtained final answers of artificial neural network were presented in a histogram as differences between the calculated and expected value.
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This article presents the problem of modelling drainage subsidence that accompanies the mining of solid minerals. Rock mass drainage causes a change in pressure in the aquifer, and thereby initiates the compaction process. On the surface we can observe the effect in the form of a wide drainage basin, which adds to the direct impact of mining operations. The article presents the research stage associated with the use of artificial intelligence in forecasting the indirect impacts of (drainage) in mining areas. This article also outlines the Support Vector Machine (SVM) method and its use based on the example of underground coal mining. For the purpose of calculations, the data from altitude surveying conducted on the terrain surface, and information from the network piezometric boreholes installed in subsequent aquifers were used. Used in the analysis was ε-SVM method for regression tasks with the use of radial basis function. The calculations were performed with an integrated software package for support vector regression (LIBSVM) and the obtained results were presented. The process of selection of parameters in different variants, and obtained discrepancies in the process of research and testing were described. Cross-Validation and generalization of the knowledge processes necessary for future forecasting the process of drainage subsidence were characterized. The summary includes opportunities for further research as well as analysis using artificial intelligence.
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