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Analysis of hydraulic conductivity of fractured groundwater flow media using artificial neural network back propagation
Cahyadi T.A.a, Syihab Z.b, Widodo L.E.b, Notosiswoyo S.b, Widijanto E.c
a Department of Mining Engineering, UPN Veteran Yogyakarta, Yogyakarta, Indonesia
b Faculty of Mining and Petroleum Engineering, Bandung Institute of Technology, Bandung, Indonesia
c Surface Mine GeoEngineering, PTFI, South Jakarta, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2020, Springer-Verlag London Ltd., part of Springer Nature.Groundwater flow in the Grasberg open-pit mine is governed by fractured flow media. Groundwater modeling in fractured media requires detailed hydraulic conductivity (K) value distribution to illustrate hydrogeological conditions of the Grasberg open-pit mine properly. The value of K is estimated by using the hydraulic conductivity (HC) system method based on rock quality designation (RQD), lithology permeability index (LPI), depth index (DI), and gouge content designation (GCD) data. Accordingly, all parameters must be available, but in this case, this information are only partially available. This paper proposes a method to solve this problem with artificial neural network (ANN) through a five approach scenario to find the K value with a model of incomplete empirical parameters. The artificial neural network back propagation (ANNBP) method is used to estimate the distributed K value based on the distributed observed RQD and LPI, as shown in the block model. This paper uses five optimum architectural schemes consisting of learning rate selection, momentum coefficient, number of nodes, number of hidden layers, activation method (sigmoid, tangent hyperbolic and Gaussian). Verification of the results of the K value is approached by the statistical approaches determination coefficient (R2), correlation coefficient (R), standard error of estimate (SEE), root-mean-squared (RMS), and normal root mean square (NRMS). ANNBP modeling used data for training and testing based on packer test and slug test as many as 49 points. Through five scenarios, it was found that scenario with learning rates of 10–5, momentum coefficient 0.1, number of nodes 10, number of hidden layer 5, with hyperbolic tangent activation methods is the most optimum result with R2 = 0.822, the accuracy of predicting/validating results is expressed by using R2 = 0.863, with R = 0.929, and the error percentage = 3.67%. The K value prediction results are used as an input for groundwater modeling. The modeling results show a significant correlation with high validity between the data extracted from the model and field observation. In a 3D model, the K value distribution is similar to the field RQD data distribution. Furthermore, the K value distribution results are clustered in order to understand the relationship between geological as well as geotechnical data. The highest K values are found in volcanic breccia rocks (DLMVB), volcanic sediments (DLMVS), volcanic andesite (DLMVA) and quaternary glacial till. The geological condition is confirmed by the average rock RQD value between 12.4 and 39.6%.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Correlation coefficient,Determination coefficients,Geological conditions,Groundwater modeling,Hydrogeological conditions,Rock quality designation,Statistical approach,Training and testing[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Artificial neural network backpropagation,Clustering of parameters,Fractured media,Hydraulic conductivity,Lithology permeability index,Rock quality designation[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]The authors would like to thank the Research Institution and Community Services of Bandung Institute of Technology for P3MI 2017, 2018, 2019 research funding and UPN ?Veteran? Yogyakarta for the support; Dr Amega for being a discussion partner in developing the simulation and optimization model; Mr Eman Widijanto, manager of Grasberg GeoEngineering, for allowing the authors to study the dewatering system at PTFI; and Dr Koesnaryo of the Department of Mining Engineering, UPN ?Veteran? Yogyakarta, for allowing the authors to use the visual license of the Visual MODFLOW 2.8.1 for this research. The authors are also grateful to Respati Muhammad, Aldin Ardian and Dr Agus Haris Widayat for proofreading this manuscript.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1007/s00521-020-04970-z[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]