ORCID Profile
0000-0002-0577-940X
Current Organisation
University of South Australia
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Publisher: ZIbeline International Publishing
Date: 10-02-2020
DOI: 10.26480/ESMY.01.2020.01.07
Abstract: Paleoproterozoic sedimentary rocks associated with the Man Shield of West Africa are perceived to be similar, irrespective of their locality. This research seeks to establish the provenance and tectonic setting of these rocks to ascertain any such similarity perception, based on information from two localities. The study uses modal mineral estimations to reconstruct the source, paleocurrent, paleoclimate and relief of some conglomerates and sandstones from Chagupana and Tarkwa areas in Ghana. Chagupana conglomerate has igneous and metamorphic provenances, while Kawere conglomerate has metamorphic provenance. Average mineralogical composition of Chagupana sandstone is Q53-F45-R3 and classify as arkose. Tarkwa suites of Huni, Kawere and Banket sandstones are composed of Q48-F34-R18, Q51-F25-R23 and Q76-F7-R17, and classify as lithic arkose, lithic arkose-feldspathic litharenite, and sublitharenite, respectively. Detritus of all the sandstones suggest acid igneous rock source, with minor sedimentary and metamorphic imprints, with an order of maturity as Banket Kawere Huni Chagupana. Detritus in the Chagupana, Huni and Kawere sandstones are from the transitional continental margin. The Chagupana is from the cold arid climate, while the Huni and Kawere are from the semi-arid/semi-humid climates. The Banket sandstone mobilises from craton interior with recycled orogenic materials in a humid environment. The angular-subangular feldspars in Chagupana sandstone indicate low relief and low-moderate recycling close to the source. Huni, Kawere and Banket sandstones derive from low-moderate reliefs with multiple recycling episodes. The Chagupana and Huni sandstones show paleo-current directions from the north and east, respectively. Similarities between the Chagupana and Tarkwa rocks can only be limited to the tectonic setting and not from source area, paleo-climate, paleo-current and relief.
Publisher: Springer Science and Business Media LLC
Date: 14-01-2021
DOI: 10.1007/S13201-020-01352-7
Abstract: 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 R 2 consistently greater than 0.850. The GPR model gave a better prediction of TDS concentration, with an average R 2 , MAE and RMSE of 0.987, 4.090 and 7.910, respectively. For the BPNN, an average R 2 , MAE and RMSE of 0.913, 9.720 and 19.137, respectively, were achieved, while the PCR gave an average R 2 , 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.
Publisher: Scientific Research Publishing, Inc.
Date: 2018
Publisher: American Society of Civil Engineers
Date: 23-03-2023
No related grants have been discovered for Derrick Aikins.