Publication Cover
Spectroscopy Letters
An International Journal for Rapid Communication
Volume 54, 2021 - Issue 2
141
Views
5
CrossRef citations to date
0
Altmetric
Articles

Mine reclamation based on remote sensing information and error compensation extreme learning machine

, , &
Pages 151-164 | Received 28 Oct 2020, Accepted 17 Dec 2020, Published online: 08 Feb 2021

References

  • Li, P. Determination and Analysis of Copper Content by Electrolytic Analysis. World Nonferrous Metals 2019, 06, 179–181.
  • Yu, X. J.; Fan, J. P.; Wang, W. W.; Wei, X. P.; Gu, J. H. Comparison of Different Determination Methods of Copper Content in Industrial Sludge. Analysis and Testing Technology and Instrument 2018, 24, 241–244.
  • Huang, W. F.; Liu, T.; Peng, Y. H.; Zhang, Y. Z. Determination of Copper Content in Industrial Sludge. Chemical Engineer 2019, 23, 17–19.
  • Luo, Z. Q. Determination of Copper Oxide in Raw Ore by Flame Atomic Absorption Spectrometry. Copper Engineering 2018, 2, 103–104.
  • Lv, Q. Q.; Zhang, Z.; Wang, J. P. Determination of Copper in Anode Copper by Electrolytic Gravimetric Method. Metallurgical Analysis 2017, 37, 30–33.
  • Li, G. Talking about the Geological Environmental Problems of the Open-Pit Mining and the Restoration Measures. World Nonferrous Metals 2019, 3, 47–48.
  • Boim, A. G.; Wragg, J. S.; Canniatti-Brazaca, G.; Alleoni, L. R. F. Human Intestinal Caco-2 Cell Line in Vitro Assay to Evaluate the Absorption of Cd, Cu, Mn and Zn from Urban Environmental Matrices. Environmental Geochemistry and Health 2020, 42, 601–615. DOI: 10.1007/s10653-019-00394-4.
  • Borah, P.; Gujre, N.; Rene, E. R.; Rangan, L.; Paul, R. K.; Karak, T.; Mitra, S. Assessment of Mobility and Environmental Risks Associated with Copper, Manganese and Zinc in Soils of a Dumping Site around a Ramsar site. Chemosphere 2020, 254, 126852.DOI: 10.1016/j.chemosphere.2020.126852.
  • Haller, H.; Jonsson, A. Growing Food in Polluted Soils: A Review of Risks and Opportunities Associated with Combined Phytoremediation and Food Production (CPFP)). Chemosphere 2020, 254, 126826.DOI: 10.1016/j.chemosphere.2020.126826.
  • Xie, L. X.; Van, Z. D. Distinguishing Reclamation, Revegetation and Phytoremediation, and the Importance of Geochemical Processes in the Reclamation of Sulfidic Mine Tailings: A review. Chemosphere 2020, 252, 126446. DOI: 10.1016/j.chemosphere.2020.126446.
  • Ke, X.; Zhang, F. J.; Zhou, Y.; Zhang, H. J.; Guo, L. G.; Tian, Y. Removal of Cd, Pb, Zn, Cu in Smelter Soil by Citric Acid leaching. Chemosphere 2020, 255, 126690. DOI: 10.1016/j.chemosphere.2020.126690.
  • Hancock, G. R.; Duque, J. F. M.; Willgoose, G. R. Mining Rehabilitation–Using Geomorphology to Engineer Ecologically Sustainable Landscapes for Highly Disturbed Lands. Ecological Engineering 2020, 155, 105836. DOI: 10.1016/j.ecoleng.2020.105836.
  • Vidal, C.; Ruiz, A.; Ortiz, J.; Larama, G.; Perez, R.; Santander, C.; Ferreira, P. A. A.; Cornejo, P. Antioxidant Responses of Phenolic Compounds and Immobilization of Copper in Imperata Cylindrica, a Plant with Potential Use for Bioremediation of Cu Contaminated Environments. Plants-Basel 2020, 9, 1397. DOI: 10.3390/plants9101397.
  • Mushtaq, M. U.; Iqbal, A.; Nawaz, I.; Mirza, C. R.; Yousaf, S.; Farooq, G.; Ali, M. A.; Khan, A. H. A.; Iqbal, M. Enhanced Uptake of Cd, Cr, and Cu in Catharanthus Roseus (L.) G.Don by Bacillus cereus: application of Moss and Compost to Reduce Metal availability. Environmental Science and Pollution Research International 2020, 27, 39807–39818. DOI: 10.1007/s11356-020-08839-5.
  • Santoyo-Martinez, M.; Mussali-Galante, P.; Hernandez-Plata, I.; Valencia-Cuevas, L.; Flores-Morales, A.; Ortiz-Hernandez, L.; Flores-Trujillo, K.; Ramos-Quintana, F.; Tovar-Sanchez, E. Heavy Metal Bioaccumulation and Morphological Changes in Vachellia Campechiana (Fabaceae) Reveal Its Potential for Phytoextraction of Cr, Cu, and Pb in Mine Tailings. Environmental Science and Pollution Research International 2020, 27, 11260–11276. DOI: 10.1007/s11356-020-07730-7.
  • Okada, K. A Historical Overview of the past Three Decades of Mineral Exploration Technology. Natural Resources Research 2020. DOI: 10.1007/s11053-020-09721-4.
  • Duke, E. F. Near Infrared Spectra of Muscovite, Tschermak Substitution, and Metamorphic Reaction Progress: Implications for Remote Sensing. Geology 1994, 22, 621–624. DOI: 10.1130/0091-7613(1994)022<0621:NISOMT>2.3.CO;2.
  • Cudahy, T. J.; Whitbourn, L. B.; Connor, P. M.; Mason, P.; Phillips, R. N. Mapping Surface Mineralogy and Scattering Behavior Using Backscattered Reflectance from a Hyperspectral Midinfrared Airborne CO2 Laser System (MIRACO(2)LAS). IEEE Transaction on Geoscience and Remote Sensing 1999, 37, 2019–2034. DOI: 10.1109/36.774713.
  • Le, B. T.; Xiao, D.; Okello, D.; He, D.; Xu, J. L.; Doan, T. T. Coal Exploration Technology Based on Visible-Infrared Spectra and Remote Sensing Data. Spectroscopy Letters 2017, 50, 440–450. DOI: 10.1080/00387010.2017.1354889.
  • Kruse, F. Identification and Mapping of Minerals in Drill Core Using Hyperspectral Image Analysis of Infrared Reflectance Spectra. International Journal of Remote Sensing 1996, 17, 1623–1632. DOI: 10.1080/01431169608948728.
  • Pour, A. B.; Hashim, M.; Hong, J. K.; Park, Y. Lithological and Alteration Mineral Mapping in Poorly Exposed Lithologies Using Landsat-8 and ASTER Satellite Data: North-Eastern Graham Land, Antarctic Peninsula. Ore Geology Reviews 2019, 108, 112–133. DOI: 10.1016/j.oregeorev.2017.07.018.
  • Guo, J.; Zhou, K.; Wang, J.; Cui, S.; Zhou, S.; Tang, C. Identification of Iron-Bearing Minerals Based on HySpex Hyperspectral Remote Sensing Data. Journal of Applied Remote Sensing 2019, 13, 1. DOI: 10.1117/1.JRS.13.047501.
  • Xu, Y.; Chen, J.; Meng, P. Detection of Alteration Zones Using Hyperspectral Remote Sensing Data from Dapingliang Skarn Copper Deposit and Its Surrounding Area, Shanshan County, Xinjiang Uygur Autonomous Region. China. Journal of Visual Communication and Image Representation 2019, 58, 67–78. DOI: 10.1016/j.jvcir.2018.11.032.
  • Liu, L.; Zhou, J.; Jiang, D.; Zhuang, D.; Mansaray, L. R. Lithological Discrimination of the Mafic-Ultramafic Complex, Huitongshan, Beishan, China: Using ASTER Data. Journal of Earth Science 2014, 25, 529–536. DOI: 10.1007/s12583-014-0437-3.
  • Cheng, X. F.; Song, T. T.; Chen, Y.; Shen, Y. M.; Qi, W. F. Hyperspectral Inversion Analysis of Soil Heavy Metal Content in Lanping Lead Zinc Mining Area, Western Yunnan. Acta Petrologica et Mineralogica 2017, 36, 60–69.
  • Wang, L.; Lin, Q. D.; Jia, D.; Shi, H. S.; Huang, X. H. Prediction of Soil Heavy Metal Content Based on Reflectance Spectroscopy. Journal of Remote Sensing 2007, 11, 906–913.
  • Qu, Y. H.; Jiao, S. H.; Liu, S. H.; Zhu, Y. Q. Retrieval of Copper Pollution Information from Hyperspectral Satellite Data in a Vegetation Cover Mining Area. Spectroscopy and Spectral Analysis 2015, 35, 3176–3181.
  • Wu, J. S.; Song, J.; Zheng, M. K.; Xie, Q.; Li, J. J.; Huang, X. L. Research Progress on Total Monitoring Methods of Soil Heavy Metals. Ournal of Northeast Agricultural University 2011, 42, 133–139.
  • Shi, L.; Jianping, C.; Jie, X. Prospecting Information Extraction by Text Mining Based on Convolutional Neural Networks–a Case Study of the Lala Copper Deposit, China. IEEE Access 2018, 6, 52286–52297. DOI: 10.1109/ACCESS.2018.2870203.
  • Patel, A. K.; Chatterjee, S. Computer Vision-Based Limestone Rock-Type Classification Using Probabilistic Neural Network. Geoscience Frontiers 2016, 7, 53–60. DOI: 10.1016/j.gsf.2014.10.005.
  • Viswanathan, R.; Samui, P. Determination of Rock Depth Using Artificial Intelligence Techniques. Geoscience Frontiers 2016, 7, 61–66. DOI: 10.1016/j.gsf.2015.04.002.
  • Bazi, Y.; Melgani, F. Toward an Optimal SVM Classification System for Hyperspectral Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing 2006, 44, 3374–3385. DOI: 10.1109/TGRS.2006.880628.
  • Huang, G. B.; Zhu, Q. Y.; Siew, C. K. Extreme Learning Machine: A New Learning Scheme of Feedforward Neural Networks. 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541). Budapest 2004, 2, 985–990.
  • Deng, W. Y.; Zheng, Q. H.; Chen, L. Regularized extreme learning machine. 2009 IEEE Symposium on Computational Intelligence and Data Mining, Nashville, TN, 2009, 389-395,
  • Deng, W. Y.; Zheng, Q. H.; Chen, L.; Xu, X. B. Research on Fast Learning Method of Neural Network. Journal of Computer Science 2010, 33, 279–287. DOI: 10.3724/SP.J.1016.2010.00279.
  • Huang, G. B.; Zhou, H.; Ding, X. Extreme Learning Machine for Regression and Multiclass Classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B 2012, 42, 513–529.
  • Liu, X. W.; Wang, L.; Huang, G. B.; Zhang, J.; Yin, J. Multiple Kernel Extreme Learning Machine. Neurocomputing 2015, 149, 253–264. DOI: 10.1016/j.neucom.2013.09.072.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.