+ Site Statistics
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

Evaluation of artificial neural networks and kriging for the prediction of arsenic in Alaskan bedrock-derived stream sediments using gold concentration data



Evaluation of artificial neural networks and kriging for the prediction of arsenic in Alaskan bedrock-derived stream sediments using gold concentration data



International Journal of Mining, Reclamation and Environment 21(4): 282-294




Please choose payment method:






(PDF emailed within 0-6 h: $19.90)

Accession: 022596838

Download citation: RISBibTeXText

DOI: 10.1080/17480930701259294


Related references

Daily stream flow prediction capability of artificial neural networks as influenced by minimum air temperature data. Biosystems Engineering 95(4): 557-567, 2006

Characterization of aquifer properties using artificial neural networks: neural kriging. Water Resources Research 30(2): 483-497, 1994

Prediction of macroinvertebrate communities in sediments of Flemish watercourses based on artificial neural networks. Internationale Vereinigung fuer Theoretische und Angewandte Limnologie Verhandlungen 282: 777-780, 2002

Prediction of tropospheric ozone concentration by employing artificial neural networks. Environmental Engineering Science: 9, 1249-1254, 2008

Wave prediction and data supplementation with artificial neural networks. Journal of Coastal Research 23(4): 951-960, 2007

Prediction of Zn concentration in human seminal plasma of Normospermia samples by Artificial Neural Networks (ANN). Journal of Assisted Reproduction and Genetics 30(4): 453-459, 2013

Prediction of fracture frequency from wireline data with the aid of artificial neural networks. Institution of Mining and Metallurgy, Transactions, Section B: Applied Earth Science 109: 190-195, 2000

The use of artificial neural networks in prediction of congenital CMV outcome from sequence data. Bioinformatics and Biology Insights 2: 281-289, 2008

Application of Counter-propagation Artificial Neural Networks in Prediction of Topiramate Concentration in Patients with Epilepsy. Journal of Pharmacy and Pharmaceutical Sciences 18(5): 856-862, 2015

Evaluation of the efficiency of artificial neural networks for genetic value prediction. Genetics and Molecular Research 15(1):, 2016

The prediction of total anthocyanin concentration in red-grape homogenates using visible-near-infrared spectroscopy and artificial neural networks. Analytica Chimica Acta 594(1): 107-118, 2007

Prediction of sulfur dioxide concentration levels from the Mina Al-Fahal refinery in Oman using artificial neural networks. American Journal Of Environmental Sciences: 5, 473-481, 2008

Online prediction of onsets of seizure-like events in hippocampal neural networks using wavelet artificial neural networks. Annals of Biomedical Engineering 34(2): 282-294, 2006

Testing the identity of hashish samples with ICP-AES and NAA and data handling with neural networks. 2. Data verification with the use of artificial neural networks. Die Pharmazie 53(1): 39-42, 1998

Comparison of ordinary kriging and artificial neural network for spatial mapping of arsenic contamination of groundwater. Stochastic Environmental Research and Risk Assessment 24(1): 1-7, 2010