Application of an ordinary kriging–artificial neural network for elemental distribution in Kahang porphyry deposit, Central Iran

2020 ◽  
Vol 13 (15) ◽  
Author(s):  
Amir Bijan Yasrebi ◽  
Ardeshir Hezarkhani ◽  
Peyman Afzal ◽  
Reza Karami ◽  
Mohammad Eskandarnejad Tehrani ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ordinary Kriging ◽  
Central Iran ◽  
Elemental Distribution ◽  
Porphyry Deposit ◽  
Artificial Neural

scholarly journals Combining Artificial Neural Network and Ordinary Kriging to Predict Wetland Soil Organic Carbon Concentration in China’s Liao River Basin

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7005
Author(s):  
Yingdong Kang ◽  
Xiaoyan Li ◽  
Dehua Mao ◽  
Zongming Wang ◽  
Mingxuan Liang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
River Basin ◽  
Carbon Concentration ◽  
Ordinary Kriging ◽  
Wetland Soil ◽  
Organic Carbon Concentration ◽  
Artificial Neural

Accurate prediction of wetland soil organic carbon concentration and an understanding of its controlling factors are important for studying regional climate change and wetland carbon cycles; with that knowledge mechanisms can be put in place that are conducive to sustainable ecosystem management for environmental health. In this study, a hybrid approach combining an artificial neural network and ordinary kriging and 103 soil samples at three soil depth ranges (0–30, 30–60, and 60–100 cm) were used to predict wetland soil organic carbon concentration in China’s Liao River Basin. The model evaluation indicated that a combination of artificial neural network and ordinary kriging and limited soil samples achieved good performance in predicting wetland soil organic carbon concentration. Wetland soil organic carbon concentration in the Liao River Basin has apparent spatial and vertical heterogeneities with values decreasing from southeast to northwest and concentrates present mainly in the topsoil (0–30 cm). Mean wetland soil organic carbon concentration values at the three soil depths were 10.43 ± 0.38, 7.93 ± 0.25, and 7.61 ± 0.22 g/kg, respectively, which are smaller than those over other wetland regions in Northeast China. Terrain aspect contributed the most in predicting wetland soil organic carbon concentration at each of the three soil depths, followed by normalized difference vegetation index at 0–30 cm and mean annual precipitation at 30–60 and 60–100 cm. This study provides a framework method and baseline to quantify the soil organic carbon concentration dynamics in response to climatic and anthropogenic drivers.

Spatial modeling of soil salinity using multiple linear regression, ordinary kriging and artificial neural network methods

2016 ◽  
Vol 63 (2) ◽  
pp. 151-160 ◽  
Author(s):  
Mahmoud Shahabi ◽  
Ali Asghar Jafarzadeh ◽  
Mohammad Reza Neyshabouri ◽  
Mohammad Ali Ghorbani ◽  
Khalil Valizadeh Kamran
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Soil Salinity ◽  
Spatial Modeling ◽  
Ordinary Kriging ◽  
Network Methods ◽  
Artificial Neural

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural
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