scholarly journals Correction: Tabassum Abbasi et al., Modelling Methane and Nitrous Oxide Emissions from Rice Paddy Wetlands in India Using Artificial Neural Networks (ANNs). Water 2019, 11, 2169

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 751
Author(s):  
Tabassum Abbasi ◽  
Tasneem Abbasi ◽  
Chirchom Luithui ◽  
S. A. Abbasi
Keyword(s):  
Neural Networks ◽  
Nitrous Oxide ◽  
Artificial Neural Networks ◽  
Rice Paddy ◽  
Nitrous Oxide Emissions ◽  
Artificial Neural

The authors wish to make the following corrections to this paper [...]

scholarly journals Use of Artificial Neural Networks in Predicting Direct Nitrous Oxide Emissions from Agricultural Soils

2013 ◽  
Vol 20 (2) ◽  
pp. 419-428 ◽  
Author(s):  
Alicja Kolasa-Więcek
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Nitrous Oxide ◽  
Artificial Neural Networks ◽  
Agricultural Soils ◽  
Organic Fertilizers ◽  
Nitrous Oxide Emissions ◽  
Multilayer Perceptrons ◽  
Soil Emissions ◽  
Artificial Neural

Abstract Agricultural greenhouse gases emissions are mainly produced in direct emissions from plant and animal production as well as those associated with land use changes. Agriculture is a major source of atmospheric nitrous oxide (N2O). N2O emissions from agricultural production has the source primarily in soil fertilized by mineral and organic fertilizers. In Poland, agricultural soils are responsible for 77.1% of emissions. Emissions associated with the animal manner farming amount 22.8%. Studies attempt to modeling and predicting of N2O emissions from Direct Soil Emissions in relation to the use of crops and livestock population. In the analysis an artificial neural networks were used. The best values showing the quality of neural regression model were obtained by multilayer perceptrons MLP. Based on the sensitivity analysis, attempts were taken to determine the extent of the contribution of each selected variables on the estimate of the direct emissions of N2O from agricultural soils. The sensitivity analysis of designed network on the structure MLP 9-4-1 shows that the amount of nitrogen fertilizer consumption has the biggest share in the shaping of N2O emissions from Direct Soil Emissions. The sensitivity analysis of network on the structure MLP 16-5-1 pointed to participate cattle and pigs as the most important in the formation of N2O emissions from Direct Soil Emissions. Among the crops in Poland, which may affect the release of N2O stands out rapeseed and rye. The study was conducted using the statistical package Statistica v. 10.0.

FUTURE OF ARTIFICIAL NEURAL NETWORKS IN SPACE EXPLORATION AND SPACE ENGINEERING

2018 ◽  
pp. 6-11
Author(s):  
Kobiljon Kh. Zoidov ◽  
◽  
Svetlana V. Ponomareva ◽  
Daniel I. Serebryansky ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Space Exploration ◽  
Artificial Neural

Bankruptcy Prediction Models: A Bet on Artificial Neural Networks

2012 ◽  
Vol 3 (2) ◽  
pp. 48-50
Author(s):  
Ana Isabel Velasco Fernández ◽  
◽  
Ricardo José Rejas Muslera ◽  
Juan Padilla Fernández-Vega ◽  
María Isabel Cepeda González
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Artificial Neural
Sign in / Sign up

Export Citation Format

Share Document