Assess and prognosticate green house gas emissions from agricultural production of EU countries, by implementing, DEA Window analysis and artificial neural networks

2017 ◽  
Vol 76 ◽  
pp. 155-162 ◽  
Author(s):  
G. Vlontzos ◽  
P.M. Pardalos
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Agricultural Production ◽  
Gas Emissions ◽  
Eu Countries ◽  
Green House ◽  
Window Analysis ◽  
Green House Gas ◽  
Artificial Neural ◽  
Dea Window Analysis

Modeling energy consumption and greenhouse gas emissions for kiwifruit production using artificial neural networks

2016 ◽  
Vol 133 ◽  
pp. 924-931 ◽  
Author(s):  
Ashkan Nabavi-Pelesaraei ◽  
Shahin Rafiee ◽  
Homa Hosseinzadeh-Bandbafha ◽  
Shahaboddin Shamshirband
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Consumption ◽  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Gas Emissions ◽  
Artificial Neural

scholarly journals Using Artificial Neural Networks and SPI Measure Techniques to Forecast the Risk of Drought in Iraq and Its Impact on Environment

2018 ◽  
Vol 4 (2) ◽  
pp. 69
Author(s):  
Renas A.A. Nader ◽  
Aras J.M. Karim ◽  
Mohammad M.F. Hussien
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Agricultural Production ◽  
Water Retention ◽  
Negative Impact ◽  
The Neural Network ◽  
Normal Period ◽  
The World ◽  
Artificial Neural ◽  
Severe Shortage

The world suffers from drought, which has a negative impact on human, economic, social, cultural and tourism fields. As science progressed and developed, several ways of reducing drought were found. This phenomenon is also called (aridity and infertility, and water retention), it means a severe shortage of water resources due to low precipitation and low rainfall over a specific normal period time, which are causing heavy losses in agricultural production, and the occurrence of disasters and human calamities such as starvation, and it is forcing some population to emigrate collectively. The artificial neural networks (ANN) and the Standard Rain Index (SPI) were used in the analysis of the rainfall for all Iraqi governorates for the period 1991-2016 monthly. This study shows that the best model of the neural network is [19-3-1] according to AIC to forecast the amount of rainfall, and that the Iraqi provinces over next 10 years are exposed to a different behavior of climate between moderate dry and average humidity, and increase the area of ​​desertification.

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