scholarly journals Climate change prediction using artificial neural network

2022 ◽  
Vol 961 (1) ◽  
pp. 012003
Author(s):  
Zahraa Maamoon Mohammed Amin ◽  
Seroor Atalah Khaleefa Ali
PLoS ONE ◽  
2019 ◽  
Vol 14 (11) ◽  
pp. e0224813 ◽  
Author(s):  
Zahra Asadgol ◽  
Hamed Mohammadi ◽  
Majid Kermani ◽  
Alireza Badirzadeh ◽  
Mitra Gholami

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3643
Author(s):  
Jaewon Jung ◽  
Heechan Han ◽  
Kyunghun Kim ◽  
Hung Soo Kim

As the effects of climate change are becoming severe, countries need to substantially reduce carbon emissions. Small hydropower (SHP) can be a useful renewable energy source with a high energy density for the reduction of carbon emission. Therefore, it is necessary to revitalize the development of SHP to expand the use of renewable energy. To efficiently plan and utilize this energy source, there is a need to assess the future SHP potential based on an accurate runoff prediction. In this study, the future SHP potential was predicted using a climate change scenario and an artificial neural network model. The runoff was simulated accurately, and the applicability of an artificial neural network to the runoff prediction was confirmed. The results showed that the total amount of SHP potential in the future will generally a decrease compared to the past. This result is applicable as base data for planning future energy supplies and carbon emission reductions.


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