scholarly journals Stream hydrological and ecological responses to climate change assessed with an artificial neural network

1996 ◽  
Vol 41 (5) ◽  
pp. 857-863 ◽  
Author(s):  
N. LeRoy Poff ◽  
Sezin Tokar ◽  
Peggy Johnson
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Artificial Neural Network ◽  
Ecological Responses ◽  
Artificial Neural

scholarly journals Investigation the Ability of Artificial Neural Network in Simulation of Rainfall-Runoff Process under the Climate Change Conditions (Case Study: Pashakola Babol Dam Basin)

2018 ◽  
Vol 8 (16) ◽  
pp. 53-64
Author(s):  
معصومه خادمی ◽  
رامین فضل‌ اولی ◽  
علیرضا عمادی ◽  
◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Artificial Neural Network ◽  
Rainfall Runoff ◽  
Artificial Neural

scholarly journals The effect of climate change on cholera disease: The road ahead using artificial neural network

PLoS ONE ◽  
2019 ◽  
Vol 14 (11) ◽  
pp. e0224813 ◽  
Author(s):  
Zahra Asadgol ◽  
Hamed Mohammadi ◽  
Majid Kermani ◽  
Alireza Badirzadeh ◽  
Mitra Gholami
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Artificial Neural Network ◽  
The Road ◽  
Artificial Neural

scholarly journals Machine Learning-Based Small Hydropower Potential Prediction under Climate Change

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3643
Author(s):  
Jaewon Jung ◽  
Heechan Han ◽  
Kyunghun Kim ◽  
Hung Soo Kim
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Energy Source ◽  
Artificial Neural Network ◽  
Renewable Energy ◽  
Carbon Emission ◽  
Small Hydropower ◽  
Runoff Prediction ◽  
The Future ◽  
Artificial Neural

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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