scholarly journals Prediction of the future impact of climate change on reference evapotranspiration in Cyprus using artificial neural network

2017 ◽  
Vol 120 ◽  
pp. 276-283 ◽  
Author(s):  
Jazuli Abdullahi ◽  
Gozen Elkiran
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Artificial Neural Network ◽  
Reference Evapotranspiration ◽  
Impact Of Climate Change ◽  
The Future ◽  
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.

scholarly journals An Artificial Neural Network Approach to Predict the Future Land Use Land Cover of Great Malang Region, Indonesia

2021 ◽  
Author(s):  
Fatwa Ramdani ◽  
Budi Setiawan ◽  
Alfi Rusydi ◽  
Muhammad Furqon
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Artificial Neural Network ◽  
Land Cover ◽  
Negative Impact ◽  
Population Increase ◽  
Land Use Land Cover ◽  
Neural Network Approach ◽  
The Future ◽  
Artificial Neural

Great Malang region is developing rapidly with the population increase and inhabitant`s activity, like migration and urbanization. Other activities like agricultural expansion as well as an uncontrolled residential development need to be monitored to avoid any negative impact in the future. The availability of free and open-source software, spatial high-resolution satellite imagery datasets, and powerful algorithms open the possibilities to map, monitor, and predict the future trend of land use land cover (LULC) changes. However, the accuracy and precision of this model is still in doubt, especially in the Great Malang region. Research is needed to provide a foundational basis and documentation on how the changes occur, where did the changes occur, and the accuracy of the predicted model. This study tries to answer those questions using the high spatial resolution of Sentinel-2 imageries. Combination of the fuzzy algorithm, artificial neural network, and cellular automata was utilized to process the datasets. We analysed four different scenarios of simulation and the result then compared. The different number of hidden layers and iteration was used and evaluated to understand the effect of different parameters in the prediction result. The best scenario was then used to predict future land use changes. This study has successfully produced the future LULC model of Great Malang region with high accuracy level (87%). The study also found that the land use transformation from agriculture to urban built-up area is relatively low, where changes of the built-up area over three periods of analysis are below than 5%. This is due to the physical condition of Great Malang region where mountainous areas are dominated.

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
Sign in / Sign up

Export Citation Format

Share Document