https://github.com/annefou/annmaster

https://github.com/annefou/annmaster

Last synced: 1 day ago
JSON representation

• Host: GitHub
• URL: https://github.com/annefou/annmaster
• Owner: annefou
• License: mit
• Created: 2018-10-14T19:00:47.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2018-10-14T19:01:01.000Z (about 6 years ago)
• Last Synced: 2024-11-05T10:51:42.219Z (about 2 months ago)
• Language: Jupyter Notebook
• Size: 4.81 MB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# ANNMaster

Estimating net surface radiation by using ANN

sensitivity_met_data.txt:

1. X - date and time: 2017-08-03 00:00:00 to 2017-09-08 23:30:00

2. Rl_downwell - Longwave incoming radiation in W/m^2

3. AT_mbar - Atmospheric pressure in mbar

4. Rs_downwell - Shortwave incoming radiation in W/m^2

5. rH - Relative humidity in %

6. T_b_1477 - Air temperature in degree Celsius at 10m height

7. D_g_1477 - Wind direction in degree

8. F_1_s_g_1477 - Wind speed in m/s

9. Rnet - Net surface radiation in W/m^2

The goal is to estimate Rnet. 

Assumption is that tiff files contain information related to Rnet.

Simple perceptron:

https://machinelearningmastery.com/implement-perceptron-algorithm-scratch-python/

Free Datasets:

https://archive.ics.uci.edu/ml/datasets.html

First test to make:

- Rl_downwell - Longwave incoming radiation in W/m^2

- Rs_downwell - Shortwave incoming radiation in W/m^2

And possibly add:

- D_g_1477 - Wind direction in degree

- F_1_s_g_1477 - Wind speed in m/s

On site: 10-20 August.