2011-13330 SNU CLS Seokmo-Yoo, August 2020.
Based on Long Short Term Memory(Sepp Hochreiter et al. 1997), which is effective model to predict time series data, I developed a simple prediction model of temperature and precipitation in weather sensory data. After training the model by observed data, I compared the result with the real data and analyzed the error of the result by numerical methods to discuss about accuracy.
Check out snu2020-graduation-thesis.ipynb for detail.
- using Tensorflow Keras in Python 3
Check out data/OBS_ASOS_DD_19071001-20200609.csv for detail.
- Source: https://data.kma.go.kr/cmmn/main.do, Open Weather Data Portal, accessed on 2020-06-10.
- Record: ASOS/Daily/Average, Minimum, Maximum Temperature(degree Celcius) and Daily Precipitation(mm/day)
- Range: 1907-07-01 to 2020-06-09 except 1950-01-01 to 1953-12-31 due to Korean War
- Observation Point: 37.57142°N 126.9658°E 86m, Seoul 108
- Normalize to [-1, 1]
- LSTM Layer(Unit: 100)
- Dense Layer(Unit: 1)
- Denormalize
Accuracy: MSE 3.1986, R^2 0.9706
- Total test set
- Lastest 120 days test set
- Training loss by epochs
- Scatter plot of ground truths verus predicted values
Accuracy: MSE 3.7829, R^2 0.9656
- Total test set
- Lastest 120 days test set
- Training loss by epochs
- Scatter plot of ground truths verus predicted values
Accuracy: MSE 5.3571, R^2 0.9530
- Total test set
- Lastest 120 days test set
- Training loss by epochs
- Scatter plot of ground truths verus predicted values
Accuracy: MSE 181.0255, R^2 0.1642
- Total test set
- Lastest 120 days test set
- Training loss by epochs
- Scatter plot of ground truths verus predicted values