Weather forecasting is the application of science and technology to predict the state of the atmosphere for a future time and a given location. Now days, forecasting of accurate atmospheric conditions is the major challenge for the meteorologist and poor forecasting has significant impact on our daily lives. This brings the necessity to make research works on forecasting of the weather events with respect to Ethiopia.

1. ADDIS ABABA UNIVERSITY
AAiT
School of Electrical and Computer Engineering
Thesis title: Weather Forecasting using Deep Learning
Algorithm for the Ethiopian Context
By: Haftom Aregawi
Advisor: Mr. Menore Tekeba

2. Introduction
Weather - condition of the air on earth at a
given time
 Climate - average weather conditions in a
place over many years
Weather forecasting is the application of
science and technology to predict the condition
of the atmosphere.
Weather Forecasting Using DNN Algorithm for the Ethiopian Context 2

3. Introduction…
Background of weather forecasting…
In Ethiopia
 Metrological weather prediction was started at the
end of 19th century in Addis Ababa.
 Metrological technology station was established in
1890 in Adamtilu and 1896 in Gambela.
 Officially established the NMA in December 31,
1980 under proclamation No of 201.
Weather Forecasting Using DNN Algorithm for the Ethiopian Context 3

4. Introduction…
Forecasting of the weather with Ethiopian
context:
 Short range (6hrs.-2days)
 Medium range (>2-5days)
 Long range (>=7days)
Weather Forecasting Using DNN Algorithm for the Ethiopian Context 4

5. Statement of the problem
 Research questions
Does forecasting of the weather events using DBN
much better than SVM and numerical based
regression?
Does DBN based forecasting of the atmospheric
condition suitable for the Ethiopian Context?
Is the variation of atmospheric condition solved
using DBN and SVM for regression better than the
existing forecasting method?
Weather Forecasting Using DNN Algorithm for the Ethiopian Context 5

6. Statement of the problem
 Ethiopian meteorology agency uses statistical
and dynamical methods to predict the
atmospheric conditions
 Ethiopian NMA uses a software designed by
World Meteorology Organization (WMO)
standards for weather forecasting
Weather Forecasting Using DNN Algorithm for the Ethiopian Context 6

7. Statement of the problem…
 The existing system does not consider what
type of forecasting modeling is appropriate for
Ethiopia and how to implement the system
with the Ethiopian context.
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8. Objective
 General objective
Implementing of weather forecasting system using DNN
for the Ethiopian context which can forecast Ethiopian
weather conditions with better accuracy than the existing
system.
Specific objective
To select the appropriate types of weather forecasting
models and methods for the Ethiopian context.
To enhance short range, medium range, and long range
weather prediction for Ethiopia.
To enhance the overall prediction capability of weather
forecasting in Ethiopia by introducing new weather
forecasting methods.
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9. Methodology
 The methodologies used in this thesis are:
 Literature survey
 Select algorithm
 Propose the system
 Design and Implement
 Testing
Weather Forecasting Using DNN Algorithm for the Ethiopian Context 9

10. Scope of this Thesis
 To propose appropriate and suitable
forecasting model and algorithm for the
Ethiopian context by considering the four
season and three rainfall regimes.
 To compared the result of proposed system
with the result of numerical based and support
vector machine for regression methods.
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11. Contribution of this Thesis
Weather Forecasting Using DNN Algorithm for the Ethiopian Context 11
To enhance the forecasting mechanisms in
Ethiopia by considering an appropriate model.
To help for selecting and implementing of
appropriate forecasting algorithms for the
Ethiopian context.

12. Literature Review
Weather Forecasting Using DNN Algorithm for the Ethiopian Context 12
Categorize into three
1. Artificial Neural Network Based Weather
Forecasting
 In 12–19 July 2016, Chen Kai et al.: “Short-
Term Precipitation Occurrence Prediction for
Strong Convective Weather Using Fy2-G Satellite
Data: A Case Study of Shenzhen, South China”

13. Literature Review…
2. Statistical Based Weather Forecasting
 In 17 February 2016, Taillardat Maxime et al.:
“Calibrated Ensemble Forecasts Using Quantile
Regression Forests and Ensemble Model Output
Statistics”
3. Numerical Based Weather Forecasting
In 2015, B. Iversen Emil et al.: “Short-term
probabilistic forecasting of wind speed using
stochastic differential equations”
Weather Forecasting Using DNN Algorithm for the Ethiopian Context 13

14. Design of the System
Weather Forecasting Using DNN Algorithm for the Ethiopian Context 14
Fig 1: Block Diagram of the proposed system
 Four year and Six months maximum and minimum:
 Temperature
 Dew Point
 Air Pressure
 Visibility
 Wind
 Humidity
 Precipitation

15. Design of the System…
Weather Forecasting Using DNN Algorithm for the Ethiopian Context 15
 Pre- processing
Fig 2: block diagram of pre-processing phase

16. Design of the System…
 Pre- Processing…
Weather Forecasting Using DNN Algorithm for the Ethiopian Context 16
 Handling Missing Data
 Handling small missing Values
 Handling Large missing Values

17. Design of the System…
 Pre- Processing…
Interpolation Small missing Value
Handling
Large missing Value
Handling
Cubic Spline 89.75% 71.45%
Linear
interpolation
76.2% 80.33%
Table 1: Accuracy of handling small and large missing values
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18. Weather Forecasting Using DNN Algorithm for the Ethiopian Context 18
Design of the System…
Pre- processing…
Data segmentation
Data Normalization
Fig 3: Block diagram of Time-Series data segmentation

19. Weather Forecasting Using DNN Algorithm for the Ethiopian Context 19
Design of the System…
Pre- processing…
Correlation among input and output variables

20. Weather Forecasting Using DNN Algorithm for the Ethiopian Context 20
Design of the System…
Pre- processing…
Correlation among input and output variables…

21. Weather Forecasting Using DNN Algorithm for the Ethiopian Context 21
Design of the System…
Pre- processing…
Cross-Correlation among input and output variables

22. Results and Discussion
Result
The three experiments are:
Experiment one – Short range forecasting
Experiment two – Medium range forecasting
Experiment Three – Long range forecasting
 The performance of the result provided are
evaluated using percentage of root mean square
error as well as time consumption.
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23. Results and Discussion …
 Result…
Experiment 1 – Short range
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Fig 5.1: Short range forecasting of temperature
using SVM
Fig 5.2: Short range forecasting of
Precipitation using SVM

24. Results and Discussion …
Result…
Experiment 1 – Short range…
Fig 5. 3: (a) Short range of temperature forecasting with DBN (b) Short range of
precipitation forecasting with DBN
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25. Results and Discussion …
Result…
Experiment 1 – Short range…
Fig 5.5: Short range precipitation
forecasting with numerical method
Fig 5.4: Short range Temperature
forecasting with numerical method
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26. Results and Discussion …
Result…
Experiment 1 – Short range…
Table 2: Accuracy of forecasting temperature and Precipitation
Weather event DBN SVM NWP
Temperature 88.6% 79.6% 52.5%
Precipitation 87.47% 87.3% 78%
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27. Results and Discussion …
Result…
Experiment 2 – Medium range
Fig 5. 6: (a) Maximum temperature with missing data (b) Maximum temperature after
handling the missing data using SVM
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28. Results and Discussion …
Result…
Experiment 2 – Medium range…
Fig 5.7: (a) Minimum temperature before handling the missing data (b) Minimum
temperature after handling the missing data using DBN
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29. Results and Discussion …
Result…
Experiment 2 – Medium range…
Fig 5.8: Forecasting of maximum temperature
using numerical
Fig 5.9: Forecasting of minimum
temperature using numerical
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30. Results and Discussion …
Result…
Experiment 2 – Medium range…
Weather events DBN SVM NWP
Tempe
rature
Miss 87.7% 80.4% ---
Handled 92.2% 83.3% 67.15%
Precipi
tation
Miss 84.1% 87.47% ---
Handled 92.7% 87.41% 71.7%
Table 3: Accuracy of forecasting Maximum temperature and Precipitation
before and after handling the missing values
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31. Results and Discussion …
Result…
Experiment 3 – Long range
Fig 5.10: (a) Minimum temperature with missing data (b) Minimum temperature after
handling the missing data
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32. Results and Discussion …
Result…
Experiment 3 – Long range…
Fig 5.11: Long range forecasting of maximum temperature using DBN
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33. Results and Discussion …
Result…
Experiment 3 – Long range…
Fig 5.12: forecasting of maximum temperature using numerical with polynomial
regression after handling the missing data
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34. Results and Discussion …
Result…
Experiment 3 – Long range…
Weather events DBN SVM NWP
Temper
ature
Miss 32.88% 81.69% ---
Handled 34.85% 86.023% 58%
Precipit
ation
Miss 28.48% 85.56% ---
Handled 56.83% 85.67% 63.2%
Table 4: Accuracy of forecasting Maximum temperature and Precipitation
before and after handling the missing values
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35. Results and Discussion …
 Discussion
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36. Results and Discussion …
 Discussion…
Table 5 Summary of the time required to train the system with the RMSE values in each
of the experiments

37. Conclusion and Future Works
Conclusion
 The weather forecasting is implemented using the
three algorithms namely DBN, SVM, and
numerical.
 Cubic spline and linear interpolation methods are
applied to handle the missing data
 The performance of the achieved result is
compared based on the percentage of RMSE and
time consumption
Weather Forecasting Using DNN Algorithm for the Ethiopian Context 37

38. Conclusion and Future Works …
Conclusion…
 Except in experiment three DBN algorithm has
higher performance than both SVM and the
numerical based forecasting.
 Forecasting after handling the missing data gives
us better performance.
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39. Conclusion and Future Works …
Future Works
 For the next:
In order to forecast an accurate atmospheric condition, it
is better to add different types of weather events namely
radiation, cloud distribution, wind direction and speed.
 We should focus on applying different machine
learning algorithms to forecast accurate weather
condition.
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