Weather Forecasting using Deep Learning Algorithm for the Ethiopian Context
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Date
2018-10
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Addis Ababa University
Abstract
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.
A number of algorithms have been proposed for forecasting of atmospheric condition such as
support vector machine, neural network, numerical, and statistical models. However, in this
research the design and implementation of weather prediction for the Ethiopian context based
on the forecasting ranges using deep neural network, support vector machine for regression,
and numerical based regression is presented. Four and half years’ time series daily and hourly,
temperature, precipitation, humidity, visibility, dew point, air pressure, and wind historical
recorded data is used from National Oceanic and Atmospheric Administrator (NOAA) to
implement the system. Since making discussion on all-weather variables makes the report to
long, forecasting of temperature and precipitation weather variables for Addis Ababa are only
considered to be discussed and evaluated as a sample. And their results are examine based on
percentage of Root Mean Square Error and time consumption. The same data records are
applying for all algorithms; and the experimental result shows that, in forecasting of a big data,
DBN provides a better performance relative to SVM and numerical regressions. In short range
experiment we have achieved a forecasting accuracy of temperature 88.6%, 79.6%, and 52.5%
using DBN, SVM, and Numerical algorithms respectively. However, if we apply a small
dimension dataset as input values SVM and numerical regressions completely outperforms the
DBN due to shallow training.
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Keywords
Weather, Deep Belief Network, Support Vector Machine, Restricted Boltzman Machine, Meteorology