Weather Forecasting in Sudan Using Machine Learning Schemes

Authors

  • Nazim Osman Bushara
  • Ajith Abraham

Keywords:

Weather forecasting, Rainfall prediction, Data Mining.

Abstract

In this research, effort has been made to examine the relationship of rainfall in Sudan with important parameters such as Station, Wind Direction, Date, Humidity, Min-Temperature, Max-Temperature and Wind Speed. Attention has been made to find out correlation of rainfall with these elements. The goals of this paper are to demonstrate: (1) How feature selection can be used to identify the relationships between rainfall occurrences and other weather conditions and (2) Which classifiers can give the most accurate rainfall estimates? Monthly meteorological data by Central Bureau of Statistics Sudan from 2000 to 2012 for 24 meteorological stations has been used. To perform feature selection and building prediction models, we used group of data mining algorithms. The analysis shows that, Date, Min-T, Humidity and Wind D affect rainfall in Sudan, and we got the best 14 algorithms for building models to predict the rainfall.

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Published

2014-10-01

How to Cite

Nazim Osman Bushara, & Ajith Abraham. (2014). Weather Forecasting in Sudan Using Machine Learning Schemes. Journal of Network and Innovative Computing, 2, 9. Retrieved from https://cspub-jnic.org/index.php/jnic/article/view/81

Issue

Vol. 2 (2014)

Section

Original Article

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