- author = "Ruba Al-Jundi and Mais Yasen and Nailah Al-Madi",
- title = "Thunderstorms Prediction using Genetic Programming",
- journal = "International Journal of Information Systems and Computer Sciences",
- year = "2018",
- volume = "7",
- number = "1",
- note = "Special Issue of ICSIC 2017, Held during 23-24 September 2017 in Amman Arab University, Amman, Jordan",
- keywords = "genetic algorithms, genetic programming, Evolutionary computation, Machine Learning, Weather Prediction.",
- publisher = "WARSE",
- ISSN = "2319-7595",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/al-madi/Thunderstorm_Prediction.pdf",
-
URL = "http://www.warse.org/IJISCS/static/pdf/Issue/icsic2017sp14.pdf",
- size = "7 pages",
- abstract = "Thunderstorms prediction is a major challenge for efficient flight planning and air traffic management. As the inaccurate forecasting of weather poses a danger to aviation, it increases the need to build a good prediction model. Genetic Programming (GP) is one of the evolutionary computation techniques that is used for classification process. Genetic Programming has proven its efficiency especially for dynamic and nonlinear classification. This research proposes a thunderstorm prediction model that makes use of Genetic Programming and takes real data of Lake Charles Airport (LCH) as a case study. The proposed model is evaluated using different metrics such as recall, F-measure and compared with other well-known classifiers. The results show that Genetic Programming got higher recall value of predicting thunderstorms in comparison with the other classifiers.",
-
notes = "Lake Charles Metar and SYNOP data (LCH)
broken aug 2018 http://www.warse.org/IJISCS/archives",
