Application of Data Mining for Weather Forecasting

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Addis Ababa University


Weather forecasting has been one of the most scientifically and technologically challenging problems around the world in the last century. Making an accurate prediction is one of the major challenges meteorologist are facing all over the world. Since ancient times, weather prediction has been one of the most important domains. Scientists have tried to forecast meteorological characteristics using a number of methods, some of these methods being more accurate than others. Accurate and timely weather forecasting is a major challenge for the National Meteorological Agency of Ethiopia. In this study, the researcher investigated the use of data mining techniques in forecasting rainfall. This was carried out using J48 decision tree, Multilayer perceptron artificial neural network, and PART rule induction algorithms and meteorological data collected between 2000 and 2014 from National Meteorological Agency of Ethiopia. A data model for the meteorological data was developed and this was used to train the classifier algorithms. The performances of these algorithms were compared using standard performance metrics, and the algorithm which gave the best results used to generate classification rules for the mean weather variables. A predictive model was also developed for the weather prediction program and the results compared with actual weather data for the predicted periods. The results show that given enough case data, Data Mining techniques can be used for weather forecasting. To get a better awareness in choosing which model produced sound prediction and higher accuracy, 13 experiments were done with J48 algorithm and multilayer perceptron classifier, and eight experiments were done using PART rule induction, by inputting all the records with a 10 fold cross-validation mode, and inputting different percentage (%) of the record for testing the performance of the model. The next option used by the researcher to improve the performance of the model were to test if a better model could be obtained by excluding one or more of the input variables and training different models. J48 has an accuracy of 86.65%, PART has an accuracy of 84.96 and Neural Network has an accuracy of 80.03%. Then J48 algorithm has shown better prediction performance. In the future, the effective use of information and technology is important for National Meteorology to stay competitive in today’s complex environment. The challenges faced when trying to make large, diverse, and often complex dataset records are considerable and employing other classification algorithms could yield better results.



Application of Data Mining