Abstract: Rainfall prediction has extreme significance in countless aspects and scopes. It can be very helpful to reduce the effects of sudden and extreme rainfall by taking effective security measures in advance. Due to climate variations, an accurate rainfall prediction has become more complex than before. Data mining techniques can predict the rainfall through extracting the hidden patterns among weather attributes of past data. This research contributes by exploring the use of various data mining techniques for rainfall prediction in Lahore city. Techniques include: Support Vector Machine (SVM), Naïve Bayes (NB), k Nearest Neighbor (kNN), Decision Tree (J48) and Multilayer Perceptron (MLP). The dataset is obtained from a weather forecasting website and consists of several atmospheric attributes. For effective prediction, pre-processing technique is used which consists of cleaning and normalization processes. Performance of used data mining techniques is analyzed in terms of precision, recall and f-measure with various ratios of training and test data.

Shabib Aftab, Munir Ahmad, Noureen Hameed, Muhammad Salman Bashir, Iftikhar Ali and Zahid Nawaz, “Rainfall Prediction in Lahore City using Data Mining Techniques” International Journal of Advanced Computer Science and Applications(IJACSA), 9(4), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090439

@article{Aftab2018,
title = {Rainfall Prediction in Lahore City using Data Mining Techniques},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090439},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090439},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {4},
author = {Shabib Aftab and Munir Ahmad and Noureen Hameed and Muhammad Salman Bashir and Iftikhar Ali and Zahid Nawaz}
}