Faisal Aburub and Wael Hadi
Predicting Groundwater Areas Using Data Mining Techniques Groundwater in Jordan as Case Study
1621 - 1624
2016
10
9
International Journal of Computer and Information Engineering
https://publications.waset.org/pdf/10005377
https://publications.waset.org/vol/117
World Academy of Science, Engineering and Technology
Data mining is the process of extracting useful or hidden information from a large database. Extracted information can be used to discover relationships among features, where data objects are grouped according to logical relationships; or to predict unseen objects to one of the predefined groups. In this paper, we aim to investigate four wellknown data mining algorithms in order to predict groundwater areas in Jordan. These algorithms are Support Vector Machines (SVMs), Naïve Bayes (NB), KNearest Neighbor (kNN) and Classification Based on Association Rule (CBA). The experimental results indicate that the SVMs algorithm outperformed other algorithms in terms of classification accuracy, precision and F1 evaluation measures using the datasets of groundwater areas that were collected from Jordanian Ministry of Water and Irrigation.
Open Science Index 117, 2016