Qalaai Zanist Scientific Journal
گۆڤارى قەڵاى زانست

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ISSN 2518-6558 (Print)
Qalaai Zanist Scientific Journal  
Volume 2, Issue 2, April 2017

Copyright Statement: This is an open access publication licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

 
Paper Title: The Estimation of Wind Velocity Using Data Mining Techniques
Author (s): Sattar Nabee Rasool
Ahmet Koca
Karwan Hussein Qader
https://doi.org/10.25212/lfu.qzj.2.2.44
   
Index Terms: Climate parameter estimation, data mining, predictive modeling, clustering, classification
Abstract: Estimation of wind velocity in real time is very essential as it can provide valuable information to people of different domains such as agriculture, aviation and tourism to mention few. Since climate data is growing exponentially it is hard to analyze it manually. Therefore, machine-learning techniques such as unsupervised and supervised learning methods are used to mine voluminous data and discover valuable knowledge. Predictive modeling in data mining is required to estimate climate parameters. In this paper, we proposed a framework that exploits data mining techniques such as J48, KNN, Neural Networks, SVM and Linear Regression. The framework takes climate dataset as input, completes training phase and makes different models using data mining algorithms. Finally, it ends by exploiting linear regression, which models the relationship between a dependent variable and an exploratory variable. The framework results in estimating wind velocity and finding prediction error rate. A prototype application is built based on Weka, which is used to demonstrate proof of the concept. The empirical results applied on all data are obtained from the Turkish Government Meteorology Services for summer months of 2013. It revealed that the proposed framework is useful to have a predictive model with respect to estimation of climate parameters.
   
Cite This Paper (APA): Nabee Rasool, S., Koca, A., & Hussein Qader, K. (2017). The Estimation of Wind Velocity Using Data Mining Techniques. Qalaai Zanist Scientific Journal, 2(2). doi:10.25212/lfu.qzj.2.2.44
Text Language: English
Pp.: 444 - 455
Full Text:
 
 


INVITATION

Researchers and readers of Qalaai zanist are invited to submit their articles with Kurdish, Arabic or English language (to qalaai-zanist@lfu.edu.krd) that are consistent with the objective of this journal for publishing in the future issues.

 
 
 
 
 
 

A Scientific Quarterly Refereed Journal, Published by Lebanese French University (LFU) , Erbil - Kurdistan, Iraq
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