A comparative study of prediction and classification models on NCDC weather data

Abstract

Weather forecasting plays a significant role in different aspects of life such as in the operation of hydro-power plants, renewable energy, flood management, and agriculture. Recently, machine learning techniques have been used for weather forecasting for large periods of time, as it is more accurate than models based on physical principles. To address various problems, varieties of machine learning algorithms are applied in different fields. In our work, to examine whether these models are robust to predict National Climatic Data Center (NCDC) weather conditions, we carried out to compare newly emerging models with traditional meteorological models. In this paper, a set of the most common machine learning techniques are explored to generate robust weather forecasting model for long periods of time. Moreover, the combinations of all the model parameters are considered for simulations and the performance results of each method using a 10-fold cross-validation procedure are presented. The experimental results of the classifiers show that the decision tree CART, XGBoost and AdaBoost models exhibit better classification accuracy when compared with the other methods and for regression task, the linear regression method performs better in terms of R² metric.

KEYWORDS:

• NCDC dataset
• weather forecasting
• machine learning
• deep learning
• XGBoost

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Ibrahim Gad

Ibrahim Gad graduated from Tanta University in 2006 with a B.S. in Computer Science. From 2006-2014, he worked as an instructor at the Department of Computer Science, Faculty of Science, Tanta University. He received his Master's degree in Computer Science in 2014, from the Department of Computer Science, Faculty of Science, Ain Shams University, Cairo, Egypt. From 2014 to- present date, he is a Teaching Assistant at the Department of Computer Science, Faculty of Science, Tanta University. From 2016 to- present date, he is a research scholar at the Department of Computer Science, Faculty of Science, Mangalore University. His research interests are in Data Mining, Big Data, Machine Learning, Deep Learning, and Natural Language Processing.