1
Master of Mathematical Statistics, Razi University, Faculty of Science, Department of Statistics
2
Department of Statistics, Razi University, Faculty of Science, Kermanshah, Iran
Abstract
In the present era, data classification in order to detect and predict events is one of the most important topics in various sciences. In statistics, the traditional view of these classifications is based on classical methods and statistical models, such as logistic regression. In the present era, which is called the era of information explosion, in most cases we are faced with data for which an accurate distribution cannot be found; therefore, the use of data mining and machine learning methods that do not require predetermined models can be fruitful. In many countries, accurate detection of the type of groundwater resources is one of the significant issues in the field of water sciences. In this article, we have compared the results of classifying a dataset related to groundwater resources using regression methods, neural networks, and support vector machines. The results of these classifications showed that machine learning methods were effective in accurately detecting the type of springs.
Heydari Germianki, A. & Niaparest, M. (2020). Comparison of classical regression methods with neural networks and support vector machines in classifying groundwater resources. Andishe_ye Amari, 24(2), 15-23.
MLA
Heydari Germianki, A., & Niaparest, M. "Comparison of classical regression methods with neural networks and support vector machines in classifying groundwater resources", Andishe_ye Amari, 24, 2, 2020, 15-23.
HARVARD
Heydari Germianki A., Niaparest M. (2020). 'Comparison of classical regression methods with neural networks and support vector machines in classifying groundwater resources', Andishe_ye Amari, 24(2), pp. 15-23.
CHICAGO
A. Heydari Germianki & M. Niaparest, "Comparison of classical regression methods with neural networks and support vector machines in classifying groundwater resources," Andishe_ye Amari, 24 2 (2020): 15-23,
VANCOUVER
Heydari Germianki A., Niaparest M. Comparison of classical regression methods with neural networks and support vector machines in classifying groundwater resources. Andishe_ye Amari. 2020;24(2):15-23 (In Persian).
