Comparison of classic regression methods with neural network and support vector machine in classifying groundwater resources

In the present era, classification of data is one of the most important issues in various sciences in order to detect and predict events. In statistics, the traditional view of these classifications will be based on classic methods and statistical models such as logistic regression. In the present era, known as the era of explosion of information, in most cases, we are faced with data that cannot find the exact distribution. Therefore, the use of data mining and machine learning methods that do not require predetermined models can be useful. In many countries, the exact identification of the type of groundwater resources is one of the important issues in the field of water science. In this paper, the results of the classification of a data set for groundwater resources were compared using regression, neural network, and support vector machine. The results of these classifications showed that machine learning methods were effective in determining the exact type of springs.