Kernel-based modeling

The aim of data mining is data classification and regression modeling of real-world problems. Kernel-based approaches are a relatively new important method based on the different kernels type which is based on statistical learning theory initiated. Such models are capable of adapting themselves to predict any variable of interest via sufficient inputs. The kernel trick is one key component that allows the application of kernel-based approaches on a wide variety of problems. There are many kernels, and to solve the kind of problems the right kernel with the right dataset should be used. In the past few decades, kernel-based approaches have been applied for assessing complex phenomena. One of the important and complex phenomena is water resource engineering problems which are generally arisen at every stage of water-related issues ranging from water resources development to field-scale water utilization practices. This chapter aims to provide a basic understanding of the theory behind some kernel-based approaches and a general review of their applications for solving some types of water resources engineering problems.