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- W4285281279 endingPage "310" @default.
- W4285281279 startingPage "291" @default.
- W4285281279 abstract "Available water and environmental resources are facing high scarcity due to population growth, urban development, and the rapid growth of industrial and agricultural projects. It is necessary to apply appropriate methods to achieve accurate policies and decisions for sustainable water and environmental development in such circumstances. With an increasing amount of “big data” with complex and nonlinear relationships, data-driven methods such as machine learning and data mining have attracted enormous attention in data-related studies. As a successful applied in a wide range of problems, support vector machines (SVMs) can be considered as one of the most well-known machine learning and data mining methods due to their successful and robust performance on various problems such as classification, pattern recognition, estimation, regression, and forecasting. This chapter reviews the SVM models concept and its application in water and environmental sciences. Furthermore, this chapter introduces different types of SVM models and other emerging ones. Finally, the challenges of this method for future studies will be discussed." @default.
- W4285281279 created "2022-07-14" @default.
- W4285281279 creator A5031008154 @default.
- W4285281279 creator A5040698421 @default.
- W4285281279 creator A5073347998 @default.
- W4285281279 creator A5080057370 @default.
- W4285281279 date "2022-01-01" @default.
- W4285281279 modified "2023-09-26" @default.
- W4285281279 title "Support Vector Machine Applications in Water and Environmental Sciences" @default.
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