Introduction

Abstract

Problems involving the process of water resources and environmental management such as simulation of natural events, warning of natural disasters, and impact analysis of development scenarios are of significant importance in case of the changing environment. Considering the complexity of natural phenomena as well as our limited knowledge of mathematical modeling, this might be a challenging problem. Recently, development of data-driven models has improved the application of specific tools to be used through the complex process of real-world modeling. Soft computing and statistical models are two common groups of data-driven models that could be employed to solve water resources and environmental problems. Data-driven models are among mathematical models, which use experimental data to analyze real-world phenomena. In contrast to physical models, they do not need a specific laboratory setup so are significantly cheaper. Also, in contrast to the analytical models, data-driven models can be used for the problems where we do not have enough knowledge about the intrinsic complexity of the phenomena. This chapter presents a brief review of different types of models that could be used for modeling water resources and environmental problems, reviews the process of model selection for a specific problem, and investigates the general approach of using data-driven models. The advanced stage of developing a model is discussed in the last section.