1. Introduction
Since the Stanford Watershed Model was developed by Crawford and Linsley in 1966 [1], there has been much research on the numerical hydrologic modeling in watershed hydrology. This activity has been enhanced by ever-expanding computing power and growing capability to observe, store, retrieve, and manage hydrologic data (i.e., remotely sensed rainfall and automated measured streamflow). These enhancements result in a significant revolution in the development of watershed models. Not only do these models simulate watershed hydrology more accurately, but they also simulate other components in the ecobiological system and socio-economic continuum. In addition to the simulation of various hydrologic components, models have been used for the simulation of water quality and ecologic processes, risk assessment and uncertainty analysis, and evaluation of environmental impact within watersheds. Many models have been developed over years for different purposes. Nevertheless, many of the models share structural similarities because their underlying physical laws and assumptions are the same while some of other models possess distinct differences. Based on the description of various processes and characteristics in watersheds, models can be classified as lumped or distributed, deterministic or stochastic or mixed [2]. On the same note, research in artificial intelligence increased dramatically during the 1990s which has been widely used in many hydrologic fields such as artificial neural networks, genetic algorithm, fuzzy logic, and decision tree algorithms.