AI techniques for hydrological modeling and management. II: Optimization

This article is the second of a two-part review of artificial intelligence (AI) based techniques used in hydrologic applications. The first part of this series presented an overview of several AI methods that could be used for prediction and simulation of hydrological systems. In this part, the focus is on AI-based optimization techniques. Hydrological modeling and management problems are often difficult to solve for various reasons and AI based optimization methods tend to be more suited to such problems than traditional optimization or problem-solving techniques. The main reasons for this is that they are population based, meaning that they search from a population of possible solutions rather than a single point; they can handle any type of objective function and constraints and do not require these functions to be continuous or differentiable; they are flexible in their application; and can be implemented on parallel hardware. However, there are a number of these optimization methods available and, by discussing their advantages, limitations and previous applications in the field of hydrology and water resources management, this review attempts to provide guidance as to which methods are best suited to which problems.