Implicit stochastic optimization with data mining for reservoir system operation

Optimizing reservoir system operation has been a major area of study in water resources systems. Implicit stochastic optimization (ISO) is the most popular approach regarding that most stochastic aspects of the problem are implicitly included. Many mathematical programming techniques have been applied in ISO. Recent developments in the field of data mining techniques are shown their potential as an alternative approach for reservoir system optimization. The purpose of this paper is a review of ISO methods using data mining. After briefly introduce the conventional techniques and their limitations, new techniques of data mining such as genetic algorithms, neural networks, decision tree, and particle swarm optimization are described in detail.