Review - Artificial Intelligence Based Modelling of Hydrological Processes

Hydrological processes such as runoff and contaminant transport are usually affected by various complex interrelated variables. Moreover, uncertainties in variables estimate are the common stamp of these processes. Due to this complex nature, Physical modeling of any hydrological system requires availability of large, accurate and detailed data related to all influencing variables, which are not always available due to financial and technical constraints. This may lead to deficiencies in model’s performance which in turn, negatively affect hydrological planning and policy drawing. To address these shortcomings, artificial intelligence (AI) based techniques have been recently used as alternative tools to traditional physical hydrological models. These techniques have been proved to be successful and effective in tackling wide spectrum of challenging hydrological processes. This article is intended to serve as an introductory review of application of two AI techniques namely, artificial neural networks (ANNs) and support vector machine (SVM) in various hydrological applications. In this article, ANNs and SVM theoretical background coupled with their strength points that make them suitable for hydrological modeling were briefly described. Moreover, various examples of successful applications of ANNs and SVM for modeling different hydrological processes were also provided.