Review-Artificial Intelligence Based Modelling of Hydrological Processes (original) (raw)

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, artifici...

Sign up for access to the world's latest research.

checkGet notified about relevant papers

checkSave papers to use in your research

checkJoin the discussion with peers

checkTrack your impact