Artificial intelligence and geo-statistical models for stream-flow forecasting in ungauged stations: state of the art
Document Type
Article
Publication Date
1-1-2017
Abstract
Developing an accurate model for discharge estimation techniques of the ungauged river basin is a crucial challenge in water resource management especially in under-development regions. This article is a thorough review of the historical improvement stages of this topic to understand previous challenges that faced researchers, the shortfalls of methods and techniques, how researchers prevailed and what deficiencies still require solutions. This revision focuses on data-driven approaches and GIS-based methods that have improved the accuracy of estimation of hydrological variables, considering their advantages and disadvantages. Past studies used artificial intelligence and geo-statistical methods to forecast the runoff at ungauged river basins, and mapping the spatial distribution has been considered in this study. A recommendation for future research on the potential of a hybrid model utilizing both approaches is proposed and described.
Keywords
Artificial intelligence, Geo-statistical models, Ungauged river
Divisions
fac_eng
Publication Title
Natural Hazards
Volume
86
Issue
3
Publisher
Springer Verlag (Germany)