Publication: Application of computational intelligence methods in modelling river flow prediction: A review
Date
2015
Authors
Zaini N.
Malek M.A.
Yusoff M.
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
Rainfall and river flow are one of the most difficult elements of hydrological cycle to predict. This is due to tremendous range of variability it displays over a wide range of scale both in terms of space and time. The situation is further aggravated by the fact that rainfall-runoff is also very difficult to measure at scales of interest to hydrology and climatologic. Computational intelligence techniques provide efficient and fast results for modelling non-linear and complex data. Computational intelligence methods which inspired by the capability of learning that derive meaning from unknown relationship provide guidance for a sensible decision making. This advantage creates them adaptable and talented methods for modelling real world problems. This paper is an attempt to present the introduction to computational intelligence methods; applications to river flow modelling and its performance with regards to the parameter and method used. The methods include artificial neural networks, fuzzy logic, evolutionary computation, support vector machine; swarm intelligence and hybrid method are critically compared mainly on computational results and prediction accuracy. � 2015 IEEE.
Description
Artificial intelligence; Arts computing; Decision making; Evolutionary algorithms; Forecasting; Fuzzy neural networks; Intelligent computing; Neural networks; Rain; Rivers; Stream flow; Support vector machines; Computational intelligence methods; Computational intelligence techniques; Computational results; Hydrological cycles; Neural networks , fuzzy logic; Prediction accuracy; River flow models; River flow prediction; Fuzzy logic