Application of computational intelligence methods in modelling river flow prediction: A review
- Resource Type
- Conference
- Authors
- Zaini, Nuratiah; Malek, Marlinda Abdul; Yusoff, Marina
- Source
- 2015 International Conference on Computer, Communications, and Control Technology (I4CT) Computer, Communications, and Control Technology (I4CT), 2015 International Conference on. :370-374 Apr, 2015
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Fields, Waves and Electromagnetics
General Topics for Engineers
Robotics and Control Systems
Signal Processing and Analysis
Support vector machines
Computational modeling
Predictive models
Rivers
Artificial neural networks
Accuracy
Autoregressive processes
River Flow Model
Computational Intelligence
Artificial Neural Network
Evolutionary Computation
Support Vector Machine
- Language
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.