Evolving neural network using hybrid genetic algorithm and simulated annealing for rainfall-runoff forecasting

Authors:
Hong Ding;Jiansheng Wu;Xianghui Li
Affiliations:
School of Information Engineering, Wuhan University of Technology, Wuhan, Hubei, P.R. China,Department of Physics and Information Science, Liuzhou Teachers College, Liuzhou, Guangxi, P.R. China;Department of Mathematics and Computer, Liuzhou Teachers College, Liuzhou, Guangxi, P.R. China;Liuzhou City Flood Control and Drainage Project Management Office, Liuzhou, Guangxi, P.R. China
Venue:
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
Year:
2012

Citing 9
Cited 0

Simulated annealing: theory and applications

Simulated annealing: theory and applications
An introduction to genetic algorithms

An introduction to genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
A simulated annealing approach to the traveling tournament problem

Journal of Scheduling
A Novel Nonparametric Regression Ensemble for Rainfall Forecasting Using Particle Swarm Optimization Technique Coupled with Artificial Neural Network

ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
A semiparametric regression ensemble model for rainfall forecasting based on RBF neural network

AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part II
Modeling meteorological prediction using particle swarm optimization and neural network ensemble

ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Genetic drift in genetic algorithm selection schemes

IEEE Transactions on Evolutionary Computation
An Effective Hybrid Semi-Parametric Regression Strategy for Rainfall Forecasting Combining Linear and Nonlinear Regression

International Journal of Applied Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Accurately rainfall---runoff forecasting modeling is a challenging task. Recent neural network (NN) has provided an alternative approach for developing rainfall---runoff forecasting model, which performed a nonlinear mapping between inputs and outputs. In this paper, an effective hybrid optimization strategy by incorporating the jumping property of simulated annealing (SA) into Genetic Algorithm (GA), namely GASA, is used to train and optimize the network architecture and connection weights of neural networks for rainfall---runoff forecasting in a catchment located Liujiang River, which is a watershed from Guangxi of China. This new algorithm incorporates metropolis acceptance criterion into crossover operator, which could maintain the good characteristics of the previous generation and reduce the disruptive effects of genetic operators. The results indicated that compared with pure NN, the GASA algorithm increased the diversity of the individuals, accelerated the evolution process and avoided sinking into the local optimal solution early. Results obtained were compared with existent bibliography, showing an improvement over the published methods for rainfall---runoff prediction.