TY - JOUR
T1 - Estimation of significant wave height in shallow lakes using the expert system techniques
AU - Altunkaynak, Abdüsselam
AU - Wang, Keh Han
PY - 2012/2/15
Y1 - 2012/2/15
N2 - Significant wave height is an important hydrodynamic variable for the design application and environmental evaluation in coastal and lake environments. Accurate prediction of significant wave height can assist the planning and analysis of lake and coastal projects. In this study, the Genetic Algorithm (GA) is used as the optimization technique to better predict model parameters. Also, Kalman Filtering (KF) is used for prediction of significant wave height from wind speed. KF technique makes predictions based on stochastic and dynamic structures. The integrated Geno Kalman Filtering (GKF) technique is applied to develop predictive models for estimation of significant wave height at stations LZ40, L006, L005 and L001 in Lake Okeechobee, Florida. The results show that the GKF methodology can perform very well in predicting the significant wave height and produce lower mean relative error and mean-square error than those from Artificial Neural Network (ANN) model. The superiority of GKF method over ANN is presented with comparisons of predicted and observed significant wave heights.
AB - Significant wave height is an important hydrodynamic variable for the design application and environmental evaluation in coastal and lake environments. Accurate prediction of significant wave height can assist the planning and analysis of lake and coastal projects. In this study, the Genetic Algorithm (GA) is used as the optimization technique to better predict model parameters. Also, Kalman Filtering (KF) is used for prediction of significant wave height from wind speed. KF technique makes predictions based on stochastic and dynamic structures. The integrated Geno Kalman Filtering (GKF) technique is applied to develop predictive models for estimation of significant wave height at stations LZ40, L006, L005 and L001 in Lake Okeechobee, Florida. The results show that the GKF methodology can perform very well in predicting the significant wave height and produce lower mean relative error and mean-square error than those from Artificial Neural Network (ANN) model. The superiority of GKF method over ANN is presented with comparisons of predicted and observed significant wave heights.
KW - Artificial Neural Network
KW - Dynamic model
KW - Genetic Algorithms
KW - Kalman Filtering
KW - Significant wave height
KW - Stochastic
UR - http://www.scopus.com/inward/record.url?scp=80255131412&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2011.08.106
DO - 10.1016/j.eswa.2011.08.106
M3 - Article
AN - SCOPUS:80255131412
SN - 0957-4174
VL - 39
SP - 2549
EP - 2559
JO - Expert Systems with Applications
JF - Expert Systems with Applications
IS - 3
ER -