Forecasting Sea Water Levels at Mukho Station, South Korea Using Soft Computing Techniques

The accuracy of three different data-driven methods, namely, Gene Expression Programming (GEP), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN), is investigated for hourly sea water level prediction at the Mukho Station in the East Sea (Sea of Japan). Current and four previous level measurements are used as input variables to predict sea water levels up to 1, 24, 48, 72, 96 and 120 hours ahead. Three statistical evaluation parameters, namely, the correlation coefficient, the root mean square error and the scatter index are used to assess how the models perform. Investigation results indicate that, when compared to measurements, for +1h prediction interval, all three models perform well (with average values of R = 0.993, RMSE = 1.3 cm and SI = 0.04), with slightly better results produced by the ANNs and ANFIS, while increasing the prediction interval degrades model performance.

[1]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[2]  M. C. Deo,et al.  Tide Prediction Using Neural Networks , 1998 .

[3]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[4]  Özgür Kisi,et al.  Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations , 2011, Comput. Geosci..

[5]  Makarand Deo,et al.  Prediction of breaking waves with neural networks , 2003 .

[6]  H. P. Ritzema,et al.  Gravity outlet structures. , 1994 .

[7]  Ozgur Kisi,et al.  Wind Speed Prediction by Using Different Wavelet Conjunction Models , 2011 .

[8]  Ching-Piao Tsai,et al.  BACK-PROPAGATION NEURAL NETWORK IN TIDAL-LEVEL FORECASTING , 2001 .

[9]  Dina Makarynska,et al.  Artificial neural networks in wave predictions at the west coast of Portugal , 2005, Comput. Geosci..

[10]  R. S.  Sir Isaac Newton's Mathematical Principles of Natural Philosophy and his System of the World , 1935, Nature.

[11]  Ozgur Kisi,et al.  Forecasting Water Level Fluctuations of Urmieh Lake Using Gene Expression Programming and Adaptive Neuro-Fuzzy Inference System , 2012 .

[12]  Y. Hong,et al.  Hydrological modeling using a dynamic neuro-fuzzy system with on-line and local learning algorithm , 2009 .

[13]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[14]  Özgür Kisi,et al.  Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia , 2013, Comput. Geosci..

[15]  D. Legates,et al.  Evaluating the use of “goodness‐of‐fit” Measures in hydrologic and hydroclimatic model validation , 1999 .

[16]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[17]  Ozgur Kisi,et al.  Prediction of Short-Term Operational Water Levels Using an Adaptive Neuro-Fuzzy Inference System , 2011 .

[18]  Li-Ching Lin,et al.  Multi-point tidal prediction using artificial neural network with tide-generating forces , 2006 .

[19]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[20]  O. Makarynskyy,et al.  Predicting sea level variations with artificial neural networks at Hillarys Boat Harbour, Western Australia , 2004 .

[21]  M. C. Deo,et al.  Real-time wave forecasting using genetic programming , 2008 .

[22]  Oleg Makarynskyy,et al.  Wave Prediction and Data Supplementation with Artificial Neural Networks , 2007 .

[23]  O. Kisi,et al.  Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model , 2010 .

[24]  Dina Makarynska,et al.  Predicting sea-level variations at the Cocos (Keeling) Islands with artificial neural networks , 2008, Comput. Geosci..

[25]  Seyed Jamshid Mousavi,et al.  APPLICATION OF FUZZY INFERENCE SYSTEM IN THE PREDICTION OF WAVE PARAMETERS , 2005 .

[26]  Paul Schureman,et al.  Manual of harmonic analysis and prediction of tides / by Paul Schureman. , 2019 .

[27]  M. C. Deo,et al.  Filling up gaps in wave data with genetic programming , 2008 .

[28]  N. Fernández,et al.  Neuro-fuzzy modeling for level prediction for the navigation sector on the Magdalena River (Colombia) , 2010 .

[29]  R. Poli,et al.  Covariant Parsimony Pressure for Genetic Programming , 2007 .

[30]  Vidroha Debroy,et al.  Genetic Programming , 1998, Lecture Notes in Computer Science.

[31]  Oleg Makarynskyy,et al.  A Combined Harmonic Analysis–Artificial Neural Network Methodology for Tidal Predictions , 2007 .

[32]  A. Bárdossy,et al.  Development of a fuzzy logic-based rainfall-runoff model , 2001 .

[33]  M. Erol.  Fuzzy logic model approaches to daily pan evaporation estimation in western Turkey , 2005 .

[34]  Ozgur Kisi,et al.  Comparison of two different data-driven techniques in modeling lake level fluctuations in Turkey , 2009 .

[35]  M. C. Deo,et al.  Forecasting wind with neural networks , 2003 .

[36]  Cândida Ferreira,et al.  Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence , 2014, Studies in Computational Intelligence.

[37]  Özgür Kisi,et al.  Forecasting daily lake levels using artificial intelligence approaches , 2012, Comput. Geosci..

[38]  Tsong-Lin Lee.  Back-propagation neural network for long-term tidal predictions , 2004 .

[39]  Cândida Ferreira,et al.  The Entities of Gene Expression Programming , 2006 .

[40]  Shreenivas Londhe,et al.  Soft computing approach for real-time estimation of missing wave heights , 2008 .

[41]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[42]  M. Keskin,et al.  Fuzzy logic model approaches to daily pan evaporation estimation in western Turkey / Estimation de l’évaporation journalière du bac dans l’Ouest de la Turquie par des modèles à base de logique floue , 2004 .

[43]  Fi-John Chang,et al.  Adaptive neuro-fuzzy inference system for prediction of water level in reservoir , 2006 .

[44]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..