Performance of two neural network models in bathymetry

Neural networks are widely used as predictors in several fields of applications, such as prediction of shallow water depth. The purpose of this study is to investigate the performance of two artificial neural networks models as potential methods in bathymetry. A comparison approach is used to evaluate network models, the regression tree and an inversion model. The high-resolution IKONOS and moderate-resolution Landsat satellite images serve as the case studies, and results based on the root mean square errors and coefficient of determination (R2) show that artificial neural networks outperform the inversion model and the regression tree.

[1]  Muhaini Othman,et al.  Evolving spiking neural networks for personalised modelling, classification and prediction of spatio-temporal patterns with a case study on stroke , 2014, Neurocomputing.

[2]  Anthony M. Filippi,et al.  Geographically Adaptive Inversion Model for Improving Bathymetric Retrieval From Satellite Multispectral Imagery , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Hassan B. Kazemian,et al.  Neural Network Approaches for Noisy Language Modeling , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[4]  Babak Naimi,et al.  Remotely Sensed Empirical Modeling of Bathymetry in the Southeastern Caspian Sea , 2013, Remote. Sens..

[5]  Zhi Huang,et al.  Predictive modelling of seabed sediment parameters using multibeam acoustic data: a case study on the Carnarvon Shelf, Western Australia , 2012, Int. J. Geogr. Inf. Sci..

[6]  Timothy M. Young,et al.  An extension of regression trees to generate better predictive models , 2012 .

[7]  K. Günaydın,et al.  Tsunami run-up height forecasting by using artificial neural networks , 2011 .

[8]  Andrea Masini,et al.  Approaching bathymetry estimation from high resolution multispectral satellite images using a neuro-fuzzy technique , 2011 .

[9]  Özçelik Ceyhun,et al.  Remote sensing of water depths in shallow waters via artificial neural networks , 2010 .

[10]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[11]  Tülay Yildirim,et al.  Genetic optimization of GRNN for pattern recognition without feature extraction , 2008, Expert Syst. Appl..

[12]  Juan J. Flores,et al.  The application of artificial neural networks to the analysis of remotely sensed data , 2008 .

[13]  F. Magron.  BATHYMETRIC MAPPING USING A COMBINATION OF SONAR DATA AND SATELLITE IMAGERY , 2008 .

[14]  Zhongping Lee,et al.  Improving the accuracy of water and bottom properties derived from remote sensing reflectance via artificial neural network , 2007, SPIE Optical Engineering + Applications.

[15]  Hikmet Kerem Cigizoglu,et al.  Generalized regression neural network in modelling river sediment yield , 2006, Adv. Eng. Softw..

[16]  Seree Supharatid,et al.  Ocean wave forecasting in the Gulf of Thailand during typhoon Linda 1997: Hard and soft computing approaches , 2005 .

[17]  Dimitri P. Solomatine,et al.  Neural networks and M5 model trees in modelling water level-discharge relationship , 2005, Neurocomputing.

[18]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[19]  Richard Labib,et al.  Performance of Neural Networks in Daily Streamflow Forecasting , 2002 .

[20]  Dirk Tomandl,et al.  A Modified General Regression Neural Network (MGRNN) with new, efficient training algorithms as a robust 'black box'-tool for data analysis , 2001, Neural Networks.

[21]  R. Holyer,et al.  Coastal bathymetry from hyperspectral observations of water radiance , 1998 .

[22]  L. O. Hall,et al.  A NEURAL NETWORK APPROACH TO DERIVING OPTICAL PROPERTIES AND DEPTHS OF SHALLOW WATERS , 1998 .

[23]  James V. Hansen,et al.  Learning experiments with genetic optimization of a generalized regression neural network , 1996, Decis. Support Syst..

[24]  Martin T. Hagan,et al.  Neural network design , 1995 .

[25]  D. F. Specht,et al.  Experience with adaptive probabilistic neural networks and adaptive general regression neural networks , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[26]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.