A comparison between three short-term shoreline prediction models - Goncalves, R.; Awange, Joseph; Krueger, C.; Heck, B.; Coelho, L. (Elsevier Ltd, 2012)
Monitoring and management of shorelines along populated coastal areas is a very important task, but remains a difficult endeavor. The historical information used for short-term analysis and prediction are always underpinned by uncertainties associated with old data. Predictions of shoreline positions normally depend on the accuracy of the input data as well as the validity of the mathematical models used. With the requirement to study shoreline changes along the Parana (PR) coast in Brazil, it was necessary to obtain related cartographic information, which included temporal shoreline data obtained from orthophotos. In this contribution, photogrammetric together with GPS data are used to compare the capability of three shoreline prediction models; linear regression, robust parameter estimation, and neural network to predict the 2008 Parana shoreline position, which is then validated using the GPS measured position of 2008. The results indicate a MAPE (Mean Absolute Percentage Error) of 0.61% for the linear regression, 0.14% for the robust estimation, and 0.33% for the artificial neural network method. Although the coefficient of determinant (R2) value for the neural network was the best, i.e., 0.997 compared to 0.994 for the robust model and 0.984 for the linear regression, its maximum deviation from the control values (i.e., 16.46) was almost twice that of robust model (7.63). On the one hand, the robust estimation model provides a more suitable approach for managing outliers in shoreline prediction, and also validating traditional methods such as linear regression. On the other hand, the neural network method offers an alternative approach to the robust prediction model. The results of the study highlight the importance of a model choice for predicting the shoreline position.