Shoreline erosion problem may cause the possibility of losing land, and this must be considered particularly for a country which is composed of islands. In this study, six sandy nonlinear beaches located at Kenting National Park of Taiwan, were investigated in accordance with aerial survey maps taken in three different years. The images of spatial information were loaded into computerized graphical software, and the set fold function was used to make comparison for obtaining the tendency of sand line variation for the three different times. Based on the available dataset, a back propagation neural network model was then developed for forecasting the long term variation of sand line at each beach. The results showed that the sand line does have a phenomenon of rise and fall at some local regions, but the total area of each beach does not undergo significant changes and are within an acceptable range of error from a statistical standpoint. The neural network model used in this study might offer a new approach for solving this type of nonlinear problem, and the result obtained might provide a valuable reference for a relevant agency working in the area studied.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.