Abstract
This paper presents a supervised classification method using a neural network to classify typical landforms based on a land cover map and a Digital Elevation Model (DEM). The proposed method classified the landform of Kobe city in Japan into hill, plateau, fan and reclaimed land. As a result, a Self-Organizing Map(SOM) produces the higher classification accuracy than Back Propagation method. Furthermore, we adopted these classified landforms for a ground motion estimation in Kobe during the 1995 Hyogoken Nanbu earthquake, and could obtain detailed ground motion distribution compared with the one based on the Digital National Land Information (DNLI).
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have