Neural Network Application Based on GIS and Matlab to Evaluation of Flood Risk

DOI

10.2991/rsete.2013.72How to use a DOI?

Keywords

flood damage; risk assessment; BP neural network; geographic information system

Abstract

In order to test the Artificial Neural Networks (ANN) in the applicability of flood risk assessment, this paper applies the traditional BP neural networks (BPNN), radial basis function neural networks (RBFNN) and probabilistic neural networks (PNN) to establish flood risk assessment model using MATLAB combined with GIS technology. It is observed that BPNN is superior among three methods. Taking Beijiang River basin as a case study, the risk assessment map based on BPNN model shows that the dangerous areas are mainly located in these areas: Sihui, Qingyuan city, Fogang, northwest Huaiji, central Yangshan, central Yingde, northeast Nanxiong and so on. Compared with a few historical large floods, above results can better reflect the actual situation of flood risk in Beijiang River basin, which validate the rationality of the presented model and provide a reference for flood control and disaster assessment.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Lirong Song
AU  - Shiwei Zhao
AU  - Weilin Liao
AU  - Zhaoli Wang
PY  - 2013/08
DA  - 2013/08
TI  - Neural Network Application Based on GIS and Matlab to Evaluation of Flood Risk
BT  - Proceedings of the 2013 the International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE 2013)
PB  - Atlantis Press
SP  - 294
EP  - 297
SN  - 1951-6851
UR  - https://doi.org/10.2991/rsete.2013.72
DO  - 10.2991/rsete.2013.72
ID  - Song2013/08
ER  -