Affiliation:
1. International Hellenic University
2. University if Thessaly
Abstract
In this study an Artificial Neural Network for the simulation of flood phenomena in a natural area was developed. Then this network was implemented in the urban area of a Greek city (Amyntaio, Florina). The neural networks have many advantages: non-linearity, adaptability, input-output mapping, indicative response, damage resistance, possibility of implementation with VLSI (Very Large Scale Integration) technology, content related information and analysis and design uniformity. With neural networks, mathematical simulation of the considered phenomenon is not attempted, but the extraction of quantitative conclusions for specific data, based on similar cases. With the development and implementation of this network all the points that are in risk for flood are identified. The results showed that the help of an Artificial Neural Network in these cases is crucial for the future decisions in cases of flood phenomena.
Publisher
Trans Tech Publications Ltd
Reference34 articles.
1. S. Tohma, S. Igata, S. Rainfall estimation from GMS imagery data using neural network. WIT Trans. on Ecol. and the Envir. 7 (1970).
2. Predicting Indian monsoon rainfall: a neural network approach;Navone;Climate Dynamics
3. Multivariate modelling of water resources time series using artificial neural networks;RAMAN;Hydrological Sciences Journal
4. A.H. Halff, H.M. Halff, M. Azmoodeh. Predicting runoff from rainfall using neural networks. In Eng. Hydr. (1993) 760-765.
5. Artificial Neural Networks in Hydrology. II: Hydrologic Applications;Journal of Hydrologic Engineering