Development Of An Integrated ANN-GIS Framework For Inland Excess Water Monitoring

Abstract

Inland excess water on the Great Hungarian plain is an environmental and economic problem that has attracted a lot of scientific attention. Most studies have tried to identify the phenomena that cause inland excess water and combined them using regression functions or other linear statistical analysis. In this article, a different approach using a combination of artificial neural networks (ANN) and geographic information systems (GIS) is proposed. ANNs are particularly suitable for classifying large complex non-linear data sets, while GIS has very strong capabilities for geographic analysis. An integrated framework has been developed at our department that can be used to process inland excess water related data sets and use them for training and simulation with different types of ANNs. At the moment the framework is used with a very high resolution LIDAR digital elevation model, colour infrared digital aerial photographs and in-situ fieldwork measurements. The results of the simulations show that the framework is operational and capable of identifying inland excess water inundations.