Abstract

Using Artificial Neural Networks and GIS for Natural Resources Management
Track: Ocean, Coastal, and Marine Resources
Authors: Aymen Solyman

Coastal,near shore, and offshore areas represent some of the difficult areas in which to conduct accurate geologic and geomorphic mapping.Along many coasts, the coastline is continuously changing because of the combination of natural forces and man made modifications.Under such dynamic conditions,topographic base maps are often out of date.In order to monitor and analyze such environmental problems, Remote Sensing (RS) and Geographical Information Systems (GIS)integration are being successfully applied.It has been verified that RS-GIS integration data has lead to time reduction and cost-saving benefits against field surveys that are being hardly updated by using traditional methods.However, GIS capabilities have been concentrated in Boolean operations and conventional spatial statistics when processing multilayered map-based information that has lead to a basic level of "display analysis". Recently, many efforts are being observed intending to achieve more powerful spatial analytic and spatial process modeling tools by combining Artificial Neural Networks (ANN) and Cellular automata (CA).