Decision Support System Based on Artificial Intelligence, GIS and Remote Sensing for Sustainable Public and Judicial Management

Authors

  • Georgios N. Kouziokas
  • Konstantinos Perakis

DOI:

https://doi.org/10.14207/ejsd.2017.v6n3p397

Abstract

The reformation of public and judicial management has become an important challenge nowadays with the development of the Information and Communication Technologies and also with the rapid development of the geospatial and remote sensing technologies. This paper presents a decision support system that was developed based on Artificial Intelligence, Geographic Information Systems and Remote Sensing for implementing Sustainable decision-making strategies. Several indicators were defined in order to build a decision-making system that will help the authorities to apply the adequate public management strategies. Geographic Information Systems were used in order to process and visualize spatial data that are important in public decision making, such as environmental data, infrastructure data and crime data. Remote sensing was used regarding the manipulation of satellite images that would facilitate public decision making. Artificial intelligence was used to build neural network models important in the decision making. The developed system provides a more holistic view of the factors that affect public and judicial management and aims at improving the framework of public decision making and spatial planning and also at supporting the application of the most adequate public management policies.


Keywords: Artificial intelligence; Decision Support System; Environmental information; Geographic information systems; Public management; Remote sensing; Sustainable development.

Downloads

Published

2017-10-01

How to Cite

Kouziokas, G. N., & Perakis, K. (2017). Decision Support System Based on Artificial Intelligence, GIS and Remote Sensing for Sustainable Public and Judicial Management. European Journal of Sustainable Development, 6(3), 397. https://doi.org/10.14207/ejsd.2017.v6n3p397

Issue

Section

Articles