Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Land Evaluation - An Artificial Intelligence Approach

Land Evaluation - An Artificial Intelligence Approach
View Sample PDF
Author(s): G. Tsoumakas (Aristotle University of Thessaloniki, Greece)and I. Vlahavas (Aristotle University of Thessaloniki, Greece)
Copyright: 2001
Pages: 9
Source title: Environmental Information Systems in Industry and Public Administration
Source Author(s)/Editor(s): Claus Rautenstrauch (Otto von Guericke University, Denmark)and Susanne Patig (Otto-von-Guericke University Magdeburg, Germany)
DOI: 10.4018/978-1-930708-02-0.ch009


View Land Evaluation - An Artificial Intelligence Approach on the publisher's website for pricing and purchasing information.


A major environmental concern of today’s scientists is the inefficient exploitation of natural resources. The land is the ultimate source of wealth and the foundation on which civilization is constructed. Inappropriate land use, leads to destruction of the land resource, poverty and other social problems, and even to the destruction of civilization. To avoid such phenomena, land evaluation is employed, for rational land use planning and appropriate and sustainable use of natural and human resources (Rossiter, 1994). The management of land use is an interdisciplinary activity that relies on large amounts of information from different sources. Land evaluators need to collect information from soil surveyors, climatologists and census takers on land resource. They also need the expert knowledge of soil scientists, agronomists and economists on land use. In addition, land evaluators must select and apply the most appropriate analytical methods to evaluate land qualities and to combine these into overall physical and/or economic suitability. This evaluation is then calibrated against expert judgement and related experience. Finally they must present the results of the evaluation with reports and maps. This output has to be dynamic, considering the continuous refinement of the whole land evaluation process. The above characteristics of land evaluation denote that the management of such a process definitely requires the support of computer systems, especially expert systems, remote sensing and image processing systems, and geographical information systems (GIS). Such systems exist, but they are usually stand-alone units, hence human intervention (land evaluators) for the flow of information from one system towards the other is indispensable. Therefore, integrated systems are highly desirable. The latest research and development trends in this area progressively encompass Artificial Intelligence (AI) techniques to a greater extent, in order to achieve an optimal performance in the analysis of the vast geographical data. Expert systems were included early on, in an effort to model the domain knowledge of land evaluation from experts. Now, such systems introduce fuzzy logic to cope with uncertainty within the data sources and the inference procedure. Machine learning techniques are also included to model the land evaluation procedures when expert knowledge is insufficient or even absent. In general, there exists an amount of both symbolic and non-symbolic AI techniques, which scientists are keen on combining and integrating with traditional land information systems. This chapter is structured as follows. An overview of three of the most used AI techniques in land evaluation problems is given. Following that, the next section introduces ISLE (Tsoumakas and Vlahavas, 1999), an Intelligent System for Land Evaluation that is designed as a framework for the integration of AI techniques with a geographical information system. The final section discusses conclusions and future trends in this field.

Related Content

Himanshi Srivastava, Pinki Saini, Anchal Singh, Sangeeta Yadav. © 2024. 38 pages.
Rakesh Dutta, Jayashri Dutta. © 2024. 16 pages.
Sudha Subburaj, A. Lakshmi Kanthan Bharathi. © 2024. 30 pages.
Hari Shankar Biswas, Sandeep Poddar. © 2024. 15 pages.
Mihaela Rosca, Petronela Cozma, Maria Gavrilescu. © 2024. 35 pages.
Indranee Changmai. © 2024. 28 pages.
Periasamy Palanisamy, M. Kumaresan, M. Maheswaran. © 2024. 19 pages.
Body Bottom