Panagiotis Lefakis

A computer-system that classifies the prefectures of Greece in forest fire risk zones using fuzzy sets

Abstract All of the Mediterranean countries face a serious forest fire problem. The main factors that affect the problem of forest fires in Greece are vegetation, climate conditions and most of all, arson (Proceedings of Forest Fires in Greece, Thessaloniki, 1990, p. 97). In Greece, after 1974, the number of forest fires and the total burned areas have risen dramatically. The design of an effective fight and prevention policy is a very important matter, as it can minimize the destruction. This paper describes an expert system that classifies the prefectures of Greece into forest fire risk zones, using a completely new methodology. The concept of fuzzy expected intervals (F.E.I.) was defined by Kandel and Byatt (Proc. IEEE, 66, 1978, 1619) and offered a very good approach towards forest fire risk classification. Fuzzy expected intervals are narrow intervals of values that best describe the forest fire problem in the country or a part of the country for a certain time period. Fuzzy logic was applied to produce a F.E.I. for each prefecture of the country. A successful classification of the prefectures of Greece (in forest fire risk zones) was performed by the expert system by comparing the produced fuzzy expected intervals to each other and by using a supervised machine learning algorithm that assigns a certain weight of forest fire risk to each prefecture (Machine Learning, John Wiley and Sons, 1995).

Development of a decision support system for the study of an area after the occurrence of forest fire

There is a great diffusion of modern information systems in all areas of science. In the case of forestry, new information tools have emerged during the last 15 years which have helped to improve the work of foresters. Decision support systems (DSSs) are applications which are designed to help managers in the task of decision making, by accelerating the relevant decision-making processes, while simultaneously focusing on the conservation of natural, financial and human resources. In this paper, we describe the development of a DSS which has been designed to help managers in the process of decision making, in relation to areas that have been burnt by forest fires. In addition, the above system also provides the user with the capacity to create hypothetical (what-if) scenarios in order to achieve the best form of intervention. The relevant software was created using Visual C# and the weights of the various parameters were calculated using multi-criteria decision analysis.

Evaluation of artificial neural networks as a model for forecasting consumption of wood products

In specific sciences, such as forest policy, the need for anticipation becomes more urgent because it has to manage valuable natural resources whose protection and sustainable management is rendered essential. In this paper, a modern method has been used, known as artificial neural networks ANNs. In order to forecast the necessary future volumes of timber in Greece, a neural network has been developed and trained, using a variety of time series derived from the database of the Food and Agriculture Organisation of the United Nations FAO concerning Greece as external values and as internal value the Consumer Price Index has been used. Comparing the results of this project with linear and non-linear econometric forecasting models, it has been found that neural networks correspond, as confirmed by the econometric indicators MAPE average absolute percentage error and RMSE the square root of the percentage by the average sum of squares differences.

A pilot prototype decision support system for recognition of Greek forest species

Expert Systems constitute a part of the broader field of Artificial Intelligence. There are applications which aim in the encasement of knowledge of an expert, in a certain area of science, in a computer application. Thus it is possible for the further use of the experts’ knowledge even by people who cannot directly communicate with an expert. In the field of environmental sciences expert systems were created for dealing with various problems. The purpose of this paper is the development of an expert system which would be able to identify wood produced by Greek forest species. The system will be available for wood industry professionals, for students as a support tool in the education process, and for anyone interested in wood identification.

Wood species recognition through multidimensional texture analysis

Abstract Wood recognition is a crucial task for wood sciences and industries, since it leads to the identification of the anatomical features and physical properties of wood. Traditionally, the recognition process relies almost exclusively on human experts, who are based on various characteristics of wood, such as color, structure and texture. However, there are numerous types of wood species in the nature that are difficult to be identified even by experienced scientists. Towards this end, in this paper we propose a novel approach for automated wood species recognition through multidimensional texture analysis. By taking advantage of the fact that static wood images contain periodic spatially-evolving characteristics, we introduce a new spatial descriptor considering each wood image as a collection of multidimensional signals. More specifically, the proposed methodology enables the representation of wood images as concatenated histograms of higher order linear dynamical systems produced by vertical and horizontal image patches. The final classification of images, i.e., histogram representations, into wood species, is performed using a Support Vector Machines (SVM) classifier. For the evaluation of the proposed method, a dataset, namely “WOOD-AUTH”, consisting of more than 4200 wood images (from cross, radial and tangential sections of normal wood structure) of twelve common wood species existing in Greek territory, was created. Experimental results presented in this paper show the great potential of the proposed methodology, which, despite a small number of misclassification cases with regards to both anatomically similar and different species, outperforms a number of state of the art approaches, yielding a classification rate of 91.47% in wood cross sections.