Stefanos Spartalis

Fundamental fuzzy relation concepts of a D.S.S. for the estimation of natural disasters' risk (The case of a trapezoidal membership function)

The effective protection from natural disasters requires the development of a rational and sensible protection and prevention policy. This project deals with the development and testing of a decision support system that acts on two levels. On the first level, it estimates the annual forest fire risk for each area of Greece using a fuzzy Trapezoidal membership function. Reference is done to past work in this area and past results of forest fire risk estimation (using other models) were compared to the results of this system. On the second level, it forecasts a narrow expected closed interval for the burned area, using a fuzzy expected interval model. It is the first time that such forecasts are produced and such results are obtained. Physically and operationally, the decision support system consists of two parts. The risk estimation part is more straightforward and it was developed in MS-Access in order to have the ability to store and use a vast amount of data. The forecasting part was developed in a decision support system shell, in order to have a sound Inference mechanism.

Application of fuzzy T-norms towards a new Artificial Neural Networks' evaluation framework: A case from wood industry

The development of an Artificial Neural Network requires proper learning and testing procedures that adopt error correction processes and algorithms. Monitoring of processing elements values and overall performance is one of the most critical issues of an Artificial Neural Network development process. This should happen as the network evolves and it is the actual task that enables the developer to make informed decisions about the proper network topology, math functions, training times and learning parameters. This manuscript presents an innovative and flexible error validation framework applying fuzzy logic. It offers an approach capable of viewing the task of performance improvement under several different perspectives. Then the developer has the capacity to decide which performance is most suitable according to his standards. The model has been tested for a specific industrial case study with actual data and a comparison to the existing methods is presented.

Fuzzy Cognitive Maps for Long-Term Prognosis of the Evolution of Atmospheric Pollution, Based on Climate Change Scenarios: The Case of Athens

Air pollution is related to the concentration of harmful substances in the lower layers of the atmosphere and it is one of the most serious problems threatening the modern way of life. Determination of the conditions that cause maximization of the problem and assessment of the catalytic effect of relative humidity and temperature are important research subjects in the evaluation of environmental risk. This research effort describes an innovative model towards the forecasting of both primary and secondary air pollutants in the center of Athens, by employing Soft Computing Techniques. More specifically, Fuzzy Cognitive Maps are used to analyze the conditions and to correlate the factors contributing to air pollution. According to the climate change scenarios till 2100, there is going to be a serious fluctuation of the average temperature and rainfall in a global scale. This modeling effort aims in forecasting the evolution of the air pollutants concentrations in Athens as a consequence of the upcoming climate change.

A Fuzzy Inference System Using Gaussian Distribution Curves for Forest Fire Risk Estimation

This paper describes the development of a fuzzy inference system under the MATLAB platform. The system uses three distinct Gaussian distribution fuzzy membership functions in order to estimate the partial and the overall risk indices due to wild fires in the southern part of Greece. The behavior of each curve has been investigated in order to determine which one fits better for the specific problem and for the specific areas. Regardless the characteristics of each function, the risky areas have been spotted from 1984 till 2007. The results have shown a reliable performance over time and they encourage its wider use in the near future.

An innovative risk evaluation system estimating its own fuzzy entropy

This study presents an original mathematical model and a prototype computer decision support system for the management of natural disasters risk. The system not only estimates the degree of risk for each area under study but it evaluates itself by calculating the entropy of its output as well. The degree of risk can be very useful for the design of effective protection and prevention policy. On the other hand, the system can be considered as an intelligent one as it has the ability to offer the potential user a particular type of quality judgment for its risk output. The whole model is based on fuzzy algebra concepts and principles and the software has been developed in MS-Access. The original contribution of this paper is not only the computer system model, but also its application to the torrential risk of Greek Thrace using actual data.

An intelligent Fuzzy Inference System for Risk Estimation Using Matlab Platform: the Case of Forest Fires in Greece

This paper aims in the design of an intelligent Fuzzy Inference System that evaluates risk due to natural disasters. Though its basic framework can be easily adjusted to perform in any type of natural hazard, it has been specifically designed to be applied in the case of forest fire risk in the area of the Greek terrain. Its purpose is to create a descending list of the areas under study, according to their degree of risk. This will provide important aid towards the task of distributing properly fire fighting resources. It is designed and implemented in Matlabs integrated Fuzzy Logic Toolbox. It estimates two basic kinds of risk indices, namely the man caused risk and the natural one. The fuzzy membership functions used in this project are the Triangular and the Semi-Triangular.

A Hybrid Soft Computing Approach Producing Robust Forest Fire Risk Indices

Forest fires are one of the major natural disaster problems of the Mediterranean countries. Their prevention - effective fighting and especially the local prediction of the forest fire risk, requires the rational determination of the related factors and the development of a flexible system incorporating an intelligent inference mechanism. This is an enduring goal of the scientific community. This paper proposes an Intelligent Soft Computing Multivariable Analysis system (ISOCOMA) to determine effective wild fire risk indices. More specifically it involves a Takagi-Sugeno-Kang rule based fuzzy inference approach, that produces partial risk indices (PRI) per factor and per subject category. These PRI are unified by employing fuzzy conjunction T-Norms in order to develop pairs of risk indices (PARI). Through Chi Squared hypothesis testing, plus classification of the PARI and forest fire burned areas (in three classes) it was determined which PARI are closely related to the actual burned areas. Actually we have managed to determine which pairs of risk indices are able to determine the actual burned area for each case under study. Wild fire data related to specific features of each area in Greece were considered. The Soft computing approach proposed herein, was applied for the cases of Chania, and Ilia areas in Southern Greece and for Kefalonia island in the Ionian Sea, for the temporal period 1984–2004.

Fuzzy graphs: algebraic structure and syntactic recognition

Directed fuzzy hypergraphs are introduced as a generalization of both crisp directed hypergraphs and directed fuzzy graphs. It is proved that the set of all directed fuzzy hypergraphs can be structured into a magmoid with operations graph composition and disjoint union. In this framework a notion of syntactic recognition inside magmoids is defined. The corresponding class is proved to be closed under boolean operations and inverse morphisms of magmoids. Moreover, the language of all strongly connected fuzzy graphs and the language that consists of all fuzzy graphs that have at least one directed path from the begin node to the end node through edges with membership grade 1 are recognizable. Additionally, a useful characterization of recognizability through left derivatives is also achieved.

A Generalized Fuzzy-Rough Set Application for Forest Fire Risk Estimation Feature Reduction

This paper aims in the reduction of data attributes of a fuzzy-set based system for the estimation of forest fire risk in Greece, with the use of rough-set theory. The aim is to get as good results as possible with the use of the minimum amount of data attributes possible. Data manipulation for this project is done in MS-Access. The resulting data table is inserted into Matlab in order to be fuzzified. The final result of this clustering is inserted into Rossetta, which is a Rough set exploration software, in order to estimate the reducts. The risk estimation is recalculated with the use of the reduct set in order to measure the accuracy of the final minimum attribute set. Nine forest fire risk factors were taken into consideration for the purpose of this paper and the Greek terrain was separated into smaller areas, each concerning a different Greek forest department.

A Neuro-Fuzzy Hybridization Approach to Model Weather Operations in a Virtual Warfare Analysis System

Weather operations play an important and integral part of planning, execution and sustainment of mission operations. In this paper, a neuro-fuzzy hybridization technique is applied to model the weather operations and predict its impact on the effectiveness of air tasking operations and missions. Spatio-temporal weather data from various meteorological sources are collected and used as the input to a neural network and the predicted weather conditions at a given place is classified based on fuzzy logic. The corresponding fuzzy rules are generated forming the basis for introducing the weather conditions in the evaluation of the effectiveness of the military mission plans. An agent-based architecture is proposed where agents representing the various weather sensors feed the weather data to the simulator, and a weather agent developed using neuro-fuzzy hybridization computes the weather conditions over the flight plan of the mission. These rules are then used by the Mission Planning and Execution system that evaluates the effectiveness of military missions in various weather conditions.