Vardis-Dimitris Anezakis

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.

FuSSFFra, a fuzzy semi-supervised forecasting framework: the case of the air pollution in Athens

Mining hidden knowledge from available datasets is an extremely time-consuming and demanding process, especially in our era with the vast volume of high-complexity data. Additionally, validation of results requires the adoption of appropriate multifactor criteria, exhaustive testing and advanced error measurement techniques. This paper proposes a novel Hybrid Fuzzy Semi-Supervised Forecasting Framework. It combines fuzzy logic, semi-supervised clustering and semi-supervised classification in order to model Big Data sets in a faster, simpler and more essential manner. Its advantages are clearly shown and discussed in the paper. It uses as few pre-classified data as possible while providing a simple method of safe process validation. This innovative approach is applied herein to effectively model the air quality of Athens city. More specifically, it manages to forecast extreme air pollutants’ values and to explore the parameters that affect their concentration. Also it builds a correlation between pollution and general climatic conditions. Overall, it correlates the built model with the malfunctions caused to the city life by this serious environmental problem.

Semi-supervised Hybrid Modeling of Atmospheric Pollution in Urban Centers

Air pollution is directly linked with the development of technology and science, the progress of which besides significant benefits to mankind it also has adverse effects on the environment and hence on human health. The problem has begun to take worrying proportions especially in large urban centers, where 60,000 deaths are reported each year in Europe’s towns and 3,000,000 worldwide, due to long-term air pollution exposure (exposure of the European Agency for the Environment http://www.eea.europa.eu/). In this paper we propose a novel and flexible hybrid machine learning system that combines Semi-Supervised Classification and Semi-Supervised Clustering, in order to realize prediction of air pollutants outliers and to study the conditions that favor their high concentration.

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.

Comparative analysis of exhaust emissions caused by chainsaws with soft computing and statistical approaches

This research compares the nitrogen monoxide and methane exhaust emissions produced by the engines of two conventional chainsaws (a professional and an amateur one) to those produced by a catalytic. For all the three types of chainsaws, measurements were taken under the following three different functional modes: (a) normal conditions with respect to infrequent acceleration, (b) normal conditions, (c) use of high-quality motor oil with a clean filter. The experiment was extended much further by considering measurements of nitrogen monoxide and methane concentrations for all the three types of chainsaws, in respect to four additional operation forms. More specifically, the emissions were measured (a) under normal conditions, (b) under the application of frequent acceleration, (c) with the use of poor-quality motor oil and (d) with chainsaws using impure filters. The experiments and data collection were performed in the forest under “real conditions.” Measurements conducted under real conditions were named “control” measurements and were used for future comparisons. The authors used a portable analyzer (Dräger X-am 5000 a Dräger Sensor XXSNO and a CatEx 125 PRCH4) for the measurement of exhaust emissions. The said analyzer can measure the concentrations of exhaust gas components online, while the engine is running under field conditions. In this paper, we have been employed fuzzy sets and fuzzy Chi-square tests in order to model air pollution produced by each type of chainsaw under each type of operation condition. The overall conclusion is that the catalytic chainsaw is the most environmentally friendly.

Hybrid intelligent modeling of wild fires risk

Forest fires are one of the most serious natural disasters for the countries of the Mediterranean basin and especially for Greece. Studying the climate change effect on the maximization of the problem is a constant objective of the scientific community. This research initially proposes an innovative hybrid version of the statistical Chi-Square test that employs Soft Computing methods. More specifically it introduces the Fuzzy Chi Square Independence test that fuzzifies p values using proper Risk Linguistics, based on Fuzzy Membership functions. In the second stage, it proposes a new Hybrid approach that models the evolution of burned areas in Greece. First it analyzes the parameters and determines the way they affect the problem, by constructing Fuzzy cognitive maps. The system projects into the future and forecasts the evolution of the problem through the years till 2100, based on the variance of average monthly temperature and average rain height (due to climate change) for the months May–October based on various climate models. Historical data for the period 1984–2004 were used to test the system for the areas of Chania and Ilia.

A deep spiking machine-hearing system for the case of invasive fish species

Prolonged and sustained warming of the sea, acidification of surface water and rising of sea levels, creates significant habitat losses, resulting in the proliferation and spread of invasive species which immigrate to foreign regions seeking colder climate conditions. This is happening either because their natural habitat does not satisfy the temperature range in which they can survive, or because they are just following their food. This has negative consequences not only for the environment and biodiversity but for the socioeconomic status of the areas and for the human health. This research aims in the development of an advanced Machine Hearing system towards the automated recognition of invasive fish species based on their sounds. The proposed system uses the Spiking Convolutional Neural Network algorithm which cooperates with Geo Location Based Services. It is capable to correctly classify the typical local fish inhabitants from the invasive ones.

Hybrid Soft Computing Analytics of Cardiorespiratory Morbidity and Mortality Risk Due to Air Pollution

During the last decades, climate change has been contributing significantly to the increase of Ozone and Particulate Matter in major urban centers. This might result in additional enhancement of serious seasonal respiratory and cardiovascular diseases incidents. This research effort introduces an innovative hybrid approach that fuzzifies the involved features. The final target is the development of a Mamdani fuzzy inference system with weighted fuzzy rules. The system’s output comprises of the partial meteorological and air pollution risk indices per season. Fuzzy conjunction T-Norms have been employed to estimate the unified risk index. Moreover, the effect of one to seven days delay regarding high values of the above indices to the morbidity and mortality indicators in the prefecture of Thessaloniki has been studied. Hybrid Fuzzy Chi Square Test has been performed to identify the degree of dependences between the unified air pollution-meteorological risk indices and serious health even mortality cardiorespiratory problems.

A Dynamic Ensemble Learning Framework for Data Stream Analysis and Real-Time Threat Detection

Security incident tracking systems receive a continuous, unlimited inflow of observations, where in the typical case the most recent ones are the most important. These data flows and characterized by high volatility. Their characteristics can change drastically over time in an unpredictable way, differentiating their typical normal behavior. In most cases it is not possible to store all of the historical samples, since their volume is unlimited. This fact requires the extraction of real-time knowledge over a subset of the flow, which contains a small but recent percentage of all observations. This creates serious objections to the accuracy and reliability of the employed classifiers. The research described herein, uses a Dynamic Ensemble Learning (DYENL) approach for Data Stream Analysis (DELDaStrA) which is employed in RealTime Threat Detection systems. More specifically, it proposes a DYENL model that uses the “Kappa” architecture to perform analysis of data flows. The DELDaStrA is based on the hybrid combination of k Nearest Neighbor (kNN) Classifiers, with Adaptive Random Forest (ARF) and Primal Estimated SubGradient Solver for Support Vector Machines (SVM) (SPegasos). In fact, it performs a dynamic extraction of the weighted average of the three results, to maximize the classification accuracy.

Temporal Modeling of Invasive Species’ Migration in Greece from Neighboring Countries Using Fuzzy Cognitive Maps

A serious side effect of climate change is the spread of invasive species (INSP), which constitute a serious and rapidly worsening threat to ecology, to the preservation of natural biodiversity, to the protection of flora and fauna and it can even threaten human population health. These species do not seem to have particular morphological differences, despite the intense variations in their biological characteristics. This often makes their identification very difficult. The need to protect the environment and to safeguard public health requires the development of sophisticated methods for early and valid identification which can lead to timely rational management measures. The aim of this research is the development of an advanced Computational Intelligence (COIN) system, capable to effectively analyze the conditions that influence and favors spreading of invasive species, due to the problem of climate change. Fuzzy Cognitive Maps (FCM) have been used to determine the specific temporal period (in years) in which the rapidly changing average temperature and precipitation in Greece, will become identical to the respective values of the neighboring countries for the period 1996–2015. This climatic evolution will cause spread of INSP met in these Mediterranean countries, to Greece. Separate analysis has been done for several cases of invasive species. The whole analysis is based on climate change models up to 2100.