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.

[1]  Konstantinos Demertzis,et al.  Commentary: Aedes albopictus and Aedes japonicas—two invasive mosquito species with different temperature niches in Europe , 2017, Front. Environ. Sci..

[2]  Susan P. Worner,et al.  Using a self‐organizing map to predict invasive species: sensitivity to data errors and a comparison with expert opinion , 2010 .

[3]  Konstantinos Demertzis,et al.  Artificial Intelligence Applications and Innovations: 18th IFIP WG 12.5 International Conference, AIAI 2022, Hersonissos, Crete, Greece, June 17–20, 2022, Proceedings, Part II , 2022, IFIP Advances in Information and Communication Technology.

[4]  David S. L. Ramsey,et al.  Predicting the unexpected: using a qualitative model of a New Zealand dryland ecosystem to anticipate pest management outcomes. , 2009 .

[5]  Konstantinos Demertzis,et al.  A Spiking One-Class Anomaly Detection Framework for Cyber-Security on Industrial Control Systems , 2017, EANN.

[6]  Konstantinos Demertzis,et al.  Blockchain-based Consents Management for Personal Data Processing in the IoT Ecosystem , 2018, ICETE.

[7]  Konstantinos Demertzis,et al.  HISYCOL a hybrid computational intelligence system for combined machine learning: the case of air pollution modeling in Athens , 2015, Neural Computing and Applications.

[8]  Chunjing Wang,et al.  Expansion potential of invasive tree plants in ecoregions under climate change scenarios: an assessment of 54 species at a global scale , 2017 .

[9]  A. DiTommaso,et al.  Predicting the potential distribution of Lantana camara L. under RCP scenarios using ISI-MIP models , 2015, Climatic Change.

[10]  Jose L. Salmeron,et al.  Dynamic optimization of fuzzy cognitive maps for time series forecasting , 2016, Knowl. Based Syst..

[11]  Konstantinos Demertzis,et al.  Fuzzy Cognitive Maps for Long-Term Prognosis of the Evolution of Atmospheric Pollution, Based on Climate Change Scenarios: The Case of Athens , 2016, ICCCI.

[12]  S. Lowe,et al.  100 of the world's worst invasive alien species. A selection from the global invasive species database , 2004 .

[13]  A. G. Asuero,et al.  The Correlation Coefficient: An Overview , 2006 .

[14]  Konstantinos Demertzis,et al.  A Hybrid Network Anomaly and Intrusion Detection Approach Based on Evolving Spiking Neural Network Classification , 2013, e-Democracy.

[15]  Konstantinos Demertzis,et al.  Hybrid Soft Computing Analytics of Cardiorespiratory Morbidity and Mortality Risk Due to Air Pollution , 2017, ISCRAM-med.

[16]  Didier Devaurs,et al.  An Individual-Based Evolving Predator-Prey Ecosystem Simulation Using a Fuzzy Cognitive Map as the Behavior Model , 2009, Artificial Life.

[17]  Mohammed Raju Ahmed,et al.  Invasion risk of the yellow crazy ant (Anoplolepis gracilipes) under the Representative Concentration Pathways 8.5 climate change scenario in South Korea , 2017 .

[18]  Konstantinos Demertzis,et al.  Evolving Computational Intelligence System for Malware Detection , 2014, CAiSE Workshops.

[19]  Konstantinos Demertzis,et al.  Machine learning use in predicting interior spruce wood density utilizing progeny test information , 2017, Neural Computing and Applications.

[20]  Konstantinos Demertzis,et al.  Comparative analysis of exhaust emissions caused by chainsaws with soft computing and statistical approaches , 2018, International Journal of Environmental Science and Technology.

[21]  Konstantinos Demertzis,et al.  Classifying with fuzzy chi-square test: The case of invasive species , 2018 .

[22]  Konstantinos Demertzis,et al.  Extreme deep learning in biosecurity: the case of machine hearing for marine species identification , 2018, J. Inf. Telecommun..

[23]  M. Bank,et al.  Effects of climate change on the future distributions of the top five freshwater invasive plants in South Africa , 2016 .

[24]  Sunghoon Jung,et al.  Insect distribution in response to climate change based on a model: Review of function and use of CLIMEX , 2016 .

[25]  Richard C. Willson,et al.  ACRIM total solar irradiance satellite composite validation versus TSI proxy models , 2014, 1403.7194.

[26]  Jose L. Salmeron,et al.  Fuzzy Cognitive Map-based selection of TRIZ (Theory of Inventive Problem Solving) trends for eco-innovation of ceramic industry products , 2015 .

[27]  P. Pagano,et al.  Forecasting the ongoing invasion of Lagocephalus sceleratus in the Mediterranean Sea , 2018 .

[28]  Robert I. Colautti,et al.  A neutral terminology to define ‘invasive’ species , 2004 .

[29]  Patrick Van Damme,et al.  Use of support vector machines (SVMs) to predict distribution of an invasive water fern Azolla filiculoides (Lam.) in Anzali wetland, southern Caspian Sea, Iran , 2012 .

[30]  Konstantinos Demertzis,et al.  Fast and low cost prediction of extreme air pollution values with hybrid unsupervised learning , 2016, Integr. Comput. Aided Eng..

[31]  J. Tenedório,et al.  Predicting the impact of climate change on the invasive decapods of the Iberian inland waters: an assessment of reliability , 2012, Biological Invasions.

[32]  Konstantinos Demertzis,et al.  Detecting invasive species with a bio-inspired semi-supervised neurocomputing approach: the case of Lagocephalus sceleratus , 2017, Neural Computing and Applications.

[33]  Susan P. Worner,et al.  Prediction of Global Distribution of Insect Pest Species in Relation to Climate by Using an Ecological Informatics Method , 2006 .

[34]  Konstantinos Demertzis,et al.  An innovative soft computing system for smart energy grids cybersecurity , 2018 .

[35]  Konstantinos Demertzis,et al.  Hybrid intelligent modeling of wild fires risk , 2018, Evol. Syst..

[36]  Konstantinos Demertzis,et al.  FuSSFFra, a fuzzy semi-supervised forecasting framework: the case of the air pollution in Athens , 2018, Neural Computing and Applications.