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    Analysis of 70 years of change in benthic invertebrate biodiversity in the Prince's Islands region, Istanbul
    (Elsevier, 2021) Huseyinoglu, Mehmet Fatih; Tari, Gokhan; Gunay, M. Erdem
    Capital of several empires through centuries, Istanbul has been a populated city in history. However, the population has increased by a factor of 13.3 over the last seven decades, which in turn has made this megacity a hotspot of domestic and industrial pollution. Marine biodiversity around Istanbul suffered from the increased pressures due to overfishing, extensive recreational usage, extreme habitat destruction and introduction of alien species via heavy maritime traffic. 70 years before, Muzaffer Demir published a colossal marine biodiversity study in 1952. We used the organization of this publication, and particularly employed it as a methodology, combined with Underwater Visual Census for a later study between 1997-1999 which involved more than 100 scuba dives. Our study investigated the abundance status of benthic species living mainly on rocky reefs as deep as 50 meters. The investigated species represented four phyla: Cnidaria, Echinodermata, Mollusca and Porifera. Approximately 40% of cnidarians, 40% of echinoderms, 65% of molluscs, and 80% of sponges recorded in the 1950s, were not recorded during our study, particularly due to methodological differences and population collapses or local extinctions. This paper aims to analyze and compare the temporal aspects of marine biodiversity in the Prince's Islands and Bosphorus region using several time domains; historical records, Demir's compendium in 1952, our field study in the closing of the previous millennium, combined efforts of the Turkish Journal of Zoology in 2014 of Turkey biodiversity checklists, and the most recent literature after that. (C) 2021 Elsevier B.V. All rights reserved.
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    Prediction of global temperature anomaly by machine learning based techniques
    (Springer London Ltd, 2023) Sen, Doruk; Huseyinoglu, Mehmet Fatih; Gunay, M. Erdem
    In this work, anthropogenic and natural factors were used to evaluate and forecast climate change on a global scale by using a variety of machine-learning techniques. First, significance analysis using the Shapley method was conducted to compare the importance of each variable. Accordingly, it was determined that the equivalent CO2 concentration in the atmosphere was the most important variable, which was proposed as further evidence of climate change due to fossil fuel-based energy generation. Following that, a variety of machine learning approaches were utilized to simulate and forecast the temperature anomaly until 2100 based on six distinct scenarios. Compared to the preindustrial period, the temperature anomaly for the best-case scenario was found to increase a mean value of 1.23 degrees C and 1.11 degrees C for the mid and end of the century respectively. On the other hand, the anomaly was estimated for the worst-case scenario to reach to a mean value of 2.52 degrees C and 4.97 degrees C for the same periods. It was then concluded that machine learning approaches can assist researchers in predicting climate change and developing policies for national governments, such as committing firmly to renewable energy regulations.