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    Akıllı tarım sistemlerine noktasal yağış tahmini entegrasyonu : Aydın örneği
    (Bakırçay Üniversitesi Lisansüstü Eğitim Enstitüsü, 2022) Ansay, Serkan; Köse, Bayram
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    Green Campus Certification and Sustainability Relations: Case of İzmir Bakircay University
    (Tuba-Turkish Acad Sciences, 2024) Kose, Bayram; Ansay, Serkan; Akderya, Tarkan; Tabakoglu, Guelbahar; Hiziroglu, Abduelkadir; Berktas, Mustafa
    Technological advances, population growth and diversifying consumption habits are putting increasing pressure on natural resources, while preserving the ecological balance and protecting the rights of all living beings is of critical importance. The fair and sustainable use of resources is indispensable to ensure environmental sustainability. In this context, the United Nations' initiatives to combat climate change and the European Green Deal guide sustainability efforts based on the harmony of human life and ecosystems. In this study, green campus certification processes carried out by universities with a free and scientific approach and their connection with sustainable development goals are discussed. In addition, the green certification efforts of universities in Turkey have been examined in detail and the reflections of these efforts across the country have been evaluated. The findings reveal that universities in Turkey have increasingly prioritized sustainability efforts in recent years and have expanded their position among green-certified institutions by increasing their level of awareness.
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    Precipitation Forecast with Artificial Neural Networks Method
    (İzmir Akademi Derneği, 2023) Ansay, Serkan; Köse, Bayram
    Events in the atmosphere from past to present – wind, precipitation, humidity, temperature – have almost always been the subject of research to create a forecast in regions. The rapid development of the technological field in terms of software and hardware brings methods and techniques to be used in research. One of them is Artificial Neural Networks. In this study, precipitation data were estimated using the Feed Forward Backpropagation method of Artificial Neural Networks method using past data of meteorological parameters, and they were compared with the data of multiple linear regression analysis. Based on these models, six different models were studied, and regression and performance evaluations were made. While the error average of multiple linear regression is 0.2413, this value is 0.076 in artificial neural networks, and the correlation average for both is 0.90. As a result of this study, the best model has a coefficient of determination of 0.95 and an error value of 0.18 in multiple linear regression, as well as a coefficient of certainty of 0.99 and an error value of 0.0438 in artificial neural networks; It has been understood that the 1st model, which has 6 data sets as the input layer, exhibits the best performance.