Abstract
Climate change is the most large scale problem and the well-known vexed question in the modern era. The increase of global temperature on Earth’s surfaces due to the higher emission of greenhouse gasses is an indication of climate change. This paper presents a Fuzzy Inference System (FIS) to predict the relationship between effects and causes of climate change. The Fuzzy Inference System opens a baseline for understanding the causes of climate change, with expert knowledge, which reflects the relationship between multiple environmental factors. Based on a Multiple-Input-Multiple-Output Mamdani Fuzzy Inference System the repercussions of the climate change can be examined by acquiring an anticipated result with respect to a variety of climate scenarios. Here, CO2 (Carbon Dioxide), Global Temperature Changes, Snow Cover, Percentage of Forestlands, and Net Radiation are considered as inputs; while the considered outputs are Ozone Layer Changes, Arctic Ice Sheet Level, and Sea Level. Three membership functions of the “generalized bell function” type have been considered for each input and output. This paper shows that multiple inputs and multiple outputs in a Fuzzy Inference System can lead the way for understanding climate change causes and effects. Furthermore, these results are in agreement with results from other authors approaches; however, the use of a FIS allows to include elements of uncertainty and vagueness in the input variables considered.
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Koca, G., Bhuiyan, M.T.A., Mayorga, R.V. (2020). Causes and Effects of Climate Change via a Fuzzy Inference System. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A., Sari, I. (eds) Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making. INFUS 2019. Advances in Intelligent Systems and Computing, vol 1029. Springer, Cham. https://doi.org/10.1007/978-3-030-23756-1_160
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DOI: https://doi.org/10.1007/978-3-030-23756-1_160
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