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Predict Future Climate Change Using Artificial Neural Networks
In Artificial Neural Networks (ANN) with feedback, the output of at least one cell is given as input to itself or to other cells, and feedback is usually done via a delay element. Feed-back can be between cells in a layer or between cells between layers. With this structure, the feedback ANN shows dynamic nonlinear behavior. Therefore, feedback ANN structures can be obtained in different structures and behaviors depending on the type of feedback. There have been many studies documenting the increase in the average global temperature in the last century. The consequences of a continuous rise in global temperature will be significant. The rising sea levels and increasing frequency of extreme weather events will affect billions of people. Neural Net-work Performance: We used a data table comprising 8 rows and 303 columns as input. We used a feedback neural network consisting of 1 hidden layer and 10 neurons. Results: Training 90.172%, Validation 84.859%, Test 81.697%, All 87.945%. The effects of climate change have already been observed and will become more apparent in the future. With the contribution of all countries, the negative impacts of climate change need to be identified. In this way, strategies to combat potential problems caused by future climate change can be established
Predict Future Climate Change Using Artificial Neural Networks
In Artificial Neural Networks (ANN) with feedback, the output of at least one cell is given as input to itself or to other cells, and feedback is usually done via a delay element. Feed-back can be between cells in a layer or between cells between layers. With this structure, the feedback ANN shows dynamic nonlinear behavior. Therefore, feedback ANN structures can be obtained in different structures and behaviors depending on the type of feedback. There have been many studies documenting the increase in the average global temperature in the last century. The consequences of a continuous rise in global temperature will be significant. The rising sea levels and increasing frequency of extreme weather events will affect billions of people. Neural Net-work Performance: We used a data table comprising 8 rows and 303 columns as input. We used a feedback neural network consisting of 1 hidden layer and 10 neurons. Results: Training 90.172%, Validation 84.859%, Test 81.697%, All 87.945%. The effects of climate change have already been observed and will become more apparent in the future. With the contribution of all countries, the negative impacts of climate change need to be identified. In this way, strategies to combat potential problems caused by future climate change can be established
Predict Future Climate Change Using Artificial Neural Networks
Prof. Pract. Eart. Scie.
Uzun Ozsahin, Dilber (editor) / Gökçekuş, Hüseyin (editor) / Uzun, Berna (editor) / LaMoreaux, James (editor) / Altıparmak, Hamit (author) / Salama, Ramiz (author) / Gökçekuş, Hüseyin (author) / Uzun Ozsahin, Dilber (author)
2021-03-01
7 pages
Article/Chapter (Book)
Electronic Resource
English
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