Chapter 9
Predict Future Climate Change Using
Artificial Neural Networks
Hamit Altıparmak, Ramiz Salama, Hüseyin Gökçeku¸ s,
and Dilber Uzun Ozsahin
Abstract 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,
H. Altıparmak · R. Salama (B)
Department of Computer Engineering, Near East University, Nicosia, Turkish Republic of
Northern Cyprus, Turkey
e-mail: ramiz.salama@neu.edu.tr
H. Altıparmak
e-mail: hamit.altiparmak@neu.edu.tr
D. Uzun Ozsahin
DESAM Institute, Near East University, Nicosia, Turkish Republic of Northern Cyprus, Turkey
e-mail: dilber.uzunozsahin@neu.edu.tr
H. Gökçeku¸ s
Faculty of Civil and Environmental Engineering, Near East University, Nicosia, Turkish Republic
of Northern Cyprus, Turkey
D. Uzun Ozsahin
Department of Biomedical Engineering, Near East University, Nicosia, Turkish Republic of
Northern Cyprus, Turkey
Medical Diagnostic Imaging Department, College of Health Science, University of Sharjah,
Sharjah, United Arab Emirates
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
D. Uzun Ozsahin et al. (eds.), Application of Multi-Criteria Decision Analysis in
Environmental and Civil Engineering, Professional Practice in Earth Sciences,
https://doi.org/10.1007/978-3-030-64765-0_9
57
Predict Future Climate Change Using
Artificial Neural Networks
Hamit Altıparmak, Ramiz Salama, Hüseyin Gökçeku¸ s,
and Dilber Uzun Ozsahin
Abstract 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,
H. Altıparmak · R. Salama (B)
Department of Computer Engineering, Near East University, Nicosia, Turkish Republic of
Northern Cyprus, Turkey
e-mail: ramiz.salama@neu.edu.tr
H. Altıparmak
e-mail: hamit.altiparmak@neu.edu.tr
D. Uzun Ozsahin
DESAM Institute, Near East University, Nicosia, Turkish Republic of Northern Cyprus, Turkey
e-mail: dilber.uzunozsahin@neu.edu.tr
H. Gökçeku¸ s
Faculty of Civil and Environmental Engineering, Near East University, Nicosia, Turkish Republic
of Northern Cyprus, Turkey
D. Uzun Ozsahin
Department of Biomedical Engineering, Near East University, Nicosia, Turkish Republic of
Northern Cyprus, Turkey
Medical Diagnostic Imaging Department, College of Health Science, University of Sharjah,
Sharjah, United Arab Emirates
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
D. Uzun Ozsahin et al. (eds.), Application of Multi-Criteria Decision Analysis in
Environmental and Civil Engineering, Professional Practice in Earth Sciences,
https://doi.org/10.1007/978-3-030-64765-0_9
57