Detection of Climate Crashes using Fuzzy Neural Networks

Rahib H Abiyev ,Mohammed Azad Omar ,Boran Şekeroğlu

doi:10.14569/ijacsa.2018.090208

Rahib H Abiyev , Mohammed Azad Omar + Show 1 more

Open Access

https://doi.org/10.14569/ijacsa.2018.090208

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Abstract

In this paper the detection of the climate crashes or failure that are associated with the use of climate models based on parameters induced from the climate simulation is considered. Detection and analysis of the crashes allows one to understand and improve the climate models. Fuzzy neural networks (FNN) based on Takagi-Sugeno-Kang (TSK) type fuzzy rule is presented to determine chances of failure of the climate models. For this purpose, the parameters characterising the climate crashes in the simulation are used. For comparative analysis, Support Vector Machine (SVM) is applied for simulation of the same problem. As a result of the comparison, the accuracy rates of 94.4% and 97.96% were obtained for SVM and FNN model correspondingly. The FNN model was discovered to be having better performance in modelling climate crashes.

Highlights

Climate models play important role in the prediction of future climate changes
This study aims to examine and predict the failure of parameterinduced simulation crashes in climate models
Fuzzy neural networks (FNN) with the 24 neurons was established to be the best model in terms of accuracy and this follows a recorded accuracy rate of 97.96% while model FNN with 8 neurons had the lowest accuracy rate of 93.15%

Summary

INTRODUCTION

Climate models play important role in the prediction of future climate changes. Tough climate models are offering huge benefits to the pupils. [15] mentioned that the use of numerous algorithms of anthropogenic, geologic, chemical and biological nature that are used in the simulation of climate-related issues and greenhouse gases, ozone, aerosols, Sulphur, nitrogen, and www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol 9, No 2, 2018 cycles of carbon is the main reason of climate model failure. Such algorithms are used in a set of circumstances and time and have solid, liquid and gaseous elements [16], [17] showed that crashes occur at a high rate.

FUZZY NEURAL NETWORKS FOR DETECTION CLIMATE CRASHES

SIMULATION

Findings

CONCLUSIONS