Climate analysis & attribution
Detection of Climate Crashes using Fuzzy Neural Networks
In one sentence. Takagi-Sugeno fuzzy neural network detects failures ('crashes') in climate model simulations, reaching 97.96% accuracy versus 94.4% for SVM.
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.
Labels
- Topic
- Climate analysis & attribution
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Domain
- Global
- Data
- CMIP / climate model output
- Status
- Research
- Identifiers
- DOI:10.14569/ijacsa.2018.090208 OpenAlex:W2788289703
- Added
- 2026-10-22
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