Fuzzy sets and applications
A meteorological fuzzy expert system incorporating subjective user input
Knowledge and Information Systems
Mining Dynamic Interdimension Association Rules for Local-Scale Weather Prediction
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Workshops and Fast Abstracts - Volume 02
An algorithmic approach for fuzzy inference
IEEE Transactions on Fuzzy Systems
In-vehicle network level fault diagnostics using fuzzy inference systems
Applied Soft Computing
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Discovering and understanding the dynamic phenomena of weather to accurately predict different weather events has been an integral component of scientific investigations worldwide. The weather data, being inherently fuzzy in nature, requires highly complex processing based on human observations, satellite photography, or radar followed by computer simulations. This is further combined with an understanding of the principles of global and local weather dynamics. This paper attempts to solve weather event prediction for Lahore by implementing a fuzzy rule based system. The difficult problem of weather event prediction has been dealt in this paper through two separate experimental settings. In the first experimental setting a smaller dataset consisting of 365 instances with 4 inputs and 8 weather events has been used to develop a fuzzy inference system. In the second experimental setting the developed fuzzy system has been enhanced for a larger dataset consisting of over 2500 data points, having 17 inputs, and 10 weather events. For the later experiments the results of the fuzzy system have been compared with two other models i.e., decision tree (DT) based model and partial least square based regression (PLSR) model. It has been observed in the present study that the performance of the fuzzy system is sensitive to bootstrapping sampling technique that has been used for generating training and test samples for developing the fuzzy, DT and PLSR models. Further the models under consideration have been less sensitive to principal component analysis based dimensionality reduction method.