an interval type-2 fuzzy lstm algorithm for modeling environmental time-series prediction

The statistical attributes of the non-stationary problems such as air quality and other natural phenomena frequently changed. type-2 fuzzy logic is a robust and capable model to cope with high-order uncertainties associated with non-stationary time-dependent features. this research’s main objective is to present a novel fuzzy deep lstm (it2flstm) model to predict air quality for tehran and beijing in a short and long time series scale. the proposed model has been evaluated on a real dataset that contains the one-decade information about outdoor pollutants from april 2011 to november 2020 in tehran and beijing. the it2flstm model was evaluated using a roc curve analysis and validated using 10-fold cross-validation. the results confirm the it2flstm model’s superiority with an average area under the roc curve (auc) of 97 % and a 95% confidence interval of [95-98] %. the proposed it2flstm model promises to predict complex problems to make strategic prevention decisions to save more lives.