10.22034/ap.2022.1963124.1133

An Interval Type-2 Fuzzy LSTM Algorithm for Modeling Environmental Time-Series Prediction

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  • Aref Safari*1,
  • Rahil Hosseini2,
  1. Department of Computer Engineering, Shahr-e-Qods Branch, Islamic Azad University, Shahr-e Qods, Tehran, Iran
  2. Department of Computer Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran
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Published in Issue 2023-11-17

How to Cite

Safari, A., & Hosseini, R. (2023). An Interval Type-2 Fuzzy LSTM Algorithm for Modeling Environmental Time-Series Prediction. Anthropogenic Pollution, 6(2). https://doi.org/10.22034/ap.2022.1963124.1133
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Abstract

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.

Keywords

  • Air Pollution Prediction,
  • Deep learning,
  • Enviroment,
  • LSTM network,
  • Type-2 Fuzzy Logic
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