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Time-series prediction of hourly atmospheric pressure using ANFIS and LSTM approaches

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Abstract

Atmospheric pressure (AP), which is an indicator of weather events, plays an important role in climatology, agriculture, meteorology, atmospheric and environmental science, human and animal life, and Earth’s living ecosystem. In this regard, accurate AP forecasting plays a crucial role in today’s life as it provides critical information about future weather events. In this study, four different machine learning techniques such as long short-term memory (LSTM) neural network, adaptive neuro-fuzzy inference system (ANFIS) with fuzzy c-means, ANFIS with subtractive clustering, and ANFIS with grid partition (GP) were used for one-hour-ahead AP forecasting. To achieve this, the hourly AP data measured between 2012 and 2019 at the seven measurement stations (Adana, Ankara, Gumushane, Denizli, Kirklareli, Sanliurfa, and Van) in different climate regions of Turkey were obtained. The estimation accuracy was verified by four performance criteria: R, RMSE, MAPE, and MAE. As a result, the highest relative R-value of 0.9986 and the lowest error values of RMSE = 0.2905 hPa, MAPE = 0.0230%, and MAE = 0.2040 hPa for one-hour-ahead AP forecasting were obtained from the ANFIS-GP model.

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Acknowledgements

The authors wish to thank Turkish State Meteorological Service on account of supplying the data.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Ceyhan Engineering Faculty, Cukurova University, 01950, Adana, Turkey

    Mehmet Bilgili

  2. Department of Energy Systems Engineering, Faculty of Engineering and Natural Sciences, Yıldırım Beyazıt University, 06010, Ankara, Turkey

    Akın Ilhan

  3. Department of Mechanical Engineering, Osmaniye Korkut Ata University, Engineering Faculty, 80000, Osmaniye, Turkey

    Şaban Ünal

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Correspondence to Şaban Ünal.

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Bilgili, M., Ilhan, A. & Ünal, Ş. Time-series prediction of hourly atmospheric pressure using ANFIS and LSTM approaches. Neural Comput & Applic 34, 15633–15648 (2022). https://doi.org/10.1007/s00521-022-07275-5

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