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

BİLGİLİ M., İLHAN A., ÜNAL Ş.

NEURAL COMPUTING & APPLICATIONS, vol.34, no.18, pp.15633-15648, 2022 (SCI-Expanded, Scopus) identifier identifier

  • Publication Type: Article / Article
  • Volume: 34 Issue: 18
  • Publication Date: 2022
  • Doi Number: 10.1007/s00521-022-07275-5
  • Journal Name: NEURAL COMPUTING & APPLICATIONS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, Index Islamicus, INSPEC, zbMATH
  • Page Numbers: pp.15633-15648
  • Keywords: Adaptive neuro-fuzzy inference system (ANFIS), Atmospheric pressure, Long short-term memory (LSTM), Machine learning approaches, One-hour-ahead forecasting, SHORT-TERM-MEMORY, MULTIRESOLUTION ANALYSIS, NEURAL-NETWORKS, FUZZY-LOGIC, MODEL, TEMPERATURE, REGRESSION
  • Çukurova University Affiliated: Yes

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.