(Department of Electrical Engineering, Politeknik Negeri Semarang, Indonesia)(2) Anindya Wirasatriya
(Department of Oceanography, Universitas Diponegoro, Indonesia)(3) Lutfan Lazuardi
(Faculty of Medicine, Universitas Gadjah Mada, Indonesia)(4) Adi Wibowo
(Department of Computer Science, Universitas Diponegoro, Indonesia)(5) I Ketut Agung Enriko
(Department of Electrical Engineering, Institut Teknologi Telkom Purwokerto, Indonesia)(6) Wei Hong Chin
(Graduate School of Systems Design, Tokyo Metropolitan University, Japan)(7) Naoyuki Kubota
(Graduate School of Systems Design, Tokyo Metropolitan University, Japan)*corresponding author
AbstractAccurate and reliable relative humidity forecasting is important when evaluating the impacts of climate change on humans and ecosystems. However, the complex interactions among geophysical parameters are challenging and may result in inaccurate weather forecasting. This study combines long short-term memory (LSTM) and extreme learning machines (ELM) to create a hybrid model-based forecasting technique to predict relative humidity to improve the accuracy of forecasts. Detailed experiments with univariate and multivariate problems were conducted, and the results show that LSTM-ELM and ELM-LSTM have the lowest MAE and RMSE results compared to stand-alone LSTM and ELM for the univariate problem. In addition, LSTM-ELM and ELM-LSTM result in lower computation time than stand-alone LSTM. The experiment results demonstrate that the proposed hybrid models outperform the comparative methods in relative humidity forecasting. We employed the recursive feature elimination (RFE) method and showed that dewpoint temperature, temperature, and wind speed are the factors that most affect relative humidity. A higher dewpoint temperature indicates more air moisture, equating to high relative humidity. Humidity levels also rise as the temperature rises.
KeywordsBig data analytics; Relative humidity; Time series forecasting; LSTM; ELM
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DOIhttps://doi.org/10.26555/ijain.v9i3.905 |
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