| Title |
Improving Monthly Precipitation Forecasting with GRU-LSTM Model in Turkey |
| Authors |
Yildirim, Figen ; Streimikiene, Dalia ; Altuğ Biçer, Ali ; Rostamzadeh, Reza ; Ghorbani, Shahryar |
| DOI |
10.46544/AMS.v30i1.09 |
| Full Text |
|
| Is Part of |
Acta Montanistica Slovaca.. Kosice : Technical University of Kosice - Faculty of Mining, Ecology, Process Control and Geotechnology. 2025, vol. 30, iss. 1, p. 126-132.. ISSN 1335-1788. eISSN 1335-1788 |
| Keywords [eng] |
GRU ; Hybrid Model ; LSTM ; Precipitation ; Prediction |
| Abstract [eng] |
Protecting lives and property in Turkey requires reliable precipitation forecasts due to the country's susceptibility to extreme weather events like heavy precipitation, flash floods, and typhoons. On the other hand, Predictions enable officials to implement preventive actions and alert the community. Hence, researching forecasting precipitation in Turkey is essential. This research uses two combined deep learning hybrid models of Convolutional Neural Network, Long Short-Term Memory (CNN-LSTM), and Gated Recurrent Units-Long Short-Term Memory (GRU-LSTM) to predict the monthly precipitation of Istanbul and Konya between 2000 to 2023. From the research results, it can be concluded that the GRU-LSTM model is generally better. |
| Published |
Kosice : Technical University of Kosice - Faculty of Mining, Ecology, Process Control and Geotechnology |
| Type |
Journal article |
| Language |
English |
| Publication date |
2025 |
