Time Series Prediction of Near-Surface Temperature Using an LSTM Neural Network
DOI:
https://doi.org/10.52562/injoes.2025.1482Keywords:
earth science, air temperature, multi-step forecasting, meteorological stations, machine learningAbstract
Predicting air and soil temperature is an active research topic in response to adverse changes in the global ecological environment, such as global warming and biodiversity loss. To improve prediction performance, various machine learning models and methods have been applied, some of which are costly to implement because they use spatiotemporal data such as global numerical weather prediction data. This study aims to establish a simple method for constructing a predictive model with relatively high accuracy. In this paper, we propose a method to construct a prediction model using a long short-term memory (LSTM) neural network with only time series data of measured temperatures at certain time intervals at stations in the region. The proposed method was applied to predict daily minimum and maximum temperatures for up to 10 days using only time series of near-surface temperatures observed at meteorological stations in Pyongyang and in coastal and inland areas of the Democratic People’s Republic of Korea during 2019–2023. The results show that the proposed model achieved relatively high and stable prediction performance using only observed data. The mean accuracy within ±2 °C (ACC) was 73.70% for 4-day and 87.97% for 10-day maximum temperature predictions.
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