Prediction of Daily Temperature Based on the Robust Machine Learning Algorithms
In one sentence. LSTM and deep LSTM models forecast daily air temperature in Tabriz, Iran, from 1,097 observations, with the deep version reaching RMSE 0.08 °C.
Abstract
Temperature climate is an essential component of weather forecasting and is vital in predicting future weather patterns. Accurate temperature predictions can assist individuals and organizations in preparing for potential weather-related events such as heat waves or cold snaps. However, achieving precise temperature predictions necessitates thoroughly comprehending the underlying factors influencing climate patterns. The study utilized two models, LSTM and DLSTM, to forecast daily air temperature using 1097 data points gathered from central and southern regions of Tabriz city of Iran in Asia from 2017 to 2019. The results indicated that the proposed model had a high accuracy rate for predicting daily air temperature for test data, with RMSEDLSTM = 0.08 °C and R-SquareDLSTM = 0.99. The DLSTM algorithm is known for its high speed, accuracy, time series prediction, noise reduction capabilities for data, the large volume of data processing, and improved performance of predicted data. In summary, while both LSTM and DLSTM are used for predicting data points, DLSTM is a more advanced version that includes multiple layers of memory cells and is better suited for handling complex sequences of events.
Labels
- Topic
- Local & station forecasting
- Type
- New model or method
- Method
- RNN / LSTM / ConvLSTM
- Time scale
- 6 h–3 days
- Domain
- Local / station
- Data
- Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.3390/su15129289 OpenAlex:W4380083550
- Added
- 2026-10-22
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