Optimizing the GRU-LSTM Hybrid Model for Air Temperature Prediction in Degraded Wetlands and Climate Change Implications
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| Publicado en: | International Journal of Advanced Computer Science and Applications vol. 16, no. 2 (2025) |
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| Publicado: |
Science and Information (SAI) Organization Limited
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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|---|---|---|---|
| 001 | 3180200410 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2158-107X | ||
| 022 | |a 2156-5570 | ||
| 024 | 7 | |a 10.14569/IJACSA.2025.0160272 |2 doi | |
| 035 | |a 3180200410 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a PDF | |
| 245 | 1 | |a Optimizing the GRU-LSTM Hybrid Model for Air Temperature Prediction in Degraded Wetlands and Climate Change Implications | |
| 260 | |b Science and Information (SAI) Organization Limited |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Accurate air temperature prediction is critical, particularly for micro air temperatures. The temperature of micro air changes quickly. Micro and macro air temperatures vary, particularly in degraded wetlands. By predicting air temperature, climate change in a degraded wetland environment can be predicted earlier. Furthermore, micro and macro air temperatures are drought index parameters. Knowing the drought index can help you avoid disasters like fires and floods. However, the right indicators for predicting micro or macro temperatures have yet to be found. LSTM excels at tasks requiring complex long-term memory, whereas GRU excels at tasks requiring rapid processing. We proposed a deep learning strategy based on the GRU-LSTM Hybrid model. Both of these deep learning models are excellent for predicting time series. The performance of this hybrid model is affected by changes in model indicators. The preprocessing stage, the number of input parameters, and the presence or absence of a Dropout Layer in the model architecture are among the most influential indicators of model performance. The best macro temperature prediction performance was obtained using 12 monthly average data to predict the next month’s temperature, yielding an RMSE of 0.056807, MAE of 0.046592, and R2 of 0.989371. This model also performed well in predicting daily micro temperature, with an RMSE of 0.227086, MAE of 0.190801, and R2 of 0.981802. | |
| 653 | |a Indicators | ||
| 653 | |a Climate change | ||
| 653 | |a Memory tasks | ||
| 653 | |a Deep learning | ||
| 653 | |a Parameters | ||
| 653 | |a Air temperature | ||
| 653 | |a Wetlands | ||
| 653 | |a Task complexity | ||
| 653 | |a Drought | ||
| 653 | |a Flood predictions | ||
| 653 | |a Accuracy | ||
| 653 | |a Datasets | ||
| 653 | |a Computer science | ||
| 653 | |a Time series | ||
| 653 | |a Efficiency | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Temperature | ||
| 653 | |a Sensors | ||
| 653 | |a Geophysics | ||
| 653 | |a Data collection | ||
| 653 | |a Climate science | ||
| 773 | 0 | |t International Journal of Advanced Computer Science and Applications |g vol. 16, no. 2 (2025) | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3180200410/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3180200410/fulltextPDF/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |