Comparative Analysis of LSTM and GRU Neural Networks for Precipitation Forecasting Using Historical Meteorological Data
Iman Ahmadi
1
(
Department of Genetics and Plant Production Engineering, Institute of Agriculture, Water, Food and Nutraceuticals, Isf. C., Islamic Azad University, Isfahan, Iran
)
Keywords: Time series analysis, Confusion matrix, Deep learning , Precipitation prediction,
Abstract :
In this study, a comparison between LSTM and GRU algorithms for predicting rainfall occurrence using historical meteorological data is considered. Due to the unavailability of complete long-term domestic meteorological data, a database of meteorological data from several European cities was used. Five weather stations, each belonging to a different climate, were selected, and model accuracy was calculated based on the data obtained from these stations. This research employed two algorithms: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). To evaluate the performance of the developed models, confusion matrices, Receiver Operating Characteristic (ROC) curves, and Precision-Recall curves were used. The results from the analysis of the confusion matrices indicated that the performance of the two algorithms in predicting rainfall occurrence was approximately similar. Numerically, the average values of recall, specificity, precision, and accuracy for the LSTM algorithm were 0.67, 0.84, 0.75, and 0.79, respectively, while the corresponding values for the GRU algorithm were 0.65, 0.84, 0.74, and 0.78. Regarding the comparison of the two algorithms' performance, although the LSTM algorithm appeared to perform better due to its higher values in most evaluation metrics, their overall performance was statistically similar, given the absence of a significant difference between the corresponding evaluation metrics.
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