Articles | Volume 26, issue 1
https://doi.org/10.5194/nhess-26-315-2026
https://doi.org/10.5194/nhess-26-315-2026
Research article
 | 
20 Jan 2026
Research article |  | 20 Jan 2026

Meteorological Drought Trend Analysis and Forecasting Using a Hybrid SG-CEEMDAN-ARIMA-LSTM Model Based on SPI from Rain Gauge Data

Siphamandla Sibiya, Shaun Ramroop, Sileshi Melesse, and Nkanyiso Mbatha

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Short summary
This study aimed to improve drought forecasting in uMkhanyakude, where water scarcity affects agriculture and livelihoods. It analyzed rainfall trends using modified Mann-Kendall and innovative trend analysis on the Standardized Precipitation Index. A hybrid model combining Savitzky–Golay, decomposition methods, and neural networks showed high accuracy, highlighting its value for early drought warning and water resource planning.
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