@ARTICLE{Fathiyana_Rana_Z._Application_2025,
 author={Fathiyana, Rana Z. and Haryadi, Deny and Ananda, Naufal and Hidayat, Dinda J. and Wicaksana, Haryas Subyantara and Azahra, Rahma Wafii and Saputra, Hakim Giraldi},
 number={No 65},
 pages={113-121},
 journal={Journal of Water and Land Development},
 howpublished={online},
 year={2025},
 publisher={Polish Academy of Sciences; Institute of Technology and Life Sciences - National Research Institute},
 abstract={Based on data from the National Disaster Management Agency, South Sumatra is one of the provinces with a reasonably large drought-affected area, totalling 8,853,691.009 ha. Drought is a hydrometeorological disaster, characterised by anomalous rainfall below normal levels. Reduced rainfall can lead to decreased soil moisture, reduced river flows, and a general scarcity of water, which limits availability of water both on the surface and in the soil. To anticipate and mitigate the impacts of drought, an accurate forecasting system is essential for effective disaster management and mitigation. This research focuses on forecasting drought using the standardised precipitation index (SPI) based on Long Short-Term Memory (LSTM) and Multilayer Perceptron (MLP) algorithms. It compares LSTM and MLP algorithms by integrating rainfall data from the FY-4A satellite and observational rain gauges, which are processed to generate SPI values. These data are employed to train and test MLP and LSTM models in predicting future drought conditions. The results indicate that drought can be effectively predicted using both MLP and LSTM. However, the MLP outperforms the LSTM, as reflected by a higher Nash–Sutcliffe efficiency (NSE) value, a lower error rate, and a predicted date trend that more closely aligns with actual observations.},
 type={Article},
 title={Application of neural networks for drought forecasting based on the standardised precipitation index},
 URL={http://ochroma.man.poznan.pl/Content/135280/2025-02-JWLD-12.pdf},
 doi={10.24425/jwld.2025.154255},
 keywords={drought, forecasting, FY-4A satellite, long short-term memory (LSTM), multilayer perceptron (MLP), rainfall},
}