Application of neural networks for drought forecasting based on the standardised precipitation index

• Autorzy: Fathiyana Rana Z. , Haryadi Deny , Ananda Naufal , Hidayat Dinda J. , Wicaksana Haryas Subyantara , Azahra Rahma Wafii , Saputra Hakim Giraldi

Identyfikatory

DOI

10.24425/jwld.2025.154255

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

drought; forecasting; FY-4A satellite; long short-term memory; LSTM; multilayer perceptron; MLP; rainfall

• Wydawca: Wydawnictwo ITP
• Czasopismo: Journal of Water and Land Development
• Rocznik: 2025
• Tom: no. 65
• Strony: 113--121
• Opis fizyczny: Bibliogr. 30 poz., mapa, rys., tab., wykr.

Twórcy

autor

Fathiyana Rana Z.

• Telkom University, Faculty School of Computing, The University Center of Excellence Intelligent Sensing-IoT, Jl. Halimun Raya No. 2, RT.15/RW.6, Guntur, Kecamatan Setiabudi, Kota Jakarta Selatan, Jakarta 12980, Indonesia

• https://orcid.org/0009-0007-4221-6765

autor

Haryadi Deny

• Telkom University, Faculty School of Computing, The University Center of Excellence Human Centric Engineering, Jl. Halimun Raya No. 2, RT.15/RW.6, Guntur, Kecamatan Setiabudi, Kota Jakarta Selatan, Jakarta 12980, Indonesia

• https://orcid.org/0000-0002-5038-2874

autor

Ananda Naufal

• Meteorological, Climatological, and Geophysical Agency, Jl. Angkasa I No. 2 Kemayoran, Jakarta Pusat 10610, Indonesia

• https://orcid.org/0009-0009-9965-4402

autor

Hidayat Dinda J.

• Meteorological, Climatological, and Geophysical Agency, Jl. Angkasa I No. 2 Kemayoran, Jakarta Pusat 10610, Indonesia

• https://orcid.org/0009-0004-3945-6248

autor

Wicaksana Haryas Subyantara

• Meteorological, Climatological, and Geophysical Agency, Jl. Angkasa I No. 2 Kemayoran, Jakarta Pusat 10610, Indonesia

• https://orcid.org/0000-0003-0603-428X

autor

Azahra Rahma Wafii

• Telkom University, Faculty School of Computing, Information Technology, Jl. Halimun Raya No. 2, RT.15/RW.6, Guntur, Kecamatan Setiabudi, Kota Jakarta Selatan, Jakarta 12980, Indonesia

• https://orcid.org/0009-0009-6320-5963

autor

Saputra Hakim Giraldi

• Telkom University, Faculty School of Computing, Information Technology, Jl. Halimun Raya No. 2, RT.15/RW.6, Guntur, Kecamatan Setiabudi, Kota Jakarta Selatan, Jakarta 12980, Indonesia

• https://orcid.org/0009-0002-6405-3083

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