Multistep-Ahead Forecasting of Drought Using Neural Prophet and Facebook Prophet Artificial Neural Network models: Beysehir Lake Basin Case

Akkurt Eroğluer T., Apaydın H.

MIPRO 2026 - 49th ICT and Electronics Convention, Rijeka, Hırvatistan, 25 - 29 Mayıs 2026, ss.1, (Tam Metin Bildiri)

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Basıldığı Şehir: Rijeka
  • Basıldığı Ülke: Hırvatistan
  • Sayfa Sayıları: ss.1
  • Ankara Üniversitesi Adresli: Evet

Özet

Droughts cause major agricultural, social and economic problems, and, in the worst cases, prolonged droughts can cause catastrophic consequences to the drought-affected region. In this study; in order to determine a potential drought that the Beysehir Lake Basin in Turkey may face in the future, the data obtained from the Seydisehir and Beysehir meteorological observation stations and the Sarkikaraagac, Soguksu-Yesildag, Sugla and Beysehir Lake stations were used. Using the data measured at these stations, data were forecasted for the next 25 years using neural network-based models: The Neural Prophet (NP) and Facebook Prophet (FP) models. Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI) and Streamflow Drought Index (SDI) drought indices were calculated with forecasted and historical data. According to the results obtained during the study, drought indices calculated with NP and FP model data showed a similarly increasing drought trend for 3, 6, 9 and 12 months periods at all stations. Frequency rates according to drought classes in drought indices calculated with measured data are very close and consistent with the frequency rates determined using FP and NP model data. The frequency rates of the SPI, RDI and SDI indexes according to drought classes were close to each other. The rate of extreme dry periods was found to be lower in flow-measured stations compared to precipitation stations.