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• W4377014978 abstract "This research predicted the meteorological drought of Sakarya province in northwest Türkiye using long short-term memory (LSTM). This deep learning algorithm has gained popularity in prediction studies. The standardized precipitation index (SPI), which can only be derived using precipitation data, was utilized for 1, 3, and 6-month time scales. SPI-1, SPI-3, and SPI-6-month time scales drought data calculated from the monthly precipitation data of the Sakarya Meteorology Station between 1960 and 2020 were taken as input data in the LSTM model. SPI drought data were used between 1960 and 2005 as training data and 2006–2020 as test data. Drought at t+1 output time was predicted using SPI values at t, t-1, t-2, and t-3 lag times as input variables. In addition, the results were compared with the empirical mode decomposition (EMD)-extreme learning machine (ELM) hybrid model to understand the capabilities of the standalone LSTM prediction model. The LSTM model yielded the best results (MAE = 0.11, NSE = 0.97, R2 = 0.97) for the SPI-1-month time scale and the best results (MAE = 0.18, NSE = 0.92, R2 = 0.94) for 3-month time scale. The EMD-ELM hybrid model yielded the best results (MAE = 0.22, NSE = 0.95, R2 = 0.96) for the SPI-6-month time scale. Due to the high performance of this study's proposed standalone LSTM model, it was concluded that drought time series do not need to be subjected to pre-processing techniques." @default.
• W4377014978 created "2023-05-19" @default.
• W4377014978 creator A5023539450 @default.
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• W4377014978 date "2023-10-01" @default.
• W4377014978 modified "2023-10-18" @default.
• W4377014978 title "Prediction of the standardized precipitation index based on the long short-term memory and empirical mode decomposition-extreme learning machine models: The Case of Sakarya, Türkiye" @default.
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• W4377014978 doi "https://doi.org/10.1016/j.pce.2023.103418" @default.
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