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- W4384209488 abstract "In this paper, the application of LSTM and XGBoost models for rainfall prediction of a local area is investigated. The raw weather data, provided by the Central Weather Bureau of Taiwan government, for the Tamsui district of New Taipei City from 2001 to 2020 is utilized and cleaned, which is organized into three datasets: 1) the dataset of weather data for the same month only, 2) the dataset of weather data for the same month associated the two months before and after, respectively, and 3) the dataset of weather data for each month associated the whole year. The execution details show that LSTM and XGBoost have their own applicable months, respectively, and 1) for the rainfall forecast of each month, it is not necessary to use all the weather data of 12 months. In this regard, computing resources can be reduced, while the prediction results are even more accurate; 2) In 2020, the local rainfall pattern of nearly six different months do exhibit a time shift phenomenon; 3) There is no magic time sequence that is suited for all months." @default.
- W4384209488 created "2023-07-14" @default.
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- W4384209488 date "2023-02-24" @default.
- W4384209488 modified "2023-09-25" @default.
- W4384209488 title "Comparison of Two Local Rainfall Forecasting Models Applied for Smart Irrigation Systems" @default.
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- W4384209488 doi "https://doi.org/10.1145/3591569.3591618" @default.
- W4384209488 hasPublicationYear "2023" @default.
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