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Refinado por: assunto: Rainfall Forecasting remover
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Coordinate-Transformed Dynamic Mode Decomposition for Short-Term Rainfall Forecasting
Coordinate-Transformed Dynamic Mode Decomposition for Short-Term Rainfall Forecasting
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Coordinate-Transformed Dynamic Mode Decomposition for Short-Term Rainfall Forecasting

Zheng, Shitao ; Miyamoto, Takashi ; Shimizu, Shingo ; Kato, Ryohei ; Iwanami, Koyuru

IEEE transactions on geoscience and remote sensing, 2024, Vol.62, p.1-17 [Periódico revisado por pares]

New York: IEEE

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2
A hybrid approach of artificial neural network and multiple regression to forecast typhoon rainfall and groundwater-level change
A hybrid approach of artificial neural network and multiple regression to forecast typhoon rainfall and groundwater-level change
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A hybrid approach of artificial neural network and multiple regression to forecast typhoon rainfall and groundwater-level change

Hsieh, Ping-Cheng ; Tong, Wei-An ; Wang, Yung-Chieh

Hydrological sciences journal, 2019-10, Vol.64 (14), p.1793-1802 [Periódico revisado por pares]

Abingdon: Taylor & Francis

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3
Predictor selection method for the construction of support vector machine (SVM)‐based typhoon rainfall forecasting models using a non‐dominated sorting genetic algorithm
Predictor selection method for the construction of support vector machine (SVM)‐based typhoon rainfall forecasting models using a non‐dominated sorting genetic algorithm
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Predictor selection method for the construction of support vector machine (SVM)‐based typhoon rainfall forecasting models using a non‐dominated sorting genetic algorithm

Yang, Tao‐Chang ; Yu, Pao‐Shan ; Lin, Kun‐Hsiang ; Kuo, Chen‐Min ; Tseng, Hung‐Wei

Meteorological applications, 2018-10, Vol.25 (4), p.510-522 [Periódico revisado por pares]

Chichester, UK: John Wiley & Sons, Ltd

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4
Incorporating large-scale atmospheric variables in long-term seasonal rainfall forecasting using artificial neural networks: an application to the Ping Basin in Thailand
Incorporating large-scale atmospheric variables in long-term seasonal rainfall forecasting using artificial neural networks: an application to the Ping Basin in Thailand
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Incorporating large-scale atmospheric variables in long-term seasonal rainfall forecasting using artificial neural networks: an application to the Ping Basin in Thailand

Babel, M. S. ; Sirisena, T. A. J. G. ; Singhrattna, N.

Hydrology Research, 2017-06, Vol.48 (3), p.867-882 [Periódico revisado por pares]

London: IWA Publishing

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5
Typhoon Rainfall Forecasting by Means of Ensemble Numerical Weather Predictions with a GA-Based Integration Strategy
Typhoon Rainfall Forecasting by Means of Ensemble Numerical Weather Predictions with a GA-Based Integration Strategy
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Typhoon Rainfall Forecasting by Means of Ensemble Numerical Weather Predictions with a GA-Based Integration Strategy

Wu, Ming-Chang ; Yang, Sheng-Chi ; Yang, Tsun-Hua ; Kao, Hong-Ming

Atmosphere, 2018-11, Vol.9 (11), p.425 [Periódico revisado por pares]

Basel: MDPI AG

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6
Skilful precipitation nowcasting using deep generative models of radar
Skilful precipitation nowcasting using deep generative models of radar
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Skilful precipitation nowcasting using deep generative models of radar

Ravuri, Suman ; Lenc, Karel ; Willson, Matthew ; Kangin, Dmitry ; Lam, Remi ; Mirowski, Piotr ; Fitzsimons, Megan ; Athanassiadou, Maria ; Kashem, Sheleem ; Madge, Sam ; Prudden, Rachel ; Mandhane, Amol ; Clark, Aidan ; Brock, Andrew ; Simonyan, Karen ; Hadsell, Raia ; Robinson, Niall ; Clancy, Ellen ; Arribas, Alberto ; Mohamed, Shakir

Nature (London), 2021-09, Vol.597 (7878), p.672-677 [Periódico revisado por pares]

England: Nature Publishing Group

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7
A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning
A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning
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A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning

Xiang, Zhongrun ; Yan, Jun ; Demir, Ibrahim

Water resources research, 2020-01, Vol.56 (1), p.n/a [Periódico revisado por pares]

Washington: John Wiley & Sons, Inc

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8
Applicability of Simulated Precipitation for the Rainfall Flood Forecasting in the Amur Basin
Applicability of Simulated Precipitation for the Rainfall Flood Forecasting in the Amur Basin
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Applicability of Simulated Precipitation for the Rainfall Flood Forecasting in the Amur Basin

Verbitskaya, E. M. ; Ageeva, S. V. ; Gonchukov, L. V. ; Romanskii, S. O.

Russian meteorology and hydrology, 2024, Vol.49 (1), p.17-24 [Periódico revisado por pares]

Moscow: Pleiades Publishing

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9
Skilful nowcasting of extreme precipitation with NowcastNet
Skilful nowcasting of extreme precipitation with NowcastNet
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Skilful nowcasting of extreme precipitation with NowcastNet

Zhang, Yuchen ; Long, Mingsheng ; Chen, Kaiyuan ; Xing, Lanxiang ; Jin, Ronghua ; Jordan, Michael I ; Wang, Jianmin

Nature (London), 2023-07, Vol.619 (7970), p.526-532 [Periódico revisado por pares]

England: Nature Publishing Group

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10
Flash Flood and Extreme Rainfall Forecast through One-Way Coupling of WRF-SMAP Models: Natural Hazards in Rio de Janeiro State
Flash Flood and Extreme Rainfall Forecast through One-Way Coupling of WRF-SMAP Models: Natural Hazards in Rio de Janeiro State
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Flash Flood and Extreme Rainfall Forecast through One-Way Coupling of WRF-SMAP Models: Natural Hazards in Rio de Janeiro State

da Cunha Luz Barcellos, Priscila ; Cataldi, Marcio

Atmosphere, 2020-08, Vol.11 (8), p.834 [Periódico revisado por pares]

Basel: MDPI AG

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