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Self-Learning Cellular Automata for Forecasting Precipitation from Radar Images
This paper presents a new forecasting methodology that uses self-learning cellular automata (SLCA) for including variables that consider the spatial dynamics of the mass of precipitation in a radar forecast model. Because the meteorological conditions involve nonlinear dynamic behavior, an automatic learning model is used to aid the cellular automata rules (SLCA). The new methodology is applied to the western part of England (Brue river basin) using NIMROD data. The radar information from 1 month of hourly radar measurements is used. Two models, a regression model tree (MT) and an artificial neural network (ANN) model, are used to learn the dynamics of the spatially local effects within the cellular automation (CA) neighboring areas. A spatial correlation (tracking pattern) reference model is built for comparing the first hour of precipitation forecast. Model results show that the SLCA is more accurate than conventional tracking. Furthermore, it appears that this technique can be extended to include other important atmospheric variables in forecasting processes.
Self-Learning Cellular Automata for Forecasting Precipitation from Radar Images
This paper presents a new forecasting methodology that uses self-learning cellular automata (SLCA) for including variables that consider the spatial dynamics of the mass of precipitation in a radar forecast model. Because the meteorological conditions involve nonlinear dynamic behavior, an automatic learning model is used to aid the cellular automata rules (SLCA). The new methodology is applied to the western part of England (Brue river basin) using NIMROD data. The radar information from 1 month of hourly radar measurements is used. Two models, a regression model tree (MT) and an artificial neural network (ANN) model, are used to learn the dynamics of the spatially local effects within the cellular automation (CA) neighboring areas. A spatial correlation (tracking pattern) reference model is built for comparing the first hour of precipitation forecast. Model results show that the SLCA is more accurate than conventional tracking. Furthermore, it appears that this technique can be extended to include other important atmospheric variables in forecasting processes.
Self-Learning Cellular Automata for Forecasting Precipitation from Radar Images
Li, Hong (author) / Corzo Perez, Gerald A. (author) / Martinez, Carlos A. (author) / Mynett, Arthur E. (author)
Journal of Hydrologic Engineering ; 18 ; 206-211
2013-01-15
62013-01-01 pages
Article (Journal)
Electronic Resource
English
Self-Learning Cellular Automata for Forecasting Precipitation from Radar Images
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