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- W2738156680 abstract "ABSTRACTUrban cellular automata (CA) models propagate and accumulate errors during the modeling process due to the model structure or stochastic processes involved. It is feasible to assimilate real-time observations into an urban CA model to reduce model uncertainties. However, the assimilation performance is sensitive to the spatio-temporal units in the assimilation algorithm, that is, spatial block size and window length (temporal interval). In this study, we coupled an assimilation model, an ensemble Kalman filter (EnKF) and a Logistic-CA model to simulate the urban dynamic in Beijing over a period of two decades. Our results indicate that the coupled EnKF-CA model outperforms the CA-alone counterpart by about 10% in terms of the figure of merit, which reflects the agreement of modeled pixels. We also find that the assimilation performance using a finer block (1 km) is better than that using a coarser block (5 km and 10 km) because of the better depiction of spatial heterogeneity using a finer block. ..." @default.
- W2738156680 created "2017-07-31" @default.
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- W2738156680 date "2017-07-25" @default.
- W2738156680 modified "2023-10-16" @default.
- W2738156680 title "Exploring the performance of spatio-temporal assimilation in an urban cellular automata model" @default.
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- W2738156680 doi "https://doi.org/10.1080/13658816.2017.1357821" @default.
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