Neural-network-based cellular automata for simulating multiple land use changes using GIS

By Xia Li, Anthony Gar-On Yeh. International Journal of Geographical Information Science, 2002

916 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/bc0140603959d4c5f3b63d5bd159e3841b5ac836

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Citations in the corpus

Urban sustainability in developing countries’ megacities: modelling and predicting future urban growth in Lagos (2003, background): “…make them suitable for urban simulation (Batty and Longley, 1986, 1987; Cecchini and Viola, 1990; White and Engelen, 1993; Itami, 1994; White et al., 1997; Wu, 1998; Clarke and Gaydos, 1998; White et al., 1999; White and Engelen, 2000; Semboloni, 2000; Li and Yeh, 2000, 2002; Sui and Zeng, 2001).”
Spatiotemporal heterogeneity of urban planning implementation effectiveness: Evidence from five urban master plans of Beijing (2012, neutral): “…are quantitatively identified by arious approaches, such as Markov-chain analysis (Muller & iddleton, 1994), artificial neural networks (ANNs) (Li & Yeh, 2002; ajanowski, Brown, Shellito, & Manik, 2002), cellular automata CA) (Batty, Couclelis, & Eichen, 1997; Dietzel & Clarke, 2006; hite &…”
A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects (2017, methodology): “The traditional ANN-CA model is typically applied to the simulation of multiple LUCCs via the integration of neural networks with cellular automata, but it does not consider the interaction and competition of different land use types.”
Flood hazard risk assessment model based on random forest (2015, methodology): “The ANN method shows over-learning and slow convergence speed problems (Li and Yeh, 2002).”
A method to analyse neighbourhood characteristics of land use patterns (2004, methodology): “Li and Yeh (2001, 2002) propose a method that overcomes the definition problem of the transition rules of a CA model by training artificial neural networks.”
Exploring Complexity in a Human–Environment System: An Agent-Based Spatial Model for Multidisciplinary and Multiscale Integration (2005, methodology): “These methods include multi-variate spatial models (e.g., Seto and Kaufmann 2003), Markov chain analysis (e.g., Brown, Pijanowski, and Duh 2000; Lo´pez et al. 2001), and cellular automata (e.g., Batty, Xie, and Sun 1994; Li and Yeh 2002; Malanson 2002).”
Prediction of Land Use and Land Cover Changes for North Sumatra, Indonesia, Using an Artificial-Neural-Network-Based Cellular Automaton (2019, methodology): “If the highest transition probability is lower than the threshold value, which is 0.9 in this study based on Li and Yeh [2], then the cell remains unchanged.”
Using Shapley additive explanations to interpret extreme gradient boosting predictions of grassland degradation in Xilingol, China (2021, methodology): “This type of model is used to characterise the multiple aspects of LUCC patterns and processes (Li and Yeh, 2002; Sun and Müller, 2013) Agent-based models (ABM) simulate land use change decisions based on the behaviour of individual decision-makers They often consider economic and political…”
Land use change modelling: current practice and research priorities (2004, background): “A few recent publications address this issue and provide techniques to empirically quantify cellular automata models (Sui and Zeng, 2001; Li and Yeh, 2002; Verburg et al., 2004).”