Reference : A Time Monte Carlo method for addressing uncertainty in land-use change models
Scientific journals : Article
Engineering, computing & technology : Multidisciplinary, general & others
http://hdl.handle.net/2268/226838
A Time Monte Carlo method for addressing uncertainty in land-use change models
English
Mustafa, Ahmed Mohamed El Saeid mailto [Université de Liège - ULiège > Département ArGEnCo > LEMA (Local environment management and analysis) >]
Saadi, Ismaïl [Université de Liège - ULiège > Département ArGEnCo > LEMA (Local environment management and analysis) >]
Cools, Mario [Université de Liège - ULiège > Département ArGEnCo > Transports et mobilité >]
Teller, Jacques [Université de Liège - ULiège > Département ArGEnCo > Urbanisme et aménagement du territoire >]
2018
International Journal of Geographical Information Science
Taylor & Francis
32
11
2317-2333
Yes (verified by ORBi)
International
1365-8816
1365-8824
United Kingdom
[en] Land-use allocation ; uncertainty ; stochastic disturbance ; Monte Carlo simulation ; cellular automata
[en] One of the main objectives of land-use change models is to explore future land-use patterns. Therefore, the issue of addressing uncertainty in land-use forecasting has received an increasing attention in recent years. Many current models consider uncertainty by including a randomness component in their structure. In this paper, we present a novel approach for tuning uncertainty over time, which we refer to as the Time Monte Carlo (TMC) method. The TMC uses a specific range of randomness to allocate new land uses. This range is associated with the transition probabilities from one land use to another. The range of randomness is increased over time so that the degree of uncertainty increases over time. We compare the TMC to the randomness components used in previous models, through a coupled logistic regression-cellular automata model applied for Wallonia (Belgium) as a case study. Our analysis reveals that the TMC produces results comparable with existing methods over the short-term validation period (2000–2010). Furthermore, the TMC can tune uncertainty on longer-term time horizons, which is an essential feature of our method to account for greater uncertainty in the distant future.
LEMA
The research was funded through the ARC grant for Concerted Research Actions for project number 13/17-01 financed by the French Community of Belgium (Wallonia-Brussels Federation); and through the European Regional Development Fund – FEDER (Wal-e-Cities Project).
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/2268/226838
10.1080/13658816.2018.1503275
https://www.tandfonline.com/doi/full/10.1080/13658816.2018.1503275

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