A Time Monte Carlo method for addressing uncertainty in land-use change models

ABSTRACT

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.

KEYWORDS:

• Land-use allocation
• uncertainty
• stochastic disturbance
• Monte Carlo simulation
• cellular automata

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

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).