Benefits of a multiple‐solution approach in land change models

[en] Land change (LC) models are dedicated to a better understanding of land use and land cover dynamics. A fundamental aspect of those models lies in the calibration of spatial parameters underlying such dynamics. Although there are many studies on the calibration of LC models, current efforts have a common goal of seeking to find a single global optimum solution, even though land change dynamics may be inherently heterogeneous throughout a given space. This article presents a calibration approach for finding multiple optimal solutions. A crowding niching genetic algorithm (CNGA) is incorporated into a cellular automata LC model. The model is applied to simulate urban expansion in Wallonia (Belgium) as a case study. Our findings demonstrate the ability of the model to locate multiple solutions simultaneously. In addition, the CNGA performs better than the standard genetic algorithm—besides, the CNGA helps to better understand the properties of land change dynamics within a given landscape.

Research center :

LEMA

Disciplines :

Engineering, computing & technology: Multidisciplinary, general & others

Author, co-author :

Mustafa, Ahmed Mohamed El Saeid ; Université de Liège - ULiège > Département ArGEnCo > LEMA (Local environment management and analysis)

Ebaid, Amr

Teller, Jacques ; Université de Liège - ULiège > Département ArGEnCo > Urbanisme et aménagement du territoire

Language :

English

Title :

Benefits of a multiple‐solution approach in land change models

Publication date :

2018

Journal title :

Transactions in GIS

ISSN :

1361-1682

eISSN :

1467-9671

Publisher :

Wiley, Oxford, United Kingdom

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://onlinelibrary.wiley.com/doi/full/10.1111/tgis.12482

Name of the research project :

Wal-e-Cities Project

Funders :

The research was funded through the European Regional Development Fund – FEDER.

Available on ORBi :

since 17 October 2018

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