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There’s no best model! Addressing limitations of land-use scenario modelling through multi-model ensembles / Richard J. Hewitt in International journal of geographical information science IJGIS, vol 36 n° 12 (December 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 12 (December 2022) Titre : There’s no best model! Addressing limitations of land-use scenario modelling through multi-model ensembles Type de document : Article/Communication Auteurs : Richard J. Hewitt, Auteur ; Majid Shadman Roodposhti, Auteur ; Brett A. Bryan, Auteur Année de publication : 2022 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] automate cellulaire [Termes IGN] étalonnage de modèle [Termes IGN] étalonnage des données [Termes IGN] incertitude des données [Termes IGN] utilisation du sol Résumé : (auteur) Cellular automata models are popular tools for exploring future land change pathways. But simulation modelling approaches often focus too narrowly on calibration against historic reference maps, limiting the diversity of possible outcomes. We argue that, contrary to what is commonly believed, there is no ‘best model’, and that model specification and calibration accuracy depend on the objective of the research. We propose a multi-model ensemble approach, in which a wide range of models and calibration rules sets are systematically tested against multiple metrics. We apply our approach to a case study in Spain. No single model performed well for all statistics, illustrating the danger of cherry-picking statistics for best performance. In our case study, accounting for historic land changes in model design was useful for simulating compact urban development, but limited the variability of simulation outcomes. The accessibility model driver improved urban pattern replication, while suitability without accessibility was useful for simulating low-density development encroaching on natural areas. Rather than abandoning calibrations that show low agreement with reference maps based on a small number of metrics we should seek to understand what each metric is telling us and use this information to enrich the diversity of simulated outcomes. Numéro de notice : A2022-616 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2098299 Date de publication en ligne : 03/08/2022 En ligne : https://doi.org/10.1080/13658816.2022.2098299 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101372 [article]