A Fuzzy Logic-Based Approach for Modelling Uncertainty in Open Geospatial Data on Landfill Suitability Analysis


Lyimo N. N., Shao Z., Ally A. M., Twumasi N. Y. D., Altan O., Sanga C. A.

ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, cilt.9, sa.12, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 9 Sayı: 12
  • Basım Tarihi: 2020
  • Doi Numarası: 10.3390/ijgi9120737
  • Dergi Adı: ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Agricultural & Environmental Science Database, CAB Abstracts, INSPEC, Veterinary Science Database, Directory of Open Access Journals
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

Besides OpenStreetMap (OSM), there are other local sources, such as open government data (OGD), that have the potential to enrich the modeling process with decision criteria that uniquely reflect some local patterns. However, both data are affected by uncertainty issues, which limits their usability. This work addresses the imprecisions on suitability layers generated from such data. The proposed method is founded on fuzzy logic theories. The model integrates OGD, OSM data and remote sensing products and generate reliable landfill suitability results. A comparison analysis demonstrates that the proposed method generates more accurate, representative and reliable suitability results than traditional methods. Furthermore, the method has facilitated the introduction of open government data for suitability studies, whose fusion improved estimations of population distribution and land-use mapping than solely relying on free remotely sensed images. The proposed method is applicable for preparing decision maps from open datasets that have undergone similar generalization procedures as the source of their uncertainty. The study provides evidence for the applicability of OGD and other related open data initiatives (ODIs) for land-use suitability studies, especially in developing countries.