A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis
By Bakhtiar Feizizadeh, Piotr L. Jankowski, Thomas Blaschke. Computational Geosciences, 2014
299 citations recorded by the source corpus.
GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty and sensitivity analysis. We access the uncertainty of landslide susceptibility maps produced with GIS-MCDA techniques. A new spatially-explicit approach and Dempster–Shafer Theory (DST) are employed to assess the uncertainties associated with two MCDA techniques, namely Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA) implemented in GIS. The methodology is composed of three different phases. First, weights are computed to express the relative importance of factors (criteria) for landslide susceptibility. Next, the uncertainty and sensitivity of landslide susceptibility is analyzed as a function of weights using Monte Carlo Simulation and Global Sensitivity Analysis. Finally, the results are validated using a landslide inventory database and by applying DST. The comparisons of the obtained landslide susceptibility maps of both MCDA techniques with known landslides show that the AHP outperforms OWA. However, the OWA-generated landslide susceptibility map shows lower uncertainty than the AHP-generated map. The results demonstrate that further improvement in the accuracy of GIS-based MCDA can be achieved by employing an integrated uncertainty–sensitivity analysis approach, in which the uncertainty of landslide susceptibility model is decomposed and attributed to model's criteria weights.
Subfields: Management, Monitoring, Policy and Law, Safety, Risk, Reliability and Quality
View Paper: https://www.semanticscholar.org/paper/01cfd031b16f452688606af5b5a951c25ca8ece4
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In Management, Monitoring, Policy and Law
- Binh Thai Pham
- D. Tien Bui
- Hamid Reza Pourghasemi
- M. Dholakia
- P. Indra
- Indra Prakash
- Khabat Khosravi
- Biswajeet Pradhan
In Safety, Risk, Reliability and Quality
Citations in the corpus
- Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods (2017, background): “They were widely used in many landslide susceptibility researches (Chen et al. 2015; Dou et al. 2015a; Feizizadeh et al. 2014; Fourniadis et al. 2007; Martha et al. 2013; Vijith and Madhu 2008).”