Integrating local multi-criteria evaluation with spatially explicit uncertainty-sensitivity analysis

Şalap-Ayça, Seda; Jankowski, Piotr

doi:10.1080/13875868.2015.1137578

Cited by 21 publications

(7 citation statements)

References 42 publications

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“…For the model validation, it was proposed to perform a SA in both study areas, in order to delve into the components of the model and the degree of influence in the variation of the results. In the context of SA associated with MCE techniques the approach could be local or global [ 76 , 77 , 78 ]. The local SA consists in altering one factor each time and leaving the rest fixed [ 79 ].…”

Section: Methodsmentioning

confidence: 99%

Multi-Criteria Evaluation and Sensitivity Analysis for the Optimal Location of Constructed Wetlands (METland) at Oceanic and Mediterranean Areas

Peñacoba-Antona

Delgado

Esteve‐Núñez

2021

IJERPH

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METland is a new variety of Constructed Wetland (CW) for treating wastewater where gravel is replaced by a biocompatible electroconductive material to stimulate the metabolism of electroactive bacteria. The system requires a remarkably low land footprint (0.4 m2/pe) compared to conventional CW, due to the high pollutant removal rate exhibited by such microorganisms. In order to predict the optimal locations for METland, a methodology based on Multi-Criteria Evaluation (MCE) techniques applied to Geographical Information Systems (GIS) has been proposed. Seven criteria were evaluated and weighted in the context of Analytical Hierarchy Process (AHP). Finally, a Global Sensitivity Analysis (GSA) was performed using the Sobol method for resource optimization. The model was tested in two locations, oceanic and Mediterranean, to prove its feasibility in different geographical, demographic and climate conditions. The GSA revealed as conclusion the most influential factors in the model: (i) land use, (ii) distance to population centers, and (iii) distance to river beds. Interestingly, the model could predict best suitable locations by reducing the number of analyzed factors to just such three key factors (responsible for 78% of the output variance). The proposed methodology will help decision-making stakeholders in implementing nature-based solutions, including constructed wetlands, for treating wastewater in rural areas.

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Section: Methodsmentioning

confidence: 99%

Multi-Criteria Evaluation and Sensitivity Analysis for the Optimal Location of Constructed Wetlands (METland) at Oceanic and Mediterranean Areas

Peñacoba-Antona

Delgado

Esteve‐Núñez

2021

IJERPH

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“…Those differences can be expressed in weight ranges, which indicate uncertainties concerning the expert preferences [77]. Examples of spatial uncertainty and sensitivity analysis can be found in [88][89][90][91].…”

Section: Discussionmentioning

confidence: 99%

Can ISO-Defined Urban Sustainability Indicators Be Derived from Remote Sensing: An Expert Weighting Approach

et al. 2018

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Abstract:In the context of the United Nations' "Agenda 2030 for Sustainable Development" and the presented Sustainable Development Goals (SDGs), the process of developing and agreeing on indicators to monitor the SDGs implementation becomes fundamental. In this paper, we identify indicators for the sustainable development of cities that have the greatest potential for their underlying data to be measured by means of remote sensing. We first identified existing indicators, which are derived from the International Standard ISO 37120, "Indicators for city services and quality of life", as being partly or fully measured by the use of remote sensing, and then presented these indicators to remote sensing experts in an assessment procedure. We then investigated Multi-Criteria Decision-Making (MCDM) weighting methods to identify the most relevant quality of life indicators that can be captured by means of remote sensing techniques. We assess the remote sensing experts' knowledge in the context of Decision Support Systems (DSS), and by means of both a questionnaire-based approach and a pairwise comparison approach. The approaches are compared with each other regarding their complexity, their potentials and limitations, and the respectively identified remote sensing based indicators. We identified three indicators related to surface characteristics as having the highest remote sensing potential. When contrasted to the results of the pairwise comparison, the questionnaire-based approach revealed high usability and confirmability. In the end, this approach enables cities' administrations to decide which indicators they want to cover by means of remote sensing, depending on the capacities of their departments.

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“…As stated by Malczewski (1999, p. 226), the IP method avoids some of the difficulties associated with the interdependence-among-attributes assumption. Several studies have integrated TOPSIS in the context of S-MCDA (Ligmann-Zielinska & Jankowski, 2014;Feizizadeh et al, 2014;Şalap-Ayça & Jankowski, 2016;Erlacher et al, 2017).…”

Section: The Seusa Frameworkmentioning

confidence: 99%

Parallel and Distributed Computing for large raster-based Spatial Multicriteria Decision Analysis Problems: A Computational Performance Comparison

Erlacher¹,

Desch²,

Anders³

et al. 2019

giforum

Self Cite

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This article focuses on a cluster-based parallel and distributed approach for large raster datasets in the context of Spatial Multicriteria Decision Analysis (S-MCDA). The research addresses a land-prioritization model with respect to conservation practices. The reliability of the model results is examined using a variance-based Spatially-Explicit Uncertainty and Sensitivity (SEUSA) framework. The original case study area to which we applied the model was located in southwest Michigan, USA, and incorporated millions of mapping units (pixels). As part of the model sensitivity analysis, several thousand intermediate raster datasets representing suitability surfaces are generated by means of a Monte Carlo Simulation (MCS). The creation of the suitability surfaces represents the most timeconsuming and memory-intensive step within the SEUSA framework. Sequential computational approaches to implementing SEUSA often have to accept a compromise with respect to problem size and the number of simulations, resulting in the low quality of the model sensitivity measures. This article presents the concept and implementation of a distributed and parallel solution based on the Python-Dask framework in order to improve the quality of SEUSA results for computationally-intensive spatial models.

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Integrating local multi-criteria evaluation with spatially explicit uncertainty-sensitivity analysis

Cited by 21 publications

References 42 publications

Multi-Criteria Evaluation and Sensitivity Analysis for the Optimal Location of Constructed Wetlands (METland) at Oceanic and Mediterranean Areas

Multi-Criteria Evaluation and Sensitivity Analysis for the Optimal Location of Constructed Wetlands (METland) at Oceanic and Mediterranean Areas

Can ISO-Defined Urban Sustainability Indicators Be Derived from Remote Sensing: An Expert Weighting Approach

Parallel and Distributed Computing for large raster-based Spatial Multicriteria Decision Analysis Problems: A Computational Performance Comparison

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