A GIS approach for hydrogeological risk assessment in infrastructural projects

Engineering problems in a multidisciplinary project like infrastructure contains extensive datasets with many geo-relevant parameters. While the abundance of data opens up even more in-depth analysis, the approach on how we used to handle data in the past has come to a turning point.

GIS platforms have proven to be an optimal tool to analyze and classify these multiparameter datasets and are a standard tool for risk assessment. Nevertheless, the current assessment design includes time-consuming processes due to the composition of the data consisting of: spatial, temporal and anthropogenic indicators.

In this thesis, we, therefore, intend to develop a GIS-based multi-criteria decision analysis resulting in an Hydrogeotechnical Susceptibility Index (HGSI). This index will be used to advise geotechnical project planning.

Within the Intercity Østfold project, one of the biggest infrastructure projects in Norway, Multiconsult offers a Master thesis with large multidisciplinary geo-related datasets to develop a semiautomatic GIS-workflow for future assessment of hydrogeological risk assessment. This study will build upon an existing risk analysis with already acquired datasets.

Key aspects related to the GIS assessment are:

  • Optimization of input data flow from a geotechnical database
  • Assessment and architecture of datasets, ranging from laboratory data to WMS-services.
  • Testing and generation of a workflow for a HSGI.
  • Analysis:
    • Suitability mapping defining decision rules, criteria, standardization and weight assessment (e.g.: Figure)
    • Sensitivity analysis to assess uncertainty
    • Optional: Testing of artificial neural network capabilities within ArcGis Pro to compare statistical versus data mining approaches
Example for a weighted overlay analysis (source: ESRI). Click here for a bigger version of the picture. 

To resolve the challenges arising within the project Multiconsult will support the successful candidate with senior in-house expertise and provision of latest technology and software solutions.

The main work will be carried out in ArcGIS Pro.

Candidates are free to choose their approach using existing commercial and open software tools which they assume to be most effective to solve the problem given. Knowledge or the will to get acquainted with Arc-GIS Pro and Python are essential to solve the problem formulation.

Literature (illustrative examples):

  • Akyol, E., Kaya, A., & Alkan, M. (2016). Geotechnical land suitability assessment using spatial multi-criteria decision analysis. Arabian Journal of Geosciences, 9(7), 498. https://www.researchgate.net/publication/303793637_Geotechnical_land_suitability_assessment_using_spatial_multi-criteria_decision_analysis
  • Gigović, L., Drobnjak, S., & Pamučar, D. (2019). The application of the hybrid gis spatial multi-criteria decision analysis best–worst methodology for landslide susceptibility mapping. ISPRS International Journal of Geo-Information, 8(2), 79.
  • Malczewski, J. (1999). GIS and multicriteria decision analysis. John Wiley & Sons.
  • Malczewski, J.; Rinner, C. (2015). Multicriteria Decision Analysis in Geographic Information Science; Springer: New York, NY, USA
  • Rikalovic, A., Cosic, I., & Lazarevic, D. (2014). GIS based multi-criteria analysis for industrial site selection. Procedia Engineering, 69, 1054-1063.
  • US Army Corps of Engineers Institute for Water Resources (2010) IWR Planning Suite MCDA module user’s guide. Carbondale, IL, USA. (Section 2)

Online resources:

Published Oct. 14, 2019 12:15 PM - Last modified Oct. 14, 2019 12:15 PM

Scope (credits)