Abstract:
The growing energy demand of Sri Lanka exposes the vital importance of sustainable and stable
power generation. Long-term sustainable energy supply can be provided through nuclear power,
which is a low-emission energy source. However, site selection for a Nuclear Power Plant (NPP)
is a multi-disciplinary, multi-criteria, and complicated process that must address environmental,
technical, and socio-economic aspects. This study applies a GIS-based Multi-Criteria Decision
Analysis (MCDA) approach, which integrates the Analytical Hierarchy Process (AHP) and
Preference Ranking Organisation Method for Enrichment Evaluation II (PROMETHEE-II), for
identifying suitable NPP sites for Sri Lanka. The main objective of this study was to identify
sites that minimise environmental risk, however, are also technologically feasible. Some of
these selected factors consisted of river distance, seismism, population, sites that are protected,
and availability of a grid because they are highlighted in International Atomic Energy Agency
(IAEA) guidelines as well as national development plans. Geospatial data were provided by the
OpenStreetMap, Department of Meteorology, Ceylon Electricity Board (CEB) and the Geological
Survey and Mines Bureau (GSMB) of Sri Lanka. The standardised criterion layers were
compared through AHP based on expert judgment coupled with verification of consistency ratio
using ArcGIS Pro software. PROMETHEE-II was employed for the processing of preference
flows for the ranking of alternatives. Results showed that the sites of Karachchi, Poonakary,
Vanathavilluwa, and Manthai West are most viable, excluding tsunami-prone sites for safe considerations.
The integration of GIS with AHP and PROMETHEE-II provided transparent, repeatable,
and data-driven decision-making, with a structured approach that was consistent with
international nuclear safety guidelines. These outcomes are a planning tool for future nuclear
energy development for policymakers from Sri Lanka and offer verification of the merit of spatial
decision-support systems for energy planning.
