Sustainable dam site selection using artificial intelligence-based graph neural networks with MCDM
Abstract
Identification of suitable location for construction of dam/reservoir is important for the sustainable development and flood control. This paper introduces a novel application between Artificial Intelligence (AI) and multi-criteria decision making (MCDM) for the enhancement of dam site suitability evaluation. In this research, Graph Neural Networks (GNNs) were introduced to automatically discriminate the MIF weights and substituted the weight schemes used in previous MCDM methods. A total of 12 climatic, geophysical, and accessibility indices working under a Geographic Information System (GIS) were considered. The GNN-weights were used in weighted overlay analysis to create a dam suitability map, and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was applied for ranking possible dam locations. The results show that the GNN-based weights can significantly improve model accuracy, with the AUC value increasing from 0.806 to 0.826 after incorporating them into consideration. The top-ranked site was the same as previous reports, indicating that the method we used is robust. This AI-enhanced framework has greatly advanced the objectivity and predictability of dam site selection strategies, thereby enhancing the moderator's ability to make informed decisions when choosing sites for sustainable water infrastructures.
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References
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