Enhancing disaster mutual assistance decisions with machine learning: case of electricity utilities Online publication date: Mon, 23-Aug-2021
by Ali Asgary; Ghassem Tofighi; Mohammad Ali Tofighi
International Journal of Emergency Management (IJEM), Vol. 16, No. 4, 2020
Abstract: Disaster mutual assistance (DMA) is an important mechanism that is used by many organisations including electricity utilities to generate the needed resources during major disasters and emergencies. Decision to provide (or not to provide) mutual assistance is a complicated decision that needs to be made considering multiple factors and under time pressure and uncertainty. This paper applies several machine learning algorithms to enhance DMA decisions by electricity utilities. These methods are implemented on an experimental dataset obtained during a workshop participated by disaster management experts from several Canadian electricity utilities. Results show that all of the employed machine learning methods have very high and almost similar accuracy in predicting DMA decisions. However, Random Forest and Decision Tree provide additional information by generating the weight of each criterion, optimum thresholds that can be applied to each criterion, and visual interpretation of the decision process.
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