In environmental disaster management, due to the large impacted area or limited availability of labor and financial resources, setting priorities of where, how and when to act are indispensable. When prioritized interventions on spatially dispersed entities are costly and technically challenging to perform, clustering of individual entities in larger homogeneous actionable units can improve feasibility and reduce cost of the remediation. In this article, a spatio-temporal clustering approach under a budget constraint is presented to determine homogenous clusters of polygons and interventions to reduce cost while still attaining an overall optimal distribution of interventions. We demonstrate the effectiveness of this clustering algorithm with a hypothetical case study of contaminated agricultural land in Belgium. Finally, we demonstrate the capabilities of the proposed cluster algorithm to provide decision makers with a multi-period action plan, reducing the cost of intervention while still prioritizing resources for the most important sites.
|Number of pages||12|
|Journal||International Journal On Advances in Software|
|State||Published - 31 Dec 2022|