  From an optimization point of view, resource allocation is one of the
cornerstones of research for addressing limiting factors commonly arising in
applications such as power outages and traffic jams. In this paper, we take a
data-driven approach to estimate an optimal nodal restoration sequence for
immediate recovery of the infrastructure networks after natural disasters such
as earthquakes. We generate data from td-INDP, a high-fidelity simulator of
optimal restoration strategies for interdependent networks, and employ deep
neural networks to approximate those strategies. Despite the fact that the
underlying problem is NP-complete, the restoration sequences obtained by our
method are observed to be nearly optimal. In addition, by training multiple
models---the so-called estimators---for a variety of resource availability
levels, our proposed method balances a trade-off between resource utilization
and restoration time. Decision-makers can use our trained models to allocate
resources more efficiently after contingencies, and in turn, improve the
community resilience. Besides their predictive power, such trained estimators
unravel the effect of interdependencies among different nodal functionalities
in the restoration strategies. We showcase our methodology by the real-world
interdependent infrastructure of Shelby County, TN.
