Many large-scale systems and critical industries such as transportation, telecommunications, power, and banking share significant resources, and the flow of goods and information constantly take place among these different industry sectors (Pant et al., 2011). When such systems and their subsystems are put under external shocks and duress, they suffer physical and economic collapse. While little can be done to prevent the occurrences of extreme events, preparedness measures help in lessening the impact of resulting disruptions. Interest lies in predicting the adverse impacts of disruptive events in an interdependent economy and evaluating risk management efforts to lessen these impacts. Due to the practicality of such an approach there has been a paradigm shift in infrastructure security, where the emphasis on ‘preparedness and response’ has been added to the ‘protection and prevention’ approach. In particular the ability of the system components to recover from disruptions and operate at previous or new stable production levels characterizes resilience. This research addresses the problem of estimating, quantifying and planning for resilience in interdependent systems, where interconnectedness adds to problem complexity.

Interdependence, which defines the level of resource sharing among sectors, drives the behavior of systems before and after disruptions. Since infrastructure interconnectedness leads to improved efficiency during normal operations there is interest in preserving interdependence during disruptions because it can be utilized for speedy recoveries. Among other approaches this study concentrates on macroeconomic interdependence because it provides insights into other levels of interdependence.

The inoperability input-output model (IIM) enterprise is employed and expanded in this study to provide a useful tool for measuring the cascading effects of disruptions across large-scale interdependent infrastructural systems. This research defines economic resilience for interdependent infrastructures as an “ability exhibited by such systems that allows them to recover productivity after a disruptive event in a desired time and/or with an acceptable cost”. Through the dynamic interdependent risk model resilience for a disrupted infrastructure is quantified in terms of its average system functionality, maximum loss in functionality and the time to recovery, which make up a resilience estimation decision-space (Pant et al., 2013). Estimating such a decision-space through the dynamic model depends upon the estimation of the recovery rate parameter in the model.

This research proposes a new approach, based on dynamic data assimilation methods, for estimating the recovery rate parameter and strengthening post-disaster resilience of economic systems. Data assimilation refers to the technique of combining observations with the model to improve its predictive nature. Of particular interest to us are dynamic data assimilation methods that improve the forecasting capability of the model. The solution to the data assimilation problem generates estimates for the rate of resilient recovery that reflects planning considerations interpreted as commodity substitutions, inventory management and incorporating redundancies.

The analysis approach is employed in an application of disaster recovery at inland ports. Given the annual import-export of commodities at the port we are interested in analyzing the recovery of industries if there is an event leading to port shutdown for a few days. Of particular interest in the analysis is the resilience each industry would have in order to achieve a level of recovery at some time once the port has started recovering. For any industrial sector this becomes a valid concern as it helps in planning for recovery according to set targets. At the marcoeconomic scale such effects can be extrapolated and interpreted at the regional and national economic scales. Overall the resilience estimation approach suggested in this research can be applied to the overall economy to obtain the best possible resiliences options dependent upon time and recovery patterns.