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In a previous chapter (Klimberg, Ratick, & Smith, 2018), we introduced a novel approach in which cluster centroids were used as input data for the predictor variables of a multiple linear regression (MLR) used to forecast fleet maintenance costs. We applied this approach to a real data set and significantly improved the predictive accuracy of the MLR model. In this chapter, we develop a methodology for adjusting moving average forecasts of the future values of fleet service occurrences by interpolating those forecast values using their relative distances from cluster centroids. We illustrate and evaluate the efficacy of this approach with our previously used data set on fleet maintenance.

A major consequence of global environmental change is projected to be the alteration in flood periodicity, magnitude, and geographic patterns. There are a number of extant methods designed to help identify areas vulnerable to these consequences, the construction of composite vulnerability indices prominent among them. In this paper we have implemented the Order Rated Effectiveness (ORE) model (Klimberg & Ratick, 2020) to produce composite flood vulnerability indicators through the aggregation of six constituent vulnerability indicators future projected for 204 hydrologic subbasins that cover the contiguous US. The ORE aggregation results, when compared with those obtained using the Weighted Linear Combination and Data Envelopment Analysis, provided a more robust and actionable distribution of composite vulnerability results for decision-makers when prioritizing Hydrologic Unit Codes for further analysis and for effectively and efficiently implementing adaptation and mitigation strategies to address the flooding consequences due to global climate change.

The accuracy of forecasts has a critical impact on an organization. A new, practical, and meaningful forecast performance measure, percentage forecasting error (PFE), was introduced by the authors in an earlier publication. In this chapter, we examined the accuracy of the PFE under several different scenarios and found the results to indicate that PFE offers forecasters an accurate and practical alternative to assess forecast accuracy.