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a b s t r a c tMatrices used in the analytic hierarchy process (AHP) compile expert knowledge as pairwise comparisons among various criteria and alternatives in decision-making problems. Many items are usually considered in the same comparison process and so judgment is not completely consistent -and sometimes the level of consistency may be unacceptable. Different methods have been used in the literature to achieve consistency for an inconsistent matrix. In this paper we use a linearization technique that provides the closest consistent matrix to a given inconsistent matrix using orthogonal projection in a linear space. As a result, consistency can be achieved in a closed form. This is simpler and cheaper than for methods relying on optimisation, which are iterative by nature. We apply the process to a real-world decision-making problem in an important industrial context, namely, management of water supply systems regarding leakage policies -an aspect of water management to which great sums of money are devoted every year worldwide.

Elsevier Brentan, BM.; Luvizotto, E.; Herrera Fernández, AM.; Izquierdo Sebastián, J.; Pérez García, R. (2017). Hybrid regression model for near real-time urban water demand forecasting. AbstractThe most important factor in planning and operating water distribution systems is satisfying consumer demand. This means continuously providing users with quality water in adequate volumes at reasonable pressure, thus ensuring reliable water distribution. During the last years, the application of Statistical, Machine Learning and Artificial Intelligence methodologies has been fostered for water demand forecasting. However, there is still room for improvement and new challenges concerning to on-line predictive models for water demand have aroused. This work proposes applying support vector regression, as one of the currently better Machine Learning options for short-term water demand forecasting, to build a base prediction. Over this model, a Fourier time series process is built to improve the base prediction. This addition produces a tool able to get rid of part of the errors and bias inherent to a fixed regression structure in response to new incoming time series data. The final hybrid process is validated using demand data from a water utility in Franca, Brazil. Our model, being a near real-time model for water demand, may be directly exploited in water management decision-making processes.

Optimal design of water distribution systems (WDS), including the sizing of components

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