Joaquín Izquierdo

Particle Swarm Optimization applied to the design of water supply systems

In the past decade, evolutionary methods have been used by various researchers to tackle optimal design problems for water supply systems (WSS). Particle Swarm Optimization (PSO) is one of these evolutionary algorithms which, in spite of the fact that it has primarily been developed for the solution of optimization problems with continuous variables, has been successfully adapted in other contexts to problems with discrete variables. In this work we have applied one of the variants of this algorithm to two case studies: the Hanoi water distribution network and the New York City water supply tunnel system. Both cases occur frequently in the related literature and provide two standard networks for benchmarking studies. This allows us to present a detailed comparison of our new results with those previously obtained by other authors.

Design optimization of wastewater collection networks by PSO

Optimal design of wastewater collection networks is addressed in this paper by making use of the so-called PSO (Particle Swarm Optimization) technique. This already popular evolutionary technique is adapted for dealing both with continuous and discrete variables as required by this problem. An example of a wastewater collection network is used to show the algorithm performance and the obtained results are compared with those given by using dynamic programming to solve the same problem under the same conditions. PSO is shown to be a promising method to solve optimal design problems regarding, in particular, wastewater collection networks, according to the results herein obtained.

Improved performance of PSO with self-adaptive parameters for computing the optimal design of Water Supply Systems

This paper deals with a new variant of Particle Swarm Optimization (PSO) in which no a priori parameter tuning is necessary. PSO, as an efficient and powerful problem-solving technique, has been widely used, but, as in other Evolutionary Algorithms (EA), appropriate adjustment of its parameters is cumbersome and usually requires a lot of time, effort and luck. Thus, a self-adaptive framework is proposed to improve the robustness of the PSO. In this paper, within a framework that also includes other variants previously introduced by the authors, the algorithms parameters are co-evolved with the particles. Its performance results show that the use of this self-adaptive feature averages out the performance of standard PSO and other EA applied to the same problems, namely the design of Water Supply Systems (WSS), while avoiding the process of localizing and fine-tuning suitable parameters values, when using two benchmarking problems presented in the literature, namely the Hanoi Water Supply System and the New York Tunnel Water Supply System. The results provided in the case of a real-world problem demonstrate the scalability of the proposed variant to the realistic water distribution design problems, which are much larger.

A diversity-enriched variant of discrete PSO applied to the design of water distribution networks

The design of water distribution networks (WDNs) is addressed by using a variant of the particle swarm optimization (PSO) algorithm. This variant, which makes use of a discrete version of PSO already considered by the authors, overcomes one of the PSOs main drawbacks, namely its difficulty in maintaining acceptable levels of population diversity and in balancing local and global searches. The performance of the variant proposed here is investigated by applying the model to solve two standard benchmark problems: the Hanoi new water distribution network and the New York Tunnel water supply system. The results obtained show considerable improvements in both convergence characteristics and the quality of the final solutions, and near-optimal results are consistently achieved at reduced computational cost.

Balancing consistency and expert judgment in AHP

The various mechanisms that represent the know-how of decision-makers are exposed to a common weakness, namely, a lack of consistency. To overcome this weakness within AHP (analytic hierarchy process), we propose a framework that enables balancing consistency and expert judgment. We specifically focus on a linearization process for streamlining the trade-off between expert reliability and synthetic consistency. An algorithm is developed that can be readily integrated in a suitable DSS (decision support system). This algorithm follows an iterative feedback process that achieves an acceptable level of consistency while complying to some degree with expert preferences. Finally, an application of the framework to a water management decision-making problem is presented.

Mathematical modelling of hydraulic transients in simple systems

Efficiency and economy in the design and operation of a hydraulic system, as well as its safety, are objectives needing precise calculations of pressures and flowrates within the system. The calculations are typically very time-consuming and, depending on the characteristics of the system, very complicated and difficult to organize. A suitable mathematical modelling of the different ingredients in a hydraulic system is necessary to get useful results, which help fulfill those objectives. In this paper, the mathematical modelling used to develop a computer program to simulate hydraulic transients in a simple system is described. The program (DYAGATS), developed by the authors, is currently being used by organizations and consultancies to simulate and, consequently, analyze hydraulic transients in water systems. It makes use of the so-called elastic model, also known as waterhammer, to model the behavior of the fluid within the pipes. Also, lump models for the different elements that introduce, damp, modify, absorb, etc., perturbations in the systems are presented in a unified treatment. The main objective is to provide users with a powerful tool to devise the potential risks to which an installation may be exposed and to develop suitable protection strategies.

Water distribution system computer-aided design by agent swarm optimization

Optimal design of water distribution systems (WDSs), including the sizing of components, quality control, reliability, renewal, and rehabilitation strategies, etc., is a complex problem in water engineering that requires robust methods of optimization. Classical methods of optimization are not well suited for analyzing highly dimensional, multimodal, nonlinear problems, especially given inaccurate, noisy, discrete, and complex data. Agent Swarm Optimization (ASO) is a novel paradigm that exploits swarm intelligence and borrows some ideas from multiagent-based systems. It is aimed at supporting decision-making processes by solving multiobjective optimization problems. ASO offers robustness through a framework where various population-based algorithms coexist. The ASO framework is described and used to solve the optimal design of WDS. The approach allows engineers to work in parallel with the computational algorithms to force the recruitment of new searching elements, thus contributing to the solution process with expert-based proposals.

Hybrid regression model for near real-time urban water demand forecasting

The 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. In recent 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 regarding on-line predictive models for water demand have appeared. 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. On this model, a Fourier time series process is built to improve the base prediction. This addition produces a tool able to eliminate many of the errors and much of the bias inherent in a fixed regression structure when responding 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.

Joint stakeholder decision-making on the management of the Silao-Romita aquifer using AHP

Over-exploitation and pollution have been identified as the main problems facing the Silao-Romita aquifer in Guanajuato, Mexico. The objective of this paper is to analyze the current situation, characterized by a clear lack of legislative enforcement, dispersion of competences, and scarcity of economic resources, in order to establish a new prioritization of action plans, and choose from among three specific management options. One of the main challenges when addressing these problems in a holistic manner is the conflicting viewpoints of the sectors involved. As each stakeholder has a different perception, there is a clear need for appropriate mechanisms to reach a consensus in decision-making. To achieve the objective, we use the Analytic Hierarchy Process (AHP), because of its flexibility and the availability of mathematical axiomatic principles and techniques to obtain group preferences and priorities. In addition, we use several tools developed by the authors to obtain consistency, streamline the trade-off between stakeholder know-how and synthetic consistency, and consistently complete partial judgments given by some of the stakeholders. The problem of obtaining a consensus among the actors involved regarding criteria and alternatives is also considered. The obtained results are intended to serve as guidelines for conducting priority actions to help solve the general problem of the study area, and to identify the management model that best meets the needs of the aquifer, according to the actors involved. The Silao-Romita aquifer in Guanajuato (Mexico) suffers from over-exploitation and pollution.AHP is used as an alternative to mainstream experience in decision-making.The problem of obtaining a consensus among stakeholders is extensively considered.Guidelines for priority actions to help solve problems of the area are developed.

An analytic hierarchy process for assessing externalities in water leakage management

One of the main challenges that water supply managers face is that of minimizing the water lost through leakage from the systems that they operate. To minimize water losses, substantial amounts of money must be invested every year in leak detection and repairs. This investment is usually balanced by the benefits derived from the use of the recovered water. Nevertheless, this scheme of economic assessment does not reflect the whole dimension of the profit-earning capacity of repairing leaks; and the associated benefits go beyond just the economic value of the recovered water. In this paper, we consider a new economic assessment approach that includes all the costs incurred by the existence of leaks and the benefits derived from their control. The main difficulty derives from the fact that comparisons with regard to certain properties will only work for properties with well-defined scales of measurement. The analytic hierarchy process of Saaty (2008) [1] provides a useful way to establish relative scales that are derived by making pairwise comparisons using numerical judgments from an absolute scale of numbers. The unique largest eigenvalue and the Perron eigenvector of the matrix of criteria provide, taking into account the properties that this matrix exhibits, the necessary information for dealing with complex decisions that involve dependence and feedback. These decisions are analyzed in the context of benefits, opportunities, costs, and risks. We also present a method for improving the consistency of the comparisons - as consistency may be affected by subjectivity. The main conclusion is that water supply managers and authorities should shift direction from purely economic policies based on passive leakage control towards new social and environmental policies that consider more proactive actions.