Rehabilitation of a water distribution system using sequential multiobjective optimization models

Identification of the optimal rehabilitation plan for a large water distribution system (WDS) with a substantial number of decision variables is a challenging task, especially when no supercomputer facilities are available. This paper presents an initiative methodology for the rehabilitation of WDS based on three sequential stages of multiobjective optimization models for gradually identifying the best-known Pareto front (PF). A two-objective optimization model is used in the first two stages where the objectives are to minimize rehabilitated infrastructure costs and operational costs. The optimization model in the first stage applies to a skeletonized WDS. The PFs obtained in Stage 1 are further improved in Stage 2 using the same two-objective optimization problem but for the full network. The third stage employs a three-objective optimization model by minimizing the cost of additional pressure reducing valves (PRVs) as the third objective. The suggested methodology was demonstrated through use of a real and large WDS from the literature. Results show the efficiency of the suggested methodology to achieve the optimal solutions for a large WDS in a reasonable computational time. Results also suggest the minimum total costs that will be obtained once maximum leakage reduction is achieved due to maximum possible pipeline rehabilitation without increasing the existing tanks

Feature Selection by Singular Value Decomposition for Reinforcement Learning

Solving reinforcement learning problems using value function approximation requires having good state features, but constructing them manually is often difficult or impossible. We propose Fast Feature Selection (FFS), a new method for automatically constructing good features in problems with high-dimensional state spaces but low-rank dynamics. Such problems are common when, for example, controlling simple dynamic systems using direct visual observations with states represented by raw images. FFS relies on domain samples and singular value decomposition to construct features that can be used to approximate the optimal value function well. Compared with earlier methods, such as LFD, FFS is simpler and enjoys better theoretical performance guarantees. Our experimental results show that our approach is also more stable, computes better solutions, and can be faster when compared with prior work

Efficient Data-Driven Robust Policies for Reinforcement Learning

Applying the reinforcement learning methodology to domains that involve risky decisions like medicine or robotics requires high confidence in the performance of a policy before its deployment. Markov Decision Processes (MDPs) have served as a well-established model in reinforcement learning (RL). An MDP model assumes that the exact transitional probabilities and rewards are available. However, in most cases, these parameters are unknown and are typically estimated from data, which are inherently prone to errors. Consequently, due to such statistical errors, the resulting computed policy\u27s actual performance is often different from the designer\u27s expectation. In this context, practitioners can either be negligent and ignore parameter uncertainty during decision-making or be pessimistic by planning to be protected against the worst-case scenario. This dissertation focuses on a moderate mindset that strikes a balance between the two contradicting points of view. This objective is also known as the percentile criterion and can be modeled as risk-aversion to epistemic uncertainty. We propose several RL algorithms that efficiently compute reliable policies with limited data that notably improve the policies\u27 performance and alleviate the computational complexity compared to standard risk-averse RL algorithms. Furthermore, we present a fast and robust feature selection method for linear value function approximation, a standard approach to solving reinforcement learning problems with large state spaces. Our experiments show that our technique is faster and more stable than alternative methods

Monte Carlo Localization in Hand-Drawn Maps

Robot localization is a one of the most important problems in robotics. Most of the existing approaches assume that the map of the environment is available beforehand and focus on accurate metrical localization. In this paper, we address the localization problem when the map of the environment is not present beforehand, and the robot relies on a hand-drawn map from a non-expert user. We addressed this problem by expressing the robot pose in the pixel coordinate and simultaneously estimate a local deformation of the hand-drawn map. Experiments show that we are able to localize the robot in the correct room with a robustness up to 80

WaterMet2 Toolkit functions

© TRUST 201

Oslo Case Study Report

© TRUST 2013The report presents the WaterMet2Oslo model, built based on the urban water system of Oslo which faces water scarcity problems for a 30-year planning horizon starting from year-2011. In order to cope with these challenges, 28 intervention strategies, each of which comprises either simple or complex intervention options are defined. They are examined and compared with each other in three stages against some quantitative criteria quantified by the WaterMet2 model. The quantitative criteria include water supply reliability, average annual leakage, total capital cost, average annual cost and average annual GHG emissions; and the qualitative criteria are health risks, social acceptance and company acceptance. All the intervention strategies are finally ranked by using the Compromising Programming MCDA method. Two types of rankings are performed including one with quantitative criteria only and the other one with both quantitative and qualitative criteria. The ranking of the results shows some potential and promising strategies. However they cannot be fully trusted currently for any real decision-making without further development and validation for multiple future scenarios and risk type criteria.European Union Seventh Framework Programme (FP7/2007-2013

WaterMet2: a tool for integrated analysis of sustainability-based performance of urban water systems

This paper presents the "WaterMet2" model for long-term assessment of urban water system (UWS) performance which will be used for strategic planning of the integrated UWS. WaterMet2 quantifies the principal water-related flows and other metabolism-based fluxes in the UWS such as materials, chemicals, energy and greenhouse gas emissions. The suggested model is demonstrated through sustainability-based assessment of an integrated real-life UWS for a daily time-step over a 30-year planning horizon. The integrated UWS modelled by WaterMet2 includes both water supply and wastewater systems. Given a rapid population growth, WaterMet2 calculates six quantitative sustainability-based indicators of the UWS. The result of the water supply reliability (94%) shows the need for appropriate intervention options over the planning horizon. Five intervention strategies are analysed in WaterMet2 and their quantified performance is compared with respect to the criteria. Multi-criteria decision analysis is then used to rank the intervention strategies based on different weights from the involved stakeholders' perspectives. The results demonstrate that the best and robust strategies are those which improve the performance of both water supply and wastewater systems

A hybrid Delphi-SWOT paradigm for oil and gas pipeline strategic planning in Caspian Sea basin

The Caspian Sea basin holds large quantities of both oil and natural gas that could help meet the increasing global demand for energy resources. Consequently, the oil and gas potential of the region has attracted the attention of the international oil and gas industry. The key to realizing the energy producing potential of the region is the development of transnational export routes to take oil and gas from the landlocked Caspian Sea basin to world markets. The evaluation and selection of alternative transnational export routes is a complex multi-criteria problem with conflicting objectives. The decision makers (DMs) are required to consider a vast amount of information concerning internal strengths and weaknesses of the alternative routes as well as external opportunities and threats to them. This paper presents a hybrid model that combines strength, weakness, opportunity and threat (SWOT) analysis with the Delphi metho

Sequential multi-objective evolutionary algorithm for a real-world water distribution system design

This paper presents a methodology based on a three-stage multi-objective optimization model for solving the problem of Battle of Background Leakage Assessment for Water Networks (BBLAWN) at WDSA2014 conference. At the first stage, the optimal design of pipeline rehabilitation, pump scheduling and tank sizing is formulated and solved on the skeletonized network by a optimizing (1) the costs of pipes, pumps and tank upgrading and (2) the cost of water losses and energy. Three optimal solutions are used for a second optimisation step on the full network (i.e. not skeletonised). The third optimisation step is then performed starting from second stage optimal solutions considering the three objectives of the original proble