Optimal and Objective Placement of Sensors in Water Distribution Systems Using Information Theory

Optimization-based deployment of contamination warning system in water distribution systems has been widely used in the literature, due to their superior performance compared to rule- and opinion-based approaches. However, optimization techniques impose an excessive computational burden, which in turn is compensated for by shrinking the problem’s decision space and/or using faster optimization algorithms with less accuracy. This imposes subjectivity in interpretation of the system and associated risks, and undermines model’s accuracy by not exploring the entire feasible space. We propose a framework that uses information theoretic techniques, including value of information and transinformation entropy, for optimal sensor placement. This can be used either as pre-selection, i.e. pinpointing best potential locations of sensors to be in turn used in optimization framework, or ultimate selection, i.e. single-handedly selecting sensor locations from the feasible space. The proposed framework is then applied to Lamerd water distribution system, in Fars province, Iran, and the results are compared to the suggested potential locations of sensors in previous studies and results of TEVA-SPOT model. The proposed information theoretic scheme enhances the decision space, provides more accurate results, significantly reduces the computational burden, and warrants objective selection of sensor placement

A Comprehensive Bibliometric Analysis on Social Network Anonymization: Current Approaches and Future Directions

In recent decades, social network anonymization has become a crucial research field due to its pivotal role in preserving users' privacy. However, the high diversity of approaches introduced in relevant studies poses a challenge to gaining a profound understanding of the field. In response to this, the current study presents an exhaustive and well-structured bibliometric analysis of the social network anonymization field. To begin our research, related studies from the period of 2007-2022 were collected from the Scopus Database then pre-processed. Following this, the VOSviewer was used to visualize the network of authors' keywords. Subsequently, extensive statistical and network analyses were performed to identify the most prominent keywords and trending topics. Additionally, the application of co-word analysis through SciMAT and the Alluvial diagram allowed us to explore the themes of social network anonymization and scrutinize their evolution over time. These analyses culminated in an innovative taxonomy of the existing approaches and anticipation of potential trends in this domain. To the best of our knowledge, this is the first bibliometric analysis in the social network anonymization field, which offers a deeper understanding of the current state and an insightful roadmap for future research in this domain.Comment: 73 pages, 28 figure

A Robust Decision Support Leader-Follower Framework for Design of Contamination Warning System in Water Distribution Network

In recent years, several models have been proposed to inoculate Water Distribution Systems (WDS) against impacts of accidental and/or intentional compromised water quality through optimal deployment of online monitoring sensors in the network, which is referred to as Contamination Warning Systems (CWS). Translating such modeling efforts to real-world practice is, however, a challenge as different involved parties may pursue conflicting goals and modeling-based recommendations may not justify all stakeholders’ criteria. It is, hence, pivotal to develop conflict resolution methodologies to support engagement of different stakeholders in securing a safe water distribution. The decision making structure for CWS design is often of top-down nature, with the upper level decision maker concerned mainly about public safety and lower level stakeholders concerned about operational costs. In this study, a decision support framework based on Leader-Follower Game is proposed, given different power levels. Leader’s objectives are focused on the CWS robustness, while followers have conflicting interests that are in turn resolved via Nash Bargaining method. Lamerd WDS (Fars, Iran) is selected to assess the proposed model’s performance. The results show the proposed objective and parsimonious model provides a robust solution that complies with the leader’s criteria and maximizes the followers’ satisfaction. The proposed decision support system helps govern WDSs in a resilient and safe manner and warrants practical implementation of modeling-based security assurance policies to provide sustainable service to the society

A Multi-Objective Risk-Based Game Theoretic Approach to Reservoir Operation Policy in Potential Future Drought Condition

In this paper, by using the concept of Conditional Value at Risk (CVaR), a Leader-Follower game (LFG) based multi-objective optimization model is developed to determine the optimum 12-month operation policy of a reservoir in potential future dry periods. The minimization of CVaRs of storage loss and agricultural and environmental deficits along with maximization of planned allocation to agricultural sector are considered as leader’s objectives, while the followers try to maximize their share of water rights using Nash bargaining (NB) method. This framework is then used to model the operation policy of Dorudzan basin in Fars province, southwestern Iran. Water demand and daily climate data in the period of 2003 to 2015 for this basin, as well as future projections from fifteen IPCC-AR4 global circulation models (GCMs) for 2018–2030 under A2, B1 and A1B emission scenarios are considered to evaluate future dam operation policies. Future projections are downscaled using the LARS-WG model, which then feeds the HMETS watershed model to simulate the corresponding reservoir inflow time-series. Thereafter, three-hundred 12-month rainfall, evaporation and inflow time series with least inflow volume are used as input for the optimization model, which is solved using NSGA-II and GA algorithms. The results show while the model can determine the operation policy that keeps the associated risks in the acceptable range, it can satisfy the followers demands with respect to the available resources. The results also show that the agricultural sector of the study area can be hugely affected by potential future droughts

Increasing Concurrence of Wildfire Drivers Tripled Megafire Critical Danger Days in Southern California Between 1982 and 2018

Wildfire danger is often ascribed to increased temperature, decreased humidity, drier fuels, or higher wind speed. However, the concurrence of drivers—defined as climate, meteorological and biophysical factors that enable fire growth—is rarely tested for commonly used fire danger indices or climate change studies. Treating causal factors as independent additive influences can lead to inaccurate inferences about shifting hazards if the factors interact as a series of switches that collectively modulate fire growth. As evidence, we show that in Southern California very large fires and \u27megafires\u27 are more strongly associated with multiple drivers exceeding moderate thresholds concurrently, rather than direct relationships with extreme magnitudes of individual drivers or additive combinations of those drivers. Days with concurrent fire drivers exceeding thresholds have increased more rapidly over the past four decades than individual drivers, leading to a tripling of annual \u27megafire critical danger days\u27. Assessments of changing wildfire risks should explicitly address concurrence of fire drivers to provide a more precise assessment of this hazard in the face of a changing climate

Pressure Sensor Placement in Water Distribution Networks for Leak Detection Using a Hybrid Information-Entropy Approach

This study proposes an optimization framework based on a hybrid information-entropy approach to identify leakage events in water distribution networks (WDN). Optimization-based methods are widely employed in the literature for such purposes; however, they are constrained by time-consuming procedures. Hence, researchers eliminate parts of the decision space to curtail the computational burden. Here, we propose an information theory-based approach, using Value of Information (VOI) and Transinformation Entropy (TE) methods, in conjunction with an optimization model to explore the entire decision space. VOI allows for the entire feasible space search through intelligent sampling, which in turn ensures robust solutions. TE minimizes redundant information and helps maximize the spatial distribution of sensors. The herein proposed model is developed within a multi-objective optimization framework that renders a set of Pareto-optimal solutions. ELimination and Choice Expressing the REality (ELECTRE) multi-criteria decision-making model is then used to select the best compromise solution given several weighting scenarios. The results of this study show that the information-entropy based scheme can improve the precision of leak detection by enhancing the decision space, and can reduce the computational burden