Optimal Water Distribution System Management Using Esri Map Objects Technology

Optimal Water Distribution System Management Using ESRI MapObjects Technology By Werner de Schaetzen, Ph.D and Paul F. Boulos, Ph.D MWH Soft, Inc. 300 North Lake Avenue, Suite 1200, Pasadena, CA 91101 www.mwhsoft.com Abstract Geographic Information System (GIS) technology is quickly becoming a critical component to develop and sustain asset management for today's water utilities. Used as a spatial database, GIS can greatly assist in various modeling applications through the development of automated tools for constructing and maintaining reliable hydraulic network models of water distribution systems. This paper presents a comprehensive GISbased decision support system, called H2OMAP Utility Suite, for use in the effective planning and management of water distribution systems. It links an advanced hydraulic network simulator with geospatial technology and optimization theory to address every facet of network modeling and asset management. Built with ESRI MapObjects technology, the resulting software will effortlessly read GIS data, extract necessary modeling information, and automatically construct, skeletonize, load, calibrate, secure and optimize a representative network model. It also makes it easy to run and simulate various modeling conditions, identify optimal monitoring stations, locate system deficiencies, and determine the most cost-effective improvements for optimum performance. The optimization model uses an efficient variation of the genetic algorithm for solving network model calibration, field sampling design, pump scheduling, and network design and rehabilitation problems in an optimal fashion. The integrated approach offers a virtual geospatial environment to assist water industry executives and professionals in formulating, evaluating and prioritizing facility management and infrastructure security strategies. Introduction Computer simulation models of water distribution systems represent the most effective and viable means for evaluating system response to various management strategies. To be effective, these models require extensive spatial and hydraulic infrastructure data readily available from GIS. This information system format is unique in its ability to capture and store facility data and spatial reference for asset management and eventual hydraulic network model integration. It provides functions for development and preparation of pertinent spatial information for input to network models as well as functions to facilitate graphical output display for evaluating results. Added visualization tools can be applied using spatial and aspatial queries of model results to help identify correlation between input parameters and model results. Therefore, the integration of water distribution network models with GIS is a natural development of hydraulic simulation tools complemented with the evolution and maturation of relational database technology. While many powerful water distribution modeling software exist, few are currently integrated with a GIS platform. Engineers familiar with the (CAD) environment have

grown increasingly dependent on the hydraulic software's usability, interface, and CADlike features. Likewise, enterprise GIS software has not been tailored, both in terms of price and functionality, to serve the requirements of water distribution planning and analysis. As such, software for the analysis of water distribution systems was normally created in the CAD environment to receive acceptance from an engineering community already familiar with AutoCAD for other civil applications. No such software however, has been developed to integrate the water distribution modeler's needs with the data and spatial reference abilities of GIS. This paper presents a new perspective to water distribution planning and management. It is a unique network analysis platform that addresses the requirements of the modeling engineer while providing the functionality of a GIS for consistent facility asset management1-2. The graphical interface is developed using ESRI's MapObjects geospatial technology and provides an informative structured framework for database management and complete network model construction, analysis, and result presentation. Water utility engineers are responsible for ensuring the safe and efficient supply of drinking water. The role of a GIS in the analysis of a distribution system is to provide upto-date and accurate data to be used in the engineering analysis. For years, engineers have exported data from GIS data sets to third party software for analysis and design of water distribution systems. Applications and functionalities such as energy and fire flow analysis with the ability to model different demand and operating scenarios were not fully recognized in GIS software. Therefore, leaving the GIS environment was necessary to use this data with other advanced modeling and planning tools. While efforts to date have proven successful in allowing very basic engineering analysis from a GIS, engineers have been reluctant to embrace and utilize this technology. Part of the reason for this reluctance can be attributed to the way many GIS software treat data elements. For example, a water distribution system that serves 150,000 people may have a GIS that contains anywhere from 100,000 to 250,000 separate pipe segments. From an engineering perspective, this many pipe segments are not feasible for an accurate and manageable hydraulic model. Presuming that a GIS were able to successfully model that many elements, engineers will not want the task of evaluating the voluminous amount of data generated from such an extensive hydraulic simulation. Another reason for the hesitation to use a GIS software for network analysis lies in the difficult command structures of many GIS software packages. Engineers familiar with CAD based solutions may not find a GIS network modeling package as intuitive, and would thus resist a new format which may not appear to bring much additional functionality. Still another reason for this reluctance may be the speed of the analysis software. Engineers are used to working with time and budget constraints that will not include extra time to run a lengthy and time-consuming hydraulic simulation. One final and extremely practical reason for not choosing a GIS-hydraulic model package may be the cost of many GIS modeling solutions. Spending upwards of $15,000 to create and run a model that is less robust and has fewer features than a $6,000 solution may prove difficult to justify, especially for most users who are tied to a fixed budget.

It is for these reasons that engineers have opted to export the necessary data from a GIS into a third party software application designed specifically for the engineering analysis of a water distribution system. Therefore, the GIS exporting process has become an accepted task among engineers and has raised some common issues among practicing modelers. Duplication of Data One potential problem with exporting data outside of a GIS and into a third party software application for engineering analysis is the fact that a duplication of data may be created. Once this occurs it becomes very difficult to ensure integrity between the two data sets in the future (one data set being from the GIS, the other being from the hydraulic model). This occurs because engineering models take on a life of their own outside of a GIS3. Engineers spend hours tailoring the model to match existing field conditions. Pumps, pressure reducing valves, storage reservoirs, wells, closed pipes, etc. are all addressed by the engineer in the hydraulic modeling software that would otherwise be disregarded by the GIS manager. The engineer also goes to great lengths to ensure data integrity within the model for the purpose of running a hydraulic simulation. Pressure zone boundaries are delineated, demand area polygons are created and point loads are determined. All of these activities bring about the modification and adjustment of existing facilities as well as the creation of new facilities, further complicating the task of taking modeling data back to the GIS. Finding Errors While duplication of data is a potential risk when creating an engineering model from a GIS, a recognized advantage is finding errors in GIS graphic and non-graphic data sets. When GIS mapping began, the first step for a city/utility was to decide how to best go about creating the data and which department would oversee and supervise this process. Many cities had decided to outsource data creation and then assigned an internal Information Systems (IS) division to review the contract scope and continue data management upon project completion. Due to outsourcing, many cities received facility data sets that were not thoroughly inspected for data accuracy. The engineering and operations departments, not realizing the future impact of these outsourcing contracts, were not included in the data creation process. Because of the combination of outsourcing and IS oversight (not being aware of data needed by the engineer), preliminary data integrity of a hydraulic system which is built directly from the GIS may be questionable. While many cities believe they possess a highly accurate GIS, it is only until the engineering department, in collaboration with the operations department, utilizes GIS data for the sake of creating a water master plan that the GIS inaccuracies are discovered. It is at the time of a model creation for a system-wide master plan that issues regarding pipe diameters, materials, location, connectivity, etc. are thoroughly reviewed and inspected. What many find is that the GIS data set consists of errors that require further investigation and correction. Many master plans are delayed while GIS data discrepancies are adjusted to match field conditions.

While these errors may be addressed and resolved in the hydraulic model, little is done to ensure that all corrections are taken back to the GIS. In many instances, once a hydraulic model is created for a master plan, the model becomes more accurate than the original GIS data used in its creation. A solution to this problem can now be realized using an integrated geospatial network modeling methodology. A New Geospatial Solution The proposed geospatial software, H2OMAP Utility Suite, approaches water modeling from a GIS-centric point of view for spatial database management and analysis and works to avoid the duplication effort involved in the creation of a hydraulic network model. Built entirely with ESRI MapObjects technology, the software uses the Shapefile (an industry standard GIS format) as its native data format. Therefore, as pipes and nodes are created, these data elements are also stored externally to the program as Shapefiles, ready to be viewed and edited by any third party GIS application. This integrated approach introduces a completely new perspective to the application of GIS standards with network modeling. It is further developed to host in a unifying framework the variety of processes required for constructing, calibrating, and optimizing water distribution network models. The integrated system combines the ability to accurately build network topology, prepare requisite data, conceive and evaluate multiple scenarios, execute optimization runs, and provide both hardcopy reporting and graphical output display for evaluating and presenting results. The ability to make maximum use of all available data, from any department or source, allows the utility to manage their infrastructure systems more effectively. Since each department in an organization can control its own data while giving other departments easy access to its most current and accurate information, data is shared rather than duplicated and thus, saving time and money across the organization. The following example is provided to demonstrate the system's ability to reduce user errors and facilitate network model creation. Sample Project A city has decided to undertake a water system master plan. Using the city GIS water coverage, the GIS department assigns a unique value (or ID) to each water facility in the GIS. The data is then saved as a Shapefile and imported into the software. The engineer then works on the hydraulic model, assigning pump curves to pumps, settings to control valves, etc. In the process, the engineer establishes connectivity and assigns facilities to their appropriate pressure zone. The engineer proceeds to work on the hydraulic model, rectifying problem areas during model construction. Since the data is automatically stored as Shapefiles, the GIS manager views the modeling data in a GIS program to update changes made by the engineer. This GIS update occurs directly without an importing process, which would otherwise disrupt the modeler's task of building the hydraulic model. The GIS manager may also opt to replace existing facilities data stored in the GIS with those from the hydraulic model, as the model data is now more accurate and contain additional data values not stored in the GIS.

Because data sets are stored as Shapefiles, anyone in the organization can view the hydraulic model outside of the model. Pipes, valves, tanks, etc. can be added as views to ArcView or as layers in AutoCAD or Microstation. Pressure contours from a hydraulic analysis and annotation layers for labeling the hydraulic network are also stored as Shapefiles and can be viewed at any time in any software package that supports the ESRI Shapefile format. The functionality of the proposed software allows engineers to analyze distribution systems and GIS departments to integrate model data in a smooth and seamless fashion. Advanced Functionality The proposed integrated approach offers a full-featured hydraulic analysis software solution for performing a wide range of essential modeling tasks. Water utilities can use it for pump scheduling, developing various planning scenarios, analyzing system flows and pressures, performing water quality analyses, assessing fire flow capabilities, planning unidirectional flushing programs, creating pressure contours, and monitoring SCADA operations. The graphical interface is built with object-component technology to provide a powerful and practical GIS platform for water utility solutions. As a stand-alone program, it combines spatial analysis tools and mapping functions with sophisticated and accurate network analysis capabilities. GIS or CAD layers can be added to the program as background reference. A small sample of files which can be directly imported include ArcInfo coverages, Shapefiles, ArcSDE layers, Geodatabases, MIF/MID files, as well as AutoCAD and Microstation drawings (DXF, DWG, and DGN). The open-architecture framework allows fluent and flexible management and distribution of geospatial data while facilitating the exchange of critical modeling information with other applications and enterprise systems. Immediate data storage and access in the Shapefile format puts the model at the center of all enterprise solutions. Water utilities can develop informed GIS solutions to help them increase engineering productivity, exceed drinking water quality standards, optimize system operations and capital improvement programs, and improve community and client relations. Multi-level Inheritance Scenario Management A scenario manager allows modeling of multiple and varied demand loading and operating conditions while benefiting from a multi-leveled inheritance among planning scenarios. Every change made to a "parent" scenario can be reflected through the entire set of "child" scenarios for automatic acceptance of particular data sets. With minimal effort, the hydraulic modeler can simulate skeletonized systems, proposed facilities, and operational schemes to evaluate base transmission pipe combinations, system behavior under varying demands, and cost-saving operational procedures. Such capabilities allow the modeler to alternate between scenarios, merge models of any size, and compare results instantly to clearly illustrate optimal system performance under any given network scenario and planning horizon.

Automated Network Model Reduction (Skeletonization) Interfacing with GIS applications is a very reliable and efficient method of developing hydraulic network models. However, because GIS facilities are typically created for Automated Mapping/Facilities Management (AM/FM) applications (e.g., water distribution system maintenance and management), this format is generally not suitable for construction of hydraulic network models. Common data format problems encountered by practicing modelers who import to the network model are the inclusion of hydrants, line valves, tees or crosses from the GIS. A geospatial approach greatly simplifies and reduces large GIS models to a manageable size ready for hydraulic analysis. It expeditiously processes detailed GIS data, efficiently constructs reliable water system network models using three automated data segmentation applications: data reduction (Reduce), skeletonization (Skeletonize), and trimming (Trim) applications (or RST applications), and re-allocate nodal demands. Data reduction application is the ability to remove excessive pipe segmentation caused by valves, fire hydrants or other data capture processes, by dissolving interior nodes on pipe reaches and combining the associated pipe segments into single pipes. For example, merging all series pipes of common diameter, material, roughness coefficient and age. Data skeletonization application refers to the capability of removing all pipes with diameters less than a specified value (e.g., removing all 8 in and smaller pipes). The data trimming application is the ability to remove short pipe segments leading to dead ends such as service laterals and hydrant leads. These network segmentation capabilities can be used effectively to screen and accurately convert GIS data into a more practical and manageable hydraulic model. Demand Generation Determining consumption and the spatial distribution of consumption throughout the network model is a key element of modeling. Network models are loaded with existing and future demands, depending on the type of analysis performed. All sources, distribution pipelines and available storage within the system are supporting elements that provide service to meet these system demands. The variation of demand during the course of a day must also be accounted for during an extended period simulation (EPS). For static analyses, total system demand for various modeling conditions, such as average day, maximum day, peak hour, etc., is spatially distributed as a set of individual demand values allocated to selected junction nodes. For extended time period (EPS) analyses (e.g., water quality), additional temporal characteristics, typically represented by their respective diurnal variations (hydrographs), are also required. Generally, the spatial demand levels are first estimated for all junction nodes. The temporal effects are then adjusted based on individual consumption categories. Six accurate and fully automated methods can be used for processing geometric polygons to accurately compute and load network models based on demand type, location, and

variation. These are: 1. Geocoded meter billing data (meter consumption database) 2. Shortest distance to junction 3. Shortest distance to pipe 4. Polygon Processing - spatial intersection of multiple polygon layers 5. Polygon Processing - spatial summation of consumption category area polygons 6. Large users as individual point loads The first method makes use of GIS layers to automatically geocode consumption. The demand at each junction node is determined by identifying and summing all the customers/meters within its associated service area polygon. In the second method, each meter is assigned to the nearest junction while the third method assigns each meter to the nearest pipe. In the fourth method, demands are automatically calculated based upon a direct spatial intersection between demand categorization polygons (e.g., land use polygons, population polygons, pressure zone polygons, TAZ polygons, census tract polygons, meter route polygons, and others) and the demand node area coverage polygons (service area polygons). In the fifth method, nodal demands are calculated by summing the individually assigned consumption category polygons. In the last method, consumption levels for major users such as major industries, schools, parks, golf courses, hospitals, etc. are identified directly from their billing records and their demands are automatically assigned as individual point loads at their respective junction nodes. These comprehensive capabilities will allow water engineers to effectively utilize their engineering knowledge and experience and leverage existing GIS data investments to strategically define/forecast their network demand distribution for various planning horizons in their master planning effort. Automated Network Model Calibration After a network model is properly constructed, it must be calibrated to the physical system so that model predictions can be interpreted with confidence5-6. Calibration entails adjusting certain model parameters, usually the aggregate pipe roughness coefficients, until the model results coincide with observed field conditions. The field conditions most commonly used in network calibration correspond to the pressure readings obtained from fire flow tests or from on-line SCADA systems. The price for neglecting network calibration is basing decisions on a model that may be seriously in error. Typically, engineers will attempt to calibrate their network models using a tedious and inexact trial-and-error process in which the model input parameters (typically pipe roughness coefficients) are adjusted until computed and field observations are within reasonable agreement. However, since there is a vast number of possible combinations of parameter values that need to be considered, the trial-and-error evaluation of all options is unlikely to be practically feasible or manageable, and even knowledgeable modelers often fail to obtain good results. As a result, network model calibration has generally been

neglected or done haphazardly. In order to improve the reliability of hydraulic network models as well as eliminate the need for trial-and-error calibration methods, the network model calibration problem is cast as an optimization problem. It is then solved using an improved variation of the Genetic Algorithm optimization technology7-8 coupled with advanced elitist and global search control strategies to significantly enhance accuracy and convergence. The calibration model considers any combination of field pressure, tank level, flow and water quality measurements, quickly determining pipe status and roughness coefficient, pump and valve status, demand distribution and water quality parameters to best reflect what is actually occurring in the system. It offers comprehensive micro-level water distribution model calibration capabilities - considering any time frame of calibration condition (e.g., maximum hour), an unlimited number of calibration scenarios (e.g., time-disjoint fire flow test conditions), and complete extended period simulation calibrations (e.g., on-line SCADA readings). Calibration results are then stored in the database and accessed by the software's object-oriented interface for graphical reporting and display. Optimal Sampling Design The credibility and efficacy of a calibrated network model can only be as good as the data with which it was calibrated. While field sampling programs are essential for model calibration, poorly defined sampling designs may lead to calibrated roughness coefficients that are not reasonable. Since only a limited number of measurements are normally available due to resource constraints, the number and location of calibration tests must be chosen to provide maximum information on the condition of the system. If these locations are less than optimal, the data collected may yield insufficient information for an accurate calibration and, thus, would defeat the purpose of the calibration process. Sampling design is currently performed by subjective judgement, thus depending heavily on the experience of the modeler. In order to optimize the quality of sampling designs for model calibration, the sampling design problem is formulated as a dual-level combinatorial optimization problem and solved using Genetic Algorithms. The dualobjective function is to determine the minimum number of sampling locations (junction nodes) with pressures that are collectively the most sensitive to changes in pipe roughness values and also provide the most topological coverage of the network. This ensures identifying the minimum set of junction nodes which stresses the greatest percentage of the system and that are the most uniformly spread so that pipe roughness values can be accurately inferred. Considerable flexibility is allowed when screening sampling location results. Because the optimization algorithm searches the solution space from a population of points, and not just from a single point, alternative sampling locations of the same quality, can be used and compared if additional constraints are imposed, such as a location of an inaccessible or leaking hydrant. The optimal sampling sites are then stored in the database and conveyed back to the decision-support system interface for graphical query and display.

Network Design and Rehabilitation Cost-effective design and rehabilitation of water supply and distribution systems is a problem of great importance in engineering practice. Water utilities have been using network models to assist them in planning their system rehabilitation and designing new systems. New system design (or expansion to existing systems) is required to cope with sustained growth while rehabilitation (or upgrading) of an existing system is required to maintain adequate levels of service. The network design and rehabilitation problem is cast as an optimization problem and solved using Genetic Algorithms. It consists of determining the optimal rehabilitation alternatives and pipe sizes for selected pipes in the network that produce the minimum overall cost for a given set of demand loading and operating conditions while satisfying the hydraulic operational requirements of the system. The decision variables include any selected combination of rehabilitation and design options such as cleaning or cleaning and lining of existing pipes, pipe expansion, and/or installing new pipes that can either parallel or replace existing pipes. Cost data is specified for each option and for a range of pipe sizes. This data will vary with pipe material and geographical location. System operational constraints include minimum and maximum pressures at nodes, and minimum and maximum velocity and hydraulic gradient requirements for pipes. The results of an optimization run are then stored in the database and can be displayed in both tabular and dynamic color graphic forms. Water Security Planning and Vulnerability Assessment Ensuring the provision of acceptable levels of reliability in water distribution systems and reducing their vulnerability to natural disasters and emergencies is a problem of great importance for water utilities worldwide. In order to improve the safety and security of drinking water distribution systems, the software delivers an array of cutting edge water security planning and vulnerability assessment tools. It allows water utilities to model the propagation and concentration of naturally disseminated, accidentally released, or intentionally introduced contaminants and chemical constituents throughout water distribution systems; assess the effects of water treatment on the contaminant; and evaluate the potential impact of unforeseen facility breakdown (e.g., significant structural damage and/or operational disruption). It enables users to locate all areas affected by contamination; calculate population at risk and report customer notification information; and identify the appropriate valves to close to isolate a contamination event. Finally, it helps water utilities track contaminants to the originating source; compute required purging water volume; develop efficient flushing strategies; determine the resulting impact on fire-fighting capabilities; and prepare data for eventual prosecution. These capabilities will greatly assist water utilities in reducing their infrastructure vulnerability and enhancing their ability to prepare for and respond to natural disasters and emergencies. The software can be effectively used to identify viable solutions before

an incident or disaster occurs, or to assist in responding should it occur9. Pump Scheduling Energy costs generally constitute the largest expenditure for nearly all water utilities worldwide and can consume 65 percent of a water utility's annual operating budget. One of the greatest potential areas for energy cost-savings is the scheduling of daily pump operations. Energy-saving measures in water supply and distribution systems can be realized in many ways from field testing and proper maintenance of equipment to the use of optimal computer control. Energy usage can be reduced by decreasing the volume of water pumps (e.g., adjusting pressure zone boundaries), the head against which it is pumped (e.g., optimizing tank water level range), or the price of energy (e.g., avoid peak hour pumping and make effective use of storage tanks such as filling them during off-peak periods and draining them during peak periods), and increasing the efficiency of pumps (e.g., ensuring that pumps are operating near their best efficiency point). Water utilities can further reduce energy costs by implementing on-line telemetry and control systems (SCADA) and by managing their energy consumption more effectively using optimized scheduling of daily pump operations. The pump scheduling problem is cast as an optimization problem and solved using Genetic Algorithms. The optimization problem consists of determining the least-cost pump operation policy that will best meet target hydraulic performance requirements. The operation policy for a pump station represents a set of temporal rules or guidelines (pump operating times) that indicate when a particular pump or group of pumps should be turned on and off over a specified period of time (typically 24 hours). Th optimal operation policy is defined as that schedule of pump operations that will result in the lowest total operating cost for a given set of boundary conditions and system constraints. System constraints prescribe lower and upper limits on nodal pressures, pipe velocities, and storage tank levels, and final tank volumes at the end of a specified time period (normally 24 hours) to ensure hydraulic periodicity. The resulting optimal control rules for each pump in the system are then stored in the database and passed back to the decision-support system interface for graphical results presentation. Results Presentation The use of powerful GIS thematic mapping functionality makes it easy to turn dry database information into stunningly colorful, fully dimensional visualizations and to present analysis results in map form. Users can generate accurate, and smooth contours for any variable including elevation, pressure, hydraulic grade line, demand, water age, chlorine concentration, and more, directly on the map - even overlay multiple contours on a single drawing. Draw on a range of other sophisticated graphical presentation tools, including color-coded mapping, dynamic annotation/labeling, graphing, profiling, customizable tabular reporting, and vivid VCR-style animation to produce truly compelling results. These graphical capabilities are critical to better communicate and understand problem areas and system deficiencies and to present remedial engineering

solutions at community information sessions or council meetings. Conclusions Water utility engineers are discovering a wide variety of uses for GIS technology. In particular, GIS information is critical to water distribution system planning and analysis. A new geospatial software, H2OMAP Utility Suite, has been presented as a decision support system to provide a GIS-based solution for water distribution system modeling and management. Built entirely with ESRI MapObjects technology, the software system seamlessly integrates sophisticated GIS features and functionalities with a hydraulic network simulator and optimization theory allowing accurate network model construction and providing a reliable and effective means for decision makers to quickly assess and address the implications of alternative design, rehabilitation and operational changes on system performance. Through the exploitation of GIS graphical data visualization capabilities, potential system deficiencies can be quickly identified where improvements are required. The resulting GIS-based software allows a wide range of potential network improvement and enhancement alternatives to be modeled, analyzed, contrasted, and evaluated, providing water utility managers with the ability to readily optimize their capital improvement programs. It also equips water utilities with expanded power and flexibility in estimating the consequences of a terrorist attack or a crisis event on their drinking water supply infrastructure, and in formulating and evaluating sound emergency response, recovery, remediation and operations plans and security upgrades. Finally, the software makes it easy for any utility to work seamlessly across platforms and to manage water systems in a single environment. References • • •

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H2OMAP Water - Users Guide. MWH Soft, Inc. 300 North Lake Avenue, Suite 1200, Pasadena, CA 91101. H2OMAP Skeletonizer - Users Guide. MWH Soft, Inc, 300 North Lake Avenue, Suite 1200, Pasadena, CA 91101. Miles, S.B and Ho, C.L. (1999). "Applications and issues of GIS as a tool for civil engineering modeling." Journal of Computing in Civil Engineering ASCE, Vol. 13, No. 3, pp. 144-152. H2OMAP Demand Allocator - Users Guide. MWH Soft, Inc, 300 North Lake Avenue, Suite 1200, Pasadena, CA 91101. Boulos, P.F. and Ormsbee, L.E. (1991). "Explicit network calibration for multiple loading conditions." Journal of Civil Engineering Systems, Vol. 8, pp. 153-159. H2OMAP Calibrator - Users Guide. MWH Soft, Inc, 300 North Lake Avenue, Suite 1200, Pasadena, CA 91101. Boulos, P.F. et al. (2001). "Using genetic algorithms for water distribution system optimization." In Proceedings of the ASCE Environmental and Water Resources Institute's (EWRI's) World Water & Environmental Resource Congress, May 2024, Orlando, FL.

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Boulos, P.F. et al. (2001). "Optimal operation of water distribution systems using genetic algorithms." In Proceedings of the AWWA Distribution System Symposium, September 23-26, San Diego, CA. H2OMAP Protector - Users Guide. MWH Soft, Inc, 300 North Lake Avenue, Suite 1200, Pasadena, CA 91101.

Figure 1 - Graphical Results Presentation Environment

Figure 2 - Graphical Results Analysis Capabilities

Figure 3 - Scenario Management

Figure 4 - On-line SCADA Interface

Figure 5 - Geospatial Demand Allocation

Figure 6 - Genetic Algorithm Hydraulic Model Calibration

Figure 7 - Genetic Algorithm Water Quality Calibration

Figure 8 - Water Security and Vulnerability Assessment Tool