Reliability of Stormwater Best Management Practices in Washington DC Mohammadreza Jabehdari 1 , Leila Mosleh 2 , Guangming Chen 3 , Ph.D. 1 Doctoral candidate, 3 Ph.D., Professor, Department of Industrial & System Engineering; Morgan State University 2 Ph.D.Candidate, Department of Environmental Science and Technology; University of Maryland Background Research Objective & Methodology Object-Oriented Model . Methodology – System Analysis Industrial and System Engineering Mohammadreza Jabehdari, Doctoral Student Department of Industrial and System Engineering – Morgan State University Email: [email protected] Results and Conclusion Result - Reliability Combined Sewer Overflow (CSO): To avoid flood, regulators are designed to let the excess flow, which is a mixture of stormwater and sanitary wastes, be discharged directly into the rivers and creeks. Sanitary Sewer Overflow (SSO): untreated sewage is discharged from a sanitary sewer into the environment prior to reaching sewage treatment facilities. ✓To determine the reliability of current stormwater BMPs system. ✓Analyze the system to evaluate possibilities and improve reliability of system- Moving toward resiliency Objective Methodology - Reliability Reliability is defined to be the probability that a component or system will perform a required function for a given period of time when used under stated operating conditions. Goodness of Fit The Anderson–Darling test is a statistical test of whether a given sample of data is drawn from a given probability distribution Result of Goodness of Fit Test Problem Description Conceptual Model Use Case Model Analysis Model ( OOAD ) Design Model Implementation Model Test Model System Evaluation JAD Session System Engineer Users Stakeholders Use Case Diagram Continuous User Involvement Iterate on Evaluation Iterate on Analysis Artifacts of Analysis High-Level System Diagram Artifact Diagrams Used Throughout Solution Process Problem Definition Problem Definition: The reliability of system is relatively low. The current BMPs cannot prevent overflows. (SSO&CSO) Conceptual Model object Capture the stormwater Strategically design network of BMPs BMPs document:Document - Requeirments :char - Capacity required plan - Type of BMPs + clarify the requierments() :char + support call for policy () :char Contractor 1: Contractor - Facilities :char - Equipment :char + Biuld the BMPs() :char + Adapt the sewer drains () :char Network of BMPs - linked by their function :char - Capacity- gallon :int - Type :char - Size-area :int + Capture stormwater efficeintly() + Avoid flood() + Provide water to be used localy() + Provide green space () + Provide ecosystem services() Researcher 1 : Researcher - Knowledge - Director + Manage other researchers() + Collecte data() :char + Design() :char + Analyze() :char DC :City - Border :char - Stormwater :char - Combined Sewer system :char - Population :char - Sewage :char + Ruling the city () :char + Build public places() :char Stormwater manager: Manager - Responsibility for capturing the stormwater :char + Managing () :char + Policy making () :char + Contractor selection () :char Stormwater of scope: Stormwater - Valume :int + Makes CSO() :char + Makes SSO() :char + Damage city () :char Researcher 2 : Researcher - Knows models + Collecte data() :char + Simulate runoff() :char + Analyze() :char Make problems Gives misson BMPs documents «flow» Evaluates Contracts Asks for a solution Prepars Strategically design Documents «flow» Captures Builds Documents «flow» uc Primary Use Cases Stormwater BMP Determine appropriate BMP based on the local potential Researcher Strategically design network of BMPs Manager Contractor Constructe the BMPs Simulate the amount of runoff for each nod City «include» «include» Object-Oriented Model – Network of BMPs Hirschman et al. 2008. Simulated Stormwater runoff on small scale Estimated of runoff reduction by type of BMPs. Design capacity and size for network of BMPs. Numbers of SSO by the years 6 10 13 25 32 73 0 10 20 30 40 50 60 70 80 2013 2014 2015 2016 2017 2018 Numbers of overflow Year Distribution AD P LRT P Normal 0.520 0.108 Box-Cox Transformation 0.159 0.902 Lognormal 0.159 0.902 3-Parameter Lognormal 0.358 * 1.000 Exponential 0.265 0.838 2-Parameter Exponential 0.200 >0.250 0.345 Weibull 0.252 >0.250 3-Parameter Weibull 0.273 >0.500 1.000 Smallest Extreme Value 0.708 0.048 Largest Extreme Value 0.370 >0.250 Gamma 0.254 >0.250 3-Parameter Gamma 0.371 * 1.000 Logistic 0.448 0.205 Loglogistic 0.181 >0.250 3-Parameter Loglogistic 0.217 * 1.000 6 10 13 25 32 73 138 203 266 327 0 50 100 150 200 250 300 350 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Numbers of overflow Year