Reliability of Stormwater Best Management Practices in Washington DCMohammadreza Jabehdari1, Leila Mosleh2, Guangming Chen3, Ph.D.
1Doctoral candidate, 3Ph.D., Professor, Department of Industrial & System Engineering; Morgan State University2Ph.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 StudentDepartment of Industrial and System Engineering – Morgan State UniversityEmail: [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
ContractorConstructe 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
Nu
mb
ers
of
ove
rflo
w
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 1325 32
73
138
203
266
327
0
50
100
150
200
250
300
350
2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
Nu
mb
ers
of
ove
rflo
w
Year
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