Development of LID facilities Decision Support System ... · LID facilities Decision Support System using Multiple Attribute Decision Making(MADM)Method ... LID DSS TOOL BOX ... BMP

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Maine Stormwater Conference (Portland, ME, 2015)

LID DSS TOOL BOX: Facility Ver. 1.0

LID DSS TOOL BOX: Product Ver. 1.0

Development of LID facilities Decision Support System using Multiple Attribute Decision Making(MADM)Method

Lee kyoungdo, Park Jongpyo, Choi Jongsoo, Lee Jungmin, Hwang Soodeock

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• The purpose of this study is to help the decision makers choose the“best” and

the easiest LID facilities and products for reducing non-point pollution in their

communities

1. Purpose of the research

(BEST)

Reduction facilities for non-point

pollution

(BEST)

Reduction products for non-

point pollution

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• LID Decision Support Systems have developed by Web Based

• LID Decision Support Systems are composed of 2 parts both facilities and

products selection

• Both systems can link together the information & management system(NPS-

LID) for reduction facilities

2. Introduction: LID DSS TOOL BOX

LID DSS TOOLBOX

(Facilities)

LID DSS TOOLBOX

(Products)

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3. Introduction : Information management system(NPS-LID)

• Sharing information: Reduction facilities and Reduction products for non‐point

pollution

• Contents of Information management system

(Definition, Kinds of Facilities , Registration of products , Reference, etc)

• The number of registered products : 50

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Basin Area

faicility Area

LandUse

Rainfall

Rainfall-runoff management

Treatment efficiency

Maintenance

Basin characteristics

Variable for Decision Making

“Best” LID Facility

•Runoff Reduction

•Hydrologic Cycle Improvement

•Nonpoint Pollutant Removal

4. LID DSS TOOL BOX (Facilities)

LID DSS TOOL BOX (Facilities)

• To plan and choose reduction facilities for non‐point pollution 15 factors (basin characteristics,

rainfall‐runoff management, Treatment efficiency , maintenance, etc.) need to be considered

• Comparing evaluations and various decision‐making variables require technical knowledge

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Decision Making: AHP Logic

『Virginia's Stormwater Impact Evaluation(Virginia water resources research center, 2009) 』

• Analytic hierarchy process

• Algorithm capable of assisting complex decision-making problems

• To helps decision makers find one that best suits their goal and their understanding of the

problem

BMP Comparison Matrix

Retention 1 2 3 4 5 6 7 8 9 10 11 12 13 AverageStandard

scoreRank

Stormwater Pond(1) 1.000 1.000 1.000 2.250 9.000 1.000 1.500 1.000 2.250 1.500 1.500 1.500 1.000 1.962 0.103 1

Underground Storage Tank(2) 1.00 1.000 1.000 2.250 9.000 1.000 1.500 1.000 2.250 1.500 1.500 1.500 1.000 1.962 0.103 1

Constructed Wetland(Surface flow) (3) 1.00 1.00 1.000 2.250 9.000 1.000 1.500 1.000 2.250 1.500 1.500 1.500 1.000 1.962 0.103 1

Constructed Wetland(Subsurface flow) (4) 0.44 0.44 0.44 1.000 4.000 0.444 0.667 0.444 1.000 0.667 0.667 0.667 0.444 0.872 0.046 11

Porous Pavement(5) 0.11 0.11 0.11 0.25 1.000 0.111 0.167 0.111 0.250 0.167 0.167 0.167 0.111 0.218 0.011 13

Infiltration Basins(6) 1.00 1.00 1.00 2.25 9.00 1.000 1.500 1.000 2.250 1.500 1.500 1.500 1.000 1.962 0.103 1

Infiltration Trench(7) 0.67 0.67 0.67 1.50 6.00 0.67 1.000 0.667 1.500 1.000 1.000 1.000 0.667 1.308 0.069 7

Infiltration Tank(8) 1.00 1.00 1.00 2.25 9.00 1.00 1.50 1.000 2.250 1.500 1.500 1.500 1.000 1.962 0.103 1

Vegetated Filter Strip(9) 0.44 0.44 0.44 1.00 4.00 0.44 0.67 0.44 1.000 0.667 0.667 0.667 0.444 0.872 0.046 11

Vegetated Swale(10) 0.67 0.67 0.67 1.50 6.00 0.67 1.00 0.67 1.50 1.000 1.000 1.000 0.667 1.308 0.069 7

Bioretention(11) 0.67 0.67 0.67 1.50 6.00 0.67 1.00 0.67 1.50 1.00 1.000 1.000 0.667 1.308 0.069 7

Tree Box Filter(12) 0.67 0.67 0.67 1.50 6.00 0.67 1.00 0.67 1.50 1.00 1.00 1.000 0.667 1.308 0.069 7

Planter Box(13) 1.00 1.00 1.00 2.25 9.00 1.00 1.50 1.00 2.25 1.50 1.50 1.50 1.000 1.962 0.103 1

■ Comparison Matrices : Retention (example)


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• The LID Facility Decision Support System can calculate both space requirements of LID

facilities, construction cost and the ranking for the best choice.

■ Input data

• Basic input data

- Basin Area, Faicility Area, LandUse,

Design Rainfall

• Variable for decision making

- Basin characteristics

(Impermeable area rate, Soil type, etc)

- Rainfall-runoff management

(Detention, Percolation, Infiltration, etc)

- Treatment efficiency (BOD, TSS, TN, TP)

- Maintenance (Maintenance cycle, Manpower demand, etc)

Basic input data

Variable for Decision making

Results

LID DSS TOOL BOX (Facilities): UI

■ Output data

• Basic input data

- Space requirements

- Construction cost

- Ranking for best choice

RankSpace

requirements

Construction

cost

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Applicable Facilities

Natural type

Retention typeStormwater Pond

Underground Storage Tank

Constructed WetlandConstructed Wetland(Surface flow)

Constructed Wetland(SubSurface flow)

Infiltration type

Porous Pavement

Infiltration Basins

Infiltration Trench

Infiltration Tank

Vegetation type

Vegetated Filter Strip

Vegetated Swale

Bioretention

Tree Box Filter

Planter Box

Apparatus type

Filter

Continuous deflective separation

Downstream Defender


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■ Input data

5. LID DSS TOOL BOX (Products)

• Kinds of facility

- Natural type (Retention , Infiltration,

Vegetation, etc)

- Apparatus type (Filter, Continuous

deflective separation, Downstream

Defender)

• Weighting of the variables

- Treatment efficiency for pollutant

- Maintenance efficiency

- Construction ability

- Economic feasibility

• To help the decision makers to choose the “best” and the easiest reduction products for

non-point pollution

• Based on TOPSIS Logic

■ Output data

• Score of products

• Rank of products

LID DSS TOOL BOX (Products): Input & output

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LID DSS TOOL BOX (Products) : Detail results

• Prodcuct comparison : Score, Rank

• This system can be possibly linked to the information management system(NPS‐LID)

Selected results

Comparison of Prodcucts

5. LID DSS TOOL BOX (Products)

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6. Application in Goduk New-town in Korea

Introducion: Goduk New-town

• Goduk New-town is developed by Korea Land and Housing Corporation

• Goduk New-town is planning to apply reduction facilities for non-point pollution

• Area: 13.4㎢ (3,300 acres), Construction period: 2008-2020

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①

Land Use Parking lot

Basin Area 1,827㎡

Facility Area 100㎡

Soil type Group A

Impermeable area rate 100%

Depth of groundwater 4.2m

Basin slope 4.3%

Rainfall-runoff management Infiltration

Treatment efficiency TN, TP

Maintenance Maintenance cycle

Application in a parking lots

①

Rank 1 Porous Pavement

Rank 2 Infiltration Trench

Rank 3 Infiltration Tank

Rank 4 Vegetated Swale

■ Variables for Decision Making

■ Results

1


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Application in residential zone

②

Land Use Residential zone

Basin Area 20,946㎡

Facility Area 200㎡

Soil type Group A

Impermeable area rate 800%

Depth of groundwater 5.7m

Basin slope 9.0%

Rainfall-runoff management Retention

Treatment efficiency BOD

Maintenance -

②

Rank 1 Infiltration Basins

Rank 2 Infiltration Tank

Rank 3 Tree Box Filter

Rank 4 Bioretention

■ Variables for Decision Making

■ Results

2


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7. Conclusion

• We developed the LID Decision Support System including facilities and

products selection.

• We will help the decision makers to choose the "best" and the easiest

reduction facilities for non-point pollution.

• Also, We plan to apply those systems to other test-beds.

• We have to verify the system, continuously.

(BEST)

Reduction facilities for

non-point pollution

AHP Logic

UserTOPSIS Logic

■ LID DSS TOOL BOX

NPS‐LID

Database(Link)

(BEST)

Reduction products for

non-point pollution

Variable for

Decision Making

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Documents

Development of LID facilities Decision Support System ... · LID facilities Decision Support System using Multiple Attribute Decision Making(MADM)Method ... LID DSS TOOL BOX ... BMP