東華大學圖書館 |

A Framework for Designing and Optimizing Green Infrastructure Network Under Uncertainty.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A Framework for Designing and Optimizing Green Infrastructure Network Under Uncertainty./
作者:	Heidari Haratmeh, Bardia.
面頁冊數:	1 online resource (163 pages)
附註:	Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
Contained By:	Dissertations Abstracts International83-09B.
標題:	Civil engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29024253click for full text (PQDT)
ISBN:	9798780602491

A Framework for Designing and Optimizing Green Infrastructure Network Under Uncertainty.
Heidari Haratmeh, Bardia.

A Framework for Designing and Optimizing Green Infrastructure Network Under Uncertainty. - 1 online resource (163 pages)

Source: Dissertations Abstracts International, Volume: 83-09, Section: B.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2019.

Includes bibliographical references

Green infrastructure (GI) is becoming a common solution to mitigate stormwater-related problems. Despite wide acknowledgement of GI benefits, there is a lack of decision support tools that allow practitioners to interactively identify and evaluate the performance of small GI practices using hydrologic models under uncertainty. Also, the benefits and costs of GI practices are not fully understood when the analysis scale changes from a household to a subwatershed to an entire watershed. Moreover, recognition of optimal locations in a watershed, given the uncertainty in modelling parameters, is also another challenge for GI planning and design. To address these needs, an online Cloud-based interactive tool - called Interactive DEsign and Assessment System for Green Infrastructure (IDEAS_GI)- has been developed. This study demonstrates the application of the tool, using hydrologic and empirical models, to estimate life cycle cost, stormwater volume reduction and treatment, and air pollutant deposition. The tool was applied in two small watersheds in the Baltimore metropolitan area. The results show that GI properties do not significantly affect performance of individual GI practices during design storm events due to the intensity of the storms exceeding the capacity of GI practices to treat and capture stormwater. Using the tool to identify potential locations for GI placement, the study then provides a quantitative and comparative analysis of environmental benefits and economic costs of GI using two metrics [Benefit-Cost Ratios (BCRs) and nutrient removal costs] at household, subwatershed, and watershed scales. The results for a case study in Baltimore show that the unit cost of nutrient removal in some of the subwatersheds is lower than the unit costs at either the watershed or household scales, calling for optimization frameworks to determine the features that dictate optimality at the subwatershed level. Moreover, rain gardens provide far more efficient stormwater treatment at the household scale in comparison to watershed scale, for which large-scale dry or wet basins are more efficient. The results show that for BCR, smaller subwatersheds are more cost effective for GI implementation, while for nutrient removal cost, upstream subwatersheds are more suitable. Furthermore, self-installation of rain gardens greatly reduces nutrient removal costs. Finally, to identify preferable locations for GI implementation, the numerical hydrologic model used in IDEAS_GI, SWMM, has been merged with a probabilistic noisy genetic algorithm (GA). The GA uses a probabilistic selection method that requires numerous sampling realizations to estimate the uncertainties associated with the fitness (objective function) values, which are cumulative stormwater volume reduction and GI life cycle cost. To overcome the computational challenge and to identify significant features for preferable locations, the GA is merged with artificial neural networks, which act as surrogates for the numerical models. The surrogate models use GA-generated archives as training datasets to predict the mean and standard deviation of cumulative stormwater volume reduction. The results show that the addition of meta-models decreases average computational time required to reach Pareto frontiers similar to the ones generated by the noisy GA by more than 95%.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798780602491Subjects--Topical Terms:

860360
Civil engineering.
Subjects--Index Terms:

Green infrastructureIndex Terms--Genre/Form:

542853
Electronic books.

A Framework for Designing and Optimizing Green Infrastructure Network Under Uncertainty.
LDR:04806nmm a2200397K 4500 001 2358089
005 20230725094946.5
006 m o d
007 cr mn ---uuuuu
008 241011s2019 xx obm 000 0 eng d
020 $a 9798780602491
035 $a (MiAaPQ)AAI29024253
035 $a (MiAaPQ)httphdlhandlenet2142106151
035 $a AAI29024253
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Heidari Haratmeh, Bardia. $3 3698626
245 1 2 $a A Framework for Designing and Optimizing Green Infrastructure Network Under Uncertainty.
264 0 $c 2019
300 $a 1 online resource (163 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
500 $a Advisor: Minsker, Barbara; Schmidt, Arthur.
502 $a Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2019.
504 $a Includes bibliographical references
520 $a Green infrastructure (GI) is becoming a common solution to mitigate stormwater-related problems. Despite wide acknowledgement of GI benefits, there is a lack of decision support tools that allow practitioners to interactively identify and evaluate the performance of small GI practices using hydrologic models under uncertainty. Also, the benefits and costs of GI practices are not fully understood when the analysis scale changes from a household to a subwatershed to an entire watershed. Moreover, recognition of optimal locations in a watershed, given the uncertainty in modelling parameters, is also another challenge for GI planning and design. To address these needs, an online Cloud-based interactive tool - called Interactive DEsign and Assessment System for Green Infrastructure (IDEAS_GI)- has been developed. This study demonstrates the application of the tool, using hydrologic and empirical models, to estimate life cycle cost, stormwater volume reduction and treatment, and air pollutant deposition. The tool was applied in two small watersheds in the Baltimore metropolitan area. The results show that GI properties do not significantly affect performance of individual GI practices during design storm events due to the intensity of the storms exceeding the capacity of GI practices to treat and capture stormwater. Using the tool to identify potential locations for GI placement, the study then provides a quantitative and comparative analysis of environmental benefits and economic costs of GI using two metrics [Benefit-Cost Ratios (BCRs) and nutrient removal costs] at household, subwatershed, and watershed scales. The results for a case study in Baltimore show that the unit cost of nutrient removal in some of the subwatersheds is lower than the unit costs at either the watershed or household scales, calling for optimization frameworks to determine the features that dictate optimality at the subwatershed level. Moreover, rain gardens provide far more efficient stormwater treatment at the household scale in comparison to watershed scale, for which large-scale dry or wet basins are more efficient. The results show that for BCR, smaller subwatersheds are more cost effective for GI implementation, while for nutrient removal cost, upstream subwatersheds are more suitable. Furthermore, self-installation of rain gardens greatly reduces nutrient removal costs. Finally, to identify preferable locations for GI implementation, the numerical hydrologic model used in IDEAS_GI, SWMM, has been merged with a probabilistic noisy genetic algorithm (GA). The GA uses a probabilistic selection method that requires numerous sampling realizations to estimate the uncertainties associated with the fitness (objective function) values, which are cumulative stormwater volume reduction and GI life cycle cost. To overcome the computational challenge and to identify significant features for preferable locations, the GA is merged with artificial neural networks, which act as surrogates for the numerical models. The surrogate models use GA-generated archives as training datasets to predict the mean and standard deviation of cumulative stormwater volume reduction. The results show that the addition of meta-models decreases average computational time required to reach Pareto frontiers similar to the ones generated by the noisy GA by more than 95%.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Civil engineering. $3 860360
650 4 $a Hydrologic sciences. $3 3168407
650 4 $a Urban planning. $3 2122922
650 4 $a Water resources management. $3 794747
653 $a Green infrastructure
653 $a Urban hydrology
653 $a Stromwater management
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0543
690 $a 0388
690 $a 0999
690 $a 0595
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a University of Illinois at Urbana-Champaign. $b Civil & Environmental Eng. $3 2100706
773 0 $t Dissertations Abstracts International $g 83-09B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29024253 $z click for full text (PQDT)