{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/106151"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/106151","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A framework for designing and optimizing green infrastructure network under uncertainty","abstract":"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%.","abstract_html":"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%.","abstract_has_math":false,"creators":["Heidari Haratmeh, Bardia"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Minsker, Barbara","Schmidt, Arthur","Brunner, Robert","Uster, Halit"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-03-02T21:57:56Z","date_published":"2020-03-02T21:57:56Z","updated_at":"2026-07-22T22:24:45Z","subjects":["Green infrastructure","urban hydrology","SWMM","Stromwater management"],"languages":["en"],"rights":["Copyright 2019 Bardia Heidari Haratmeh"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/106151","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Minsker, Barbara","Schmidt, Arthur","Brunner, Robert","Uster, Halit"]},{"key":"dc:creator","label":"Author","values":["Heidari Haratmeh, Bardia"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-03-02T21:57:56Z","2019-09-12","2019-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Green infrastructure","urban hydrology","SWMM","Stromwater management"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Bardia Heidari Haratmeh"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/106151"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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%.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-02-28 without embargo terms","The student, Bardia Heidari Haratmeh, accepted the attached license on 2019-09-10 at 00:05.","The student, Bardia Heidari Haratmeh, submitted this Dissertation for approval on 2019-09-10 at 00:09.","This Dissertation was approved for publication on 2019-09-12 at 14:22.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14447 on 2020-02-28 at 17:11:28","Made available in DSpace on 2020-03-02T21:57:56Z (GMT). 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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%.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-02-28 without embargo terms","The student, Bardia Heidari Haratmeh, accepted the attached license on 2019-09-10 at 00:05.","The student, Bardia Heidari Haratmeh, submitted this Dissertation for approval on 2019-09-10 at 00:09.","This Dissertation was approved for publication on 2019-09-12 at 14:22.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14447 on 2020-02-28 at 17:11:28","Made available in DSpace on 2020-03-02T21:57:56Z (GMT). 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