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Direct Policy Search for Adaptive Management of Flood Risk.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Direct Policy Search for Adaptive Management of Flood Risk./
作者:	Wang, Jingya.
面頁冊數:	1 online resource (133 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
Contained By:	Dissertations Abstracts International85-01B.
標題:	Tidal waves. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30540029click for full text (PQDT)
ISBN:	9798379874049

Direct Policy Search for Adaptive Management of Flood Risk.
Wang, Jingya.

Direct Policy Search for Adaptive Management of Flood Risk. - 1 online resource (133 pages)

Source: Dissertations Abstracts International, Volume: 85-01, Section: B.

Thesis (Ph.D.)--Purdue University, 2023.

Includes bibliographical references

Direct policy search (DPS) has been shown to be an efficient method for identifying optimal rules (i.e., policies) for adapting a system in response to changing conditions. This dissertation describes three major advances in the usage of DPS for long-range infrastructure planning, using a specific application domain of flood risk management. We first introduce a new adaptive way to incorporate learning into DPS. The standard approach identifies policies by optimizing their average performance over a large ensemble of future states of the world (SOW). Our approach exploits information gained over time, regarding what kind of SOW is being experienced, to further improve performance via adaptive meta-policies defining how control of the system should switch between policies identified by a standard DPS approach (but trained on different SOWs). We outline the general method and illustrate it using a case study of optimal dike heightening extending the work of Garner and Keller (2018). The meta-policies identified by the adaptive algorithm show Pareto-dominance in two objectives over the standard DPS, with an overall 68% improvement in hypervolume. We also see the improved performance over three grouped SOWs based on future extreme water levels, with the hypervolume improvements of 90%, 46%, and 35% for low, medium, and high water level SOWs respectively. Additionally, we evaluate the degree of improvement achieved by different ways of implementing the algorithm (i.e., different hyperparameter values). This provides guidance for decision makers with different degrees of risk aversion, and computational budgets.Due to simplifying assumptions and limitations of the adaptive DPS model used in the chapter, such as uniform levee design heights, the Surge and Waves Model for Protection Systems (SWaMPS) is presented as a more realistic application of the DPS framework. SWaMPS is a process-based model of surge-based flood risk. This chapter marks the first implementation of DPS using a realistic process-based risk model. The physical process of storm surge and rainfall is simulated independently over multiple reaches, and different frequencies are explored to manage the production system in SWaMPS. The performance of the DPS algorithm is evaluated versus a static intertemporal optimization.The computational burden of evaluating the large ensemble of SOWs to include possible future events in DPS motivates us to apply scenario reduction methods to select representative scenarios that more efficiently span an uncertain parameter space. This allows us to reduce the runtime of the optimization process. We explore a range of data-mining tools, including principal component analysis (PCA) and clustering to reduce the scenarios. We compare the computational efficiency and quality of policies to this optimization problem with reduced ensembles of SOWs.

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

Mode of access: World Wide Web

ISBN: 9798379874049Subjects--Topical Terms:

3685270
Tidal waves.
Index Terms--Genre/Form:

542853
Electronic books.

Direct Policy Search for Adaptive Management of Flood Risk.
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245 1 0 $a Direct Policy Search for Adaptive Management of Flood Risk.
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500 $a Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
500 $a Advisor: Johnson, David.
502 $a Thesis (Ph.D.)--Purdue University, 2023.
504 $a Includes bibliographical references
520 $a Direct policy search (DPS) has been shown to be an efficient method for identifying optimal rules (i.e., policies) for adapting a system in response to changing conditions. This dissertation describes three major advances in the usage of DPS for long-range infrastructure planning, using a specific application domain of flood risk management. We first introduce a new adaptive way to incorporate learning into DPS. The standard approach identifies policies by optimizing their average performance over a large ensemble of future states of the world (SOW). Our approach exploits information gained over time, regarding what kind of SOW is being experienced, to further improve performance via adaptive meta-policies defining how control of the system should switch between policies identified by a standard DPS approach (but trained on different SOWs). We outline the general method and illustrate it using a case study of optimal dike heightening extending the work of Garner and Keller (2018). The meta-policies identified by the adaptive algorithm show Pareto-dominance in two objectives over the standard DPS, with an overall 68% improvement in hypervolume. We also see the improved performance over three grouped SOWs based on future extreme water levels, with the hypervolume improvements of 90%, 46%, and 35% for low, medium, and high water level SOWs respectively. Additionally, we evaluate the degree of improvement achieved by different ways of implementing the algorithm (i.e., different hyperparameter values). This provides guidance for decision makers with different degrees of risk aversion, and computational budgets.Due to simplifying assumptions and limitations of the adaptive DPS model used in the chapter, such as uniform levee design heights, the Surge and Waves Model for Protection Systems (SWaMPS) is presented as a more realistic application of the DPS framework. SWaMPS is a process-based model of surge-based flood risk. This chapter marks the first implementation of DPS using a realistic process-based risk model. The physical process of storm surge and rainfall is simulated independently over multiple reaches, and different frequencies are explored to manage the production system in SWaMPS. The performance of the DPS algorithm is evaluated versus a static intertemporal optimization.The computational burden of evaluating the large ensemble of SOWs to include possible future events in DPS motivates us to apply scenario reduction methods to select representative scenarios that more efficiently span an uncertain parameter space. This allows us to reduce the runtime of the optimization process. We explore a range of data-mining tools, including principal component analysis (PCA) and clustering to reduce the scenarios. We compare the computational efficiency and quality of policies to this optimization problem with reduced ensembles of SOWs.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Tidal waves. $3 3685270
650 4 $a Floods. $3 549458
650 4 $a Flood damage. $3 3319040
650 4 $a Levees & battures. $3 3702592
650 4 $a Performance evaluation. $3 3562292
650 4 $a Probability distribution. $3 3562293
650 4 $a Climate change. $2 bicssc $3 2079509
650 4 $a Monte Carlo simulation. $3 3564916
650 4 $a Planning. $3 552734
650 4 $a Genetic algorithms. $3 533907
650 4 $a Decision making. $3 517204
650 4 $a Design. $3 518875
650 4 $a Storm damage. $3 3685264
650 4 $a Rain. $3 3560372
650 4 $a Meteorology. $3 542822
650 4 $a Physical oceanography. $3 3168433
650 4 $a Statistics. $3 517247
655 7 $a Electronic books. $2 lcsh $3 542853
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690 $a 0404
690 $a 0800
690 $a 0501
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710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a Purdue University. $3 1017663
773 0 $t Dissertations Abstracts International $g 85-01B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30540029 $z click for full text (PQDT)