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Modelling Spatio-Temporal Relationsh...

Yin, Lun.

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Modelling Spatio-Temporal Relationships With Deep Neural Networks to Estimate Coastal Water Levels.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modelling Spatio-Temporal Relationships With Deep Neural Networks to Estimate Coastal Water Levels./
Author:	Yin, Lun.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	239 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-12, Section: B.
Contained By:	Dissertations Abstracts International80-12B.
Subject:	Ocean engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13857808
ISBN:	9781392240243

Modelling Spatio-Temporal Relationships With Deep Neural Networks to Estimate Coastal Water Levels.
Yin, Lun.

Modelling Spatio-Temporal Relationships With Deep Neural Networks to Estimate Coastal Water Levels. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 239 p.

Source: Dissertations Abstracts International, Volume: 80-12, Section: B.

Thesis (Ph.D.)--Stevens Institute of Technology, 2019.

This item must not be added to any third party search indexes.

Coastal water level data are valuable in many monitoring, emergency management, forecast and research applications, yet observation gaps pose a challenge. This study uses multilayer perceptron and autoencoder-decoder models to learn the spatio-temporal relationships among water levels at 30 stations to estimate the missing water level data. The autoencoder approach is found to be the best to provide both accurate and stable estimations. With quality-controlled inputs, the autoencoder models achieve RMSEs ranging from 2.4 to 7.4 cm on out-of-sample data. The performances are substantially better than the results of Inverse Distance Weighting, which simply defines the spatial relationships as distance-based weights. Missing inputs, a critical issue left out of prior studies, are handled in this paper by the Designated Inverse Dropout method, which ignores the missing inputs and uses the remaining valid inputs to guarantee an output, and the symphony method, which replaces the missing inputs with model estimations at the other stations. With the symphony method of applying these models, the RMSEs are further reduced to between 2.2 and 6.5 cm, even outperforming the well-validated hydrodynamic model hindcasts from the Stevens Flood Advisory System which have RMSEs ranging from 4.2 to 11.3 cm. The resulting models have many applications beyond improving historical observations, including providing nowcast data to support real-time water surface mapping and data assimilation in operational hydrodynamic models, and establishing virtual stations to continue to provide water level data after a physical observation station is removed.

ISBN: 9781392240243Subjects--Topical Terms:

660731
Ocean engineering.

Modelling Spatio-Temporal Relationships With Deep Neural Networks to Estimate Coastal Water Levels.
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245 1 0 $a Modelling Spatio-Temporal Relationships With Deep Neural Networks to Estimate Coastal Water Levels.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 239 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-12, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Orton, Philip.
502 $a Thesis (Ph.D.)--Stevens Institute of Technology, 2019.
506 $a This item must not be added to any third party search indexes.
506 $a This item must not be sold to any third party vendors.
520 $a Coastal water level data are valuable in many monitoring, emergency management, forecast and research applications, yet observation gaps pose a challenge. This study uses multilayer perceptron and autoencoder-decoder models to learn the spatio-temporal relationships among water levels at 30 stations to estimate the missing water level data. The autoencoder approach is found to be the best to provide both accurate and stable estimations. With quality-controlled inputs, the autoencoder models achieve RMSEs ranging from 2.4 to 7.4 cm on out-of-sample data. The performances are substantially better than the results of Inverse Distance Weighting, which simply defines the spatial relationships as distance-based weights. Missing inputs, a critical issue left out of prior studies, are handled in this paper by the Designated Inverse Dropout method, which ignores the missing inputs and uses the remaining valid inputs to guarantee an output, and the symphony method, which replaces the missing inputs with model estimations at the other stations. With the symphony method of applying these models, the RMSEs are further reduced to between 2.2 and 6.5 cm, even outperforming the well-validated hydrodynamic model hindcasts from the Stevens Flood Advisory System which have RMSEs ranging from 4.2 to 11.3 cm. The resulting models have many applications beyond improving historical observations, including providing nowcast data to support real-time water surface mapping and data assimilation in operational hydrodynamic models, and establishing virtual stations to continue to provide water level data after a physical observation station is removed.
590 $a School code: 0733.
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793 $a English
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