The use of artificial neural networks to retrieve sea-level information from remote data sources
The knowledge of near-shore sea-level variations is of great importance in applications such as ocean engineering and safe navigation. It also plays an essential role in the practical realisation of the height reference surface in geodesy. In the cases of gaps in tide-gauge records, estimates can be...
Main Authors: | , , , |
---|---|
Format: | Conference Object |
Language: | unknown |
Published: |
2004
|
Subjects: | |
Online Access: | https://hdl.handle.net/20.500.11937/45993 |
id |
ftcurtin:oai:espace.curtin.edu.au:20.500.11937/45993 |
---|---|
record_format |
openpolar |
spelling |
ftcurtin:oai:espace.curtin.edu.au:20.500.11937/45993 2023-06-11T04:17:01+02:00 The use of artificial neural networks to retrieve sea-level information from remote data sources Makarynskyy, Oleg Kuhn, Michael Makarynska, D. Featherstone, Will 2004 fulltext https://hdl.handle.net/20.500.11937/45993 unknown http://www.springer.com/east/home/geosciences/geophysics?SGWID=5-10008-22-52142815-detailsPage=ppmmedia%7Ctoc http://hdl.handle.net/20.500.11937/45993 Sea level validation Western Australia simulation artificial neural network Conference Paper 2004 ftcurtin https://doi.org/20.500.11937/45993 2023-05-30T19:44:25Z The knowledge of near-shore sea-level variations is of great importance in applications such as ocean engineering and safe navigation. It also plays an essential role in the practical realisation of the height reference surface in geodesy. In the cases of gaps in tide-gauge records, estimates can be obtained by various methods of interpolation and/or extrapolation, which generally assume linearity of the data. Although plausible in many cases, this assumption does not provide accurate results because shallow-water oceanic processes, such as tides, are mostly of a non-linear nature. This paper employs artificial neural networks to supplement hourly tide-gauge records using observations from other distant tide gauges. A case study is presented using data from the SEAFRAME tide-gauge sta-tions at Hillarys Boat Harbour, Indian Ocean, and Esperance, Southern Ocean, for the period 1992 to 2002. The neural network methodology of sea-level supplementation demonstrates reliable results, with a fairly good overall agreement between the retrieved information and actual measurements. Conference Object Southern Ocean Curtin University: espace Boat Harbour ENVELOPE(69.433,69.433,-49.633,-49.633) Indian Southern Ocean |
institution |
Open Polar |
collection |
Curtin University: espace |
op_collection_id |
ftcurtin |
language |
unknown |
topic |
Sea level validation Western Australia simulation artificial neural network |
spellingShingle |
Sea level validation Western Australia simulation artificial neural network Makarynskyy, Oleg Kuhn, Michael Makarynska, D. Featherstone, Will The use of artificial neural networks to retrieve sea-level information from remote data sources |
topic_facet |
Sea level validation Western Australia simulation artificial neural network |
description |
The knowledge of near-shore sea-level variations is of great importance in applications such as ocean engineering and safe navigation. It also plays an essential role in the practical realisation of the height reference surface in geodesy. In the cases of gaps in tide-gauge records, estimates can be obtained by various methods of interpolation and/or extrapolation, which generally assume linearity of the data. Although plausible in many cases, this assumption does not provide accurate results because shallow-water oceanic processes, such as tides, are mostly of a non-linear nature. This paper employs artificial neural networks to supplement hourly tide-gauge records using observations from other distant tide gauges. A case study is presented using data from the SEAFRAME tide-gauge sta-tions at Hillarys Boat Harbour, Indian Ocean, and Esperance, Southern Ocean, for the period 1992 to 2002. The neural network methodology of sea-level supplementation demonstrates reliable results, with a fairly good overall agreement between the retrieved information and actual measurements. |
format |
Conference Object |
author |
Makarynskyy, Oleg Kuhn, Michael Makarynska, D. Featherstone, Will |
author_facet |
Makarynskyy, Oleg Kuhn, Michael Makarynska, D. Featherstone, Will |
author_sort |
Makarynskyy, Oleg |
title |
The use of artificial neural networks to retrieve sea-level information from remote data sources |
title_short |
The use of artificial neural networks to retrieve sea-level information from remote data sources |
title_full |
The use of artificial neural networks to retrieve sea-level information from remote data sources |
title_fullStr |
The use of artificial neural networks to retrieve sea-level information from remote data sources |
title_full_unstemmed |
The use of artificial neural networks to retrieve sea-level information from remote data sources |
title_sort |
use of artificial neural networks to retrieve sea-level information from remote data sources |
publishDate |
2004 |
url |
https://hdl.handle.net/20.500.11937/45993 |
long_lat |
ENVELOPE(69.433,69.433,-49.633,-49.633) |
geographic |
Boat Harbour Indian Southern Ocean |
geographic_facet |
Boat Harbour Indian Southern Ocean |
genre |
Southern Ocean |
genre_facet |
Southern Ocean |
op_relation |
http://www.springer.com/east/home/geosciences/geophysics?SGWID=5-10008-22-52142815-detailsPage=ppmmedia%7Ctoc http://hdl.handle.net/20.500.11937/45993 |
op_doi |
https://doi.org/20.500.11937/45993 |
_version_ |
1768375791662399488 |