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Pierdomenico Duttilo, Francesco Lisi
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Appendix to Circular-Letter 4-H: The Harmonic Development of the Tide-Generating Potential
1954
On the Variability of Pacific Ocean Tides at Seasonal to Decadal Time Scales: Observed vs. Modelled
2016
A Review of Modelling Approaches on Tidal Analysis and Prediction
10.5194/isprs-archives-xlii-4-w16-23-2019 · 2019
Sea-level Rise Will Change Estuarine Tidal Energy: A Review
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Integrated Modelling of Sea-State Forecasts for Safe Navigation and Operational Management in Ports: Application in the Mediterranean Sea
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Assessing Climate Change Impacts, sea-level Rise and Storm Surge Risk in Port Cities: A Case Study on Copenhagen
10.1007/s10584-010-9978-3 · 2011
Daily sea-level Prediction at Chiayi Coast, Taiwan Using Extreme Learning Machine and Relevance Vector Machine
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Physics and data dual-driven deep learning model for tide level forecasting
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Adaptation of Classical Tidal Harmonic Analysis to Nonstationary Tides, with Application to River Tides
10.1175/jtech-d-12-00016.1 · 2013
The Effect of non linear Factors on Tide-Surge Interaction: A Case Study of Typhoon Rammasun in Tieshan Bay, China
10.1016/j.ecss.2019.01.024 · 2019
Physical Processes That Impact the Evolution of Global Mean sea-level in Ocean Climate Models
10.1016/j.ocemod.2012.04.003 · 2012
Accuracy Assessment of Global Barotropic Ocean Tide Models
10.1002/2014rg000450 · 2014
Review of machine learning methods for sea-level change modeling and prediction
10.1016/j.scitotenv.2024.176410 · 2024
10.1109/icoict49345.2020.9166423
10.1109/icoict49345.2020.9166423
Application of Auto-Regressive (AR) Analysis to Improve Short-Term Prediction of Water Levels in the Yangtze Estuary
10.1016/j.jhydrol.2020.125386 · 2020
Improved Method of sea-level Forecasting at Venice (Northern Adriatic Sea)
10.1016/j.cnsns.2004.11.008 · 2006
Short- and long-term tidal level forecasting: A novel hybrid TCN + LSTM framework
10.1016/j.seares.2025.102577 · 2025
Hourly-scale coastal sea-level modeling in a changing climate using long short-term memory neural network
10.1016/j.scitotenv.2020.137613 · 2020
A multi-model architecture based on Long Short-Term Memory neural networks for multi-step sea-level forecasting
10.1016/j.future.2021.05.008 · 2021
10.3390/w13091173
10.3390/w13091173
The prediction of floods in Venice: Methods, models and uncertainty (review article)
10.5194/nhess-21-2679-2021 · 2021
Unresolved referenced work
Kept as external metadata until matched
Boon and burden: Economic performance and future perspectives of the Venice flood protection system
10.1007/s10113-024-02193-9 · 2024
Unresolved referenced work
Kept as external metadata until matched
10.3390/su12010394
10.3390/su12010394
Unresolved referenced work
Kept as external metadata until matched
Unresolved referenced work
Kept as external metadata until matched
Unresolved referenced work
Kept as external metadata until matched
10.3390/su12010394
10.3390/su12010394 · doi-reference
Boon and burden: Economic performance and future perspectives of the Venice flood protection system
10.1007/s10113-024-02193-9 · doi-reference
The prediction of floods in Venice: Methods, models and uncertainty (review article)
10.5194/nhess-21-2679-2021 · doi-reference
10.3390/w13091173
10.3390/w13091173 · doi-reference
A multi-model architecture based on Long Short-Term Memory neural networks for multi-step sea-level forecasting
10.1016/j.future.2021.05.008 · doi-reference
Hourly-scale coastal sea-level modeling in a changing climate using long short-term memory neural network
10.1016/j.scitotenv.2020.137613 · doi-reference
Short- and long-term tidal level forecasting: A novel hybrid TCN + LSTM framework
10.1016/j.seares.2025.102577 · doi-reference
Improved Method of sea-level Forecasting at Venice (Northern Adriatic Sea)
10.1016/j.cnsns.2004.11.008 · doi-reference
Application of Auto-Regressive (AR) Analysis to Improve Short-Term Prediction of Water Levels in the Yangtze Estuary
10.1016/j.jhydrol.2020.125386 · doi-reference
10.1109/icoict49345.2020.9166423
10.1109/icoict49345.2020.9166423 · doi-reference
Review of machine learning methods for sea-level change modeling and prediction
10.1016/j.scitotenv.2024.176410 · doi-reference
Accuracy Assessment of Global Barotropic Ocean Tide Models
10.1002/2014rg000450 · doi-reference
Physical Processes That Impact the Evolution of Global Mean sea-level in Ocean Climate Models
10.1016/j.ocemod.2012.04.003 · doi-reference
The Effect of non linear Factors on Tide-Surge Interaction: A Case Study of Typhoon Rammasun in Tieshan Bay, China
10.1016/j.ecss.2019.01.024 · doi-reference
Adaptation of Classical Tidal Harmonic Analysis to Nonstationary Tides, with Application to River Tides
10.1175/jtech-d-12-00016.1 · doi-reference
Physics and data dual-driven deep learning model for tide level forecasting
10.1016/j.oceaneng.2025.124134 · doi-reference
Daily sea-level Prediction at Chiayi Coast, Taiwan Using Extreme Learning Machine and Relevance Vector Machine
10.1016/j.gloplacha.2017.12.018 · doi-reference
Assessing Climate Change Impacts, sea-level Rise and Storm Surge Risk in Port Cities: A Case Study on Copenhagen
10.1007/s10584-010-9978-3 · doi-reference
Integrated Modelling of Sea-State Forecasts for Safe Navigation and Operational Management in Ports: Application in the Mediterranean Sea
10.1016/j.apm.2020.08.015 · doi-reference
Sea-level Rise Will Change Estuarine Tidal Energy: A Review
10.1016/j.rser.2021.111855 · doi-reference
A Review of Modelling Approaches on Tidal Analysis and Prediction
10.5194/isprs-archives-xlii-4-w16-23-2019 · doi-reference