Hourly-scale coastal sea level modeling in a changing climate using long short-term memory neural network

Ishida, Kei; Tsujimoto, Gozo; Ercan, Ali; Tu, Tongbi; Kiyama, Masato; Amagasaki, Motoki

doi:10.1016/j.scitotenv.2020.137613

Cited by 29 publications

(13 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among the RNNs, the use of LSTM is becoming more prevalent recently due to their peculiar optimal performance with time series prediction. Ishida et al (2020) developed hourly sea level prediction model using LSTM neural network for Osaka tide gauge stations in Japan. Result of the study revealed that LSTM neural network give an extraordinary performance in modelling sea level showing error less than 8 cm and R score above 0.85 at all stations.…”

Section: Modelling Sea Levelmentioning

confidence: 99%

See 1 more Smart Citation

Sea level prediction using ARIMA, SVR and LSTM neural network: assessing the impact of ensemble Ocean-Atmospheric processes on models’ accuracy

Balogun

Adebisi

2021

Geomatics, Natural Hazards and Risk

View full text Add to dashboard Cite

This study aims to integrate a broad spectrum of ocean-atmospheric variables to predict sea level variation along West Peninsular Malaysia coastline using machine learning and deep learning techniques. 4 scenarios of different combinations of variables such as sea surface temperature, sea surface salinity, sea surface density, surface atmospheric pressure, wind speed, total cloud cover, precipitation and sea level data were used to train ARIMA, SVR and LSTM neural network models. Results show that atmospheric processes have more influence on prediction accuracy than ocean processes. Combining ocean and atmospheric variables improves the model prediction at all stations by 1-9% for both SVR and LSTM. The means of R accuracy of optimal performing LSTM, SVR and ARIMA models at all stations are 0.853, 0.748 and 0.710, respectively. Comparison of model performance shows that the LSTM model trained with ocean and atmospheric variables is optimal for predicting sea level variation at all stations except Pulua Langkawi where ARIMA model trained without ocean-atmospheric variables performed best due to the dominating tide influence. This suggests that performance and suitability of prediction models vary across regions and selecting an optimal prediction model depends on the dominant physical processes governing sea level variability in the area of investigation.

show abstract

Section: Modelling Sea Levelmentioning

confidence: 99%

“…LSTM has complex architecture that gives the network capability to keep layer information from past inputs for long storage to be used in subsequent training which make it suitable for time series application where current values depend on past records (Ishida et al 2020).…”

Section: Arima Svr and Lstmmentioning

confidence: 99%

Sea level prediction using ARIMA, SVR and LSTM neural network: assessing the impact of ensemble Ocean-Atmospheric processes on models’ accuracy

Balogun

Adebisi

2021

Geomatics, Natural Hazards and Risk

View full text Add to dashboard Cite

show abstract

“…In natural language processing, the padding method is frequently used to align IDLs for RNN. Similarly, Ishida et al (2020) incorporated a yearly time-series of global air temperature together with hourly input time-series for LSTM by employing this method to reflect the effects of global warming on the hourly scale coastal sea-level modeling. In addition, this study utilizes the padding method to provide hourly and daily time-series together as an input to LSTM.…”

Section: Methodsmentioning

confidence: 99%

Multi-time-scale input approaches for hourly-scale rainfall–runoff modeling based on recurrent neural networks

Ishida

Kiyama

Ercan

et al. 2021

Journal of Hydroinformatics

Self Cite

View full text Add to dashboard Cite

This study proposes two effective approaches to reduce the required computational time of the training process for time-series modeling through a recurrent neural network (RNN) using multi-time-scale time-series data as input. One approach provides coarse and fine temporal resolutions of the input time-series data to RNN in parallel. The other concatenates the coarse and fine temporal resolutions of the input time-series data over time before considering them as the input to RNN. In both approaches, first, the finer temporal resolution data are utilized to learn the fine temporal scale behavior of the target data. Then, coarser temporal resolution data are expected to capture long-duration dependencies between the input and target variables. The proposed approaches were implemented for hourly rainfall–runoff modeling at a snow-dominated watershed by employing a long short-term memory network, which is a type of RNN. Subsequently, the daily and hourly meteorological data were utilized as the input, and hourly flow discharge was considered as the target data. The results confirm that both of the proposed approaches can reduce the required computational time for the training of RNN significantly. Lastly, one of the proposed approaches improves the estimation accuracy considerably in addition to computational efficiency.

show abstract

“…The morphodynamical prediction responding to the oceanographic action of TC applies Neural Networks to sandbar movement [66][67][68], seasonal beach profile changes [69], and longshore sediment transport [70,71]. In addition to TC prediction, Neural Networks has been widely applied in the prediction of tidal level [72][73][74][75], wave height [61,76] and coastal floods [77]. Compared with conventional method for tidal level prediction (harmonic analysis), the excellent nonlinear problem processing capability of Neural Networks solves the environmentally influenced noises of seasonal effects and TC-induced surge superposed on the astronomical tide level series [75].…”

Section: Introductionmentioning

confidence: 99%

Storm Surge Prediction Based on Long Short-Term Memory Neural Network in the East China Sea

et al. 2021

View full text Add to dashboard Cite

As an area frequently suffering from storm surge, the Yangtze River Estuary in the East China Sea requires fast and accurate prediction of water level for disaster prevention and mitigation. Due to storm surge process being affected by the long-term and short-term correlation of multiple factors, this study attempts to introduce a data-driven idea into the water level prediction during storm surge. By collecting the observed meteorological data and water level data of 12 typhoons from 1986 to 2016 at the Lusi tidal station of Jiangsu Province, China near the north branch of the Yangtze River Estuary, a Long Short-Term Memory (LSTM) neural network model was constructed by using multi-factor time series to predict the water level during the storm surge period. This study concludes that the LSTM model performs precisely for 1 h prediction of water level during the storm surge period and it can provide a 15 h prediction of water level within a limited error, and the prediction performance of the LSTM model is visibly superior to the four traditional ML models by 41% in terms of Accuracy Coefficient.

show abstract

Hourly-scale coastal sea level modeling in a changing climate using long short-term memory neural network

Cited by 29 publications

References 12 publications

Sea level prediction using ARIMA, SVR and LSTM neural network: assessing the impact of ensemble Ocean-Atmospheric processes on models’ accuracy

Sea level prediction using ARIMA, SVR and LSTM neural network: assessing the impact of ensemble Ocean-Atmospheric processes on models’ accuracy

Multi-time-scale input approaches for hourly-scale rainfall–runoff modeling based on recurrent neural networks

Storm Surge Prediction Based on Long Short-Term Memory Neural Network in the East China Sea

Contact Info

Product

Resources

About