Sea Level Prediction by Using Seasonal Autoregressive Integrated Moving Average Model, Case Study in Semarang, Indonesia
01 Jun 2020-
TL;DR: In this paper, the authors proposed a sea level prediction by using the autoregressive integrated moving average (ARIMA) and the seasonal Autoregressive Integrated Moving Average (SARIM) to predict sea level in Tanjung Mas Harbour in Semarang, Indonesia.
Abstract: Sea level prediction system is an important tool for many coastal engineering applications, such as for designing of engineering structures in coastal or in offshore, routing of vessels, predicting and preventing flood in low land coastal areas, etc. One classical method to predict sea level is by using the Tidal Harmonic Analysis, in which the sea level is approximated by summation of tidal components. The method needs long historical time series data, and it cannot predict non-tidal component or sealevel anomaly. In this paper, we propose a sea level prediction by using the Autoregressive Integrated Moving Average (ARIMA) and the Seasonal Autoregressive Integrated Moving Average (SARIMA) to predict sea level. Here, we choose a study case in Tanjung Mas Harbour in Semarang, Indonesia. Several input combinations for the ARIMA and the SARIMA are investigated for finding the best fit parameters. Results of prediction by using both methods are compared with the classical Tidal Harmonic Analysis. The accuracy of each method is investigated by calculating the RMSE and R-squared value. Despite of the seasonal data that is used in this paper, the ARIMA method gives the best prediction.
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Journal Article•
TL;DR: In this paper, the authors used ANN and multiplicative autoregressive integrated moving average (ARIMA) models to predict the Caspian Sea's mean monthly surface water level for the period of January 1986 to December 1993.
Abstract: Fluctuations of the Caspian Sea's mean monthly surface water level for the period of January 1986 to December 1993 were studied. The time series data showed an increasing trend and seasonal variations. Artificial neural network (ANN) and multiplicative autoregressive integrated moving average (ARIMA) modeling were used to predict the time series data. The ANN's input and output consisted of the last 12 months and the current month surface water levels, respectively. The selected ARIMA model required one-month regular differencing, 12-month seasonal differencing, and had a moving average component of lag 12. The ANN and ARIMA predictions for the period of January to December 1993 were very reasonable when compared with the recorded levels. On average, the ANN model underestimated the sea level by three cm, whereas the ARIMA model overestimated it by three cm. The monthly predictions for January to December 1994 presented a continuation of the Caspian Sea water surface level rise that would have various adv...
3 citations
2 citations
07 Apr 2023
TL;DR: In this article , the authors used Transformer deep learning approaches to predict sea data levels using only four months of data in Pangandaran, Indonesia, using the sea level dataset obtained from the Inexpensive Device for Sea Level measurement (IDSL).
Abstract: Sea level prediction is essential information for citizens who live in the coastal area and plan to build structures, especially in the construction stage around the inshore and offshore locations. The statistical method and tidal harmonic analysis have been used to predict the sea level but require long terms historical sea level data to achieve reasonable accuracy. This paper uses Transformer deep learning approaches to predict sea data levels. This paper uses only four months of data in Pangandaran, Indonesia. We use the sea level dataset obtained from the Inexpensive Device for Sea Level measurement (IDSL). The model is trained to predict 1, 7, and 14 days. We also study the sensitivity of the model in terms of lookbacks. The performance of the Transformer was compared with two other popular deep-learning methods; RNN and LSTM. To forecast 14 days, the Transformer model results in a higher coefficient correlation (CC) of 0.993 and a lower root mean squared error (RMSE) value of 0.055 compared to the other two models. Moreover, the Transformer has a faster computing performance than the other two models.
07 Apr 2023
TL;DR: In this paper , the authors used Transformer deep learning approaches to predict sea data levels using only four months of data in Pangandaran, Indonesia, using the sea level dataset obtained from the Inexpensive Device for Sea Level measurement (IDSL).
Abstract: Sea level prediction is essential information for citizens who live in the coastal area and plan to build structures, especially in the construction stage around the inshore and offshore locations. The statistical method and tidal harmonic analysis have been used to predict the sea level but require long terms historical sea level data to achieve reasonable accuracy. This paper uses Transformer deep learning approaches to predict sea data levels. This paper uses only four months of data in Pangandaran, Indonesia. We use the sea level dataset obtained from the Inexpensive Device for Sea Level measurement (IDSL). The model is trained to predict 1, 7, and 14 days. We also study the sensitivity of the model in terms of lookbacks. The performance of the Transformer was compared with two other popular deep-learning methods; RNN and LSTM. To forecast 14 days, the Transformer model results in a higher coefficient correlation (CC) of 0.993 and a lower root mean squared error (RMSE) value of 0.055 compared to the other two models. Moreover, the Transformer has a faster computing performance than the other two models.
08 Dec 2022
TL;DR: In this paper , extreme learning machine (ELM) is used to get the maximum water level forecasting value in hourly period, the most significant results obtained for the two statistical errors are 0.1141 and 0.3377.
Abstract: Water level forecasting is an essential thing for PT. Garam. This is closely related to the position of the sluice gate to enter raw materials for making salt. Therefore, placing water gates in the coastal area is essential in maximizing the incoming raw materials. Extreme learning machine (ELM) is used to get the maximum water level forecasting value in hourly period. In this study, data distribution is the main point that is the focus of the discussion. The MSE and RMSE values are calculated to get minor deals for the training and testing processes. The most significant results obtained for the two statistical errors are 0.1141 and 0.3377.
References
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01 May 1971
7,355 citations
"Sea Level Prediction by Using Seaso..." refers background in this paper
...The combination of these two models that process a stationary data is called the ARIMA(p,d,q) [11]....
[...]
5,341 citations
TL;DR: In this paper, a set of MATLAB programs have been written to perform classical harmonic analysis for periods of about 1 year or shorter, account for unresolved constituents using nodal corrections, and compute confidence intervals for the analyzed components.
2,403 citations
"Sea Level Prediction by Using Seaso..." refers methods in this paper
...Tidal Harmonic Analysis, coefficients in (2) are obtained by using the Least Square method, see [8]....
[...]
...The tide model that is used in this study is the T-Tide [8]....
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TL;DR: In this article, the artificial intelligence meshless methodology of neural networks was used to predict hourly sea level variations for the following 24-h, as well as for half-daily, daily, 5-daily and 10-daily mean sea levels.
Abstract: In the present study, the artificial intelligence meshless methodology of neural networks was used to predict hourly sea level variations for the following 24 h, as well as for half-daily, daily, 5-daily and 10-daily mean sea levels. The methodology is site specific; therefore, as an example, the measurements from a single tide gauge at Hillarys Boat Harbour, Western Australia, for the period December 1991–December 2002 were used to train and to validate the employed neural networks. The results obtained show the feasibility of the neural sea level forecasts in terms of the correlation coefficient (0.7–0.9), root mean square error (about 10% of tidal range) and scatter index (0.1–0.2).
114 citations
TL;DR: In this paper, the authors used ANN and multiplicative autoregressive integrated moving average (ARIMA) models to predict the Caspian Sea's mean monthly surface water level for the period of January 1986 to December 1993.
Abstract: Fluctuations of the Caspian Sea's mean monthly surface water level for the period of January 1986 to December 1993 were studied. The time series data showed an increasing trend and seasonal variations. Artificial neural network (ANN) and multiplicative autoregressive integrated moving average (ARIMA) modeling were used to predict the time series data. The ANN's input and output consisted of the last 12 months and the current month surface water levels, respectively. The selected ARIMA model required one-month regular differencing, 12-month seasonal differencing, and had a moving average component of lag 12. The ANN and ARIMA predictions for the period of January to December 1993 were very reasonable when compared with the recorded levels. On average, the ANN model underestimated the sea level by three cm, whereas the ARIMA model overestimated it by three cm. The monthly predictions for January to December 1994 presented a continuation of the Caspian Sea water surface level rise that would have various adv...
84 citations