High-Precision Combined Tidal Forecasting Model

Liu, Jiao; Shi, Guoyou; Zhu, Kaige

doi:10.3390/a12030065

Cited by 14 publications

(9 citation statements)

References 23 publications

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“…e determination of the coefficients p, d, and q requires experience, but the theoretical support is still very meaningful. According to some available literatures like [41], the determination of the values of p, d, and q can be summed up as follows: (a) Since the sequence requires a first-order difference to achieve smoothness, we determine that the value of d is 1. (b) Next, based on the fact that the autocorrelation images are truncated and the partial correlation images are smeared, we conclude that the sequence is suitable for the AR model.…”

Section: Resultsmentioning

confidence: 99%

Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models

Pan

2019

Mathematical Problems in Engineering

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Forecasting energy data, especially the primary energy requirement, is the key part of policy-making. For those territories of different developing types, seeking a knowledge-based and dependable forecasting model is an essential prerequisite for the prosperous development of policy-making. In this paper, both autoregressive integrated moving average and backpropagation neural network models which have been proved to be very efficient in forecasting are applied to the forecasts of the primary energy consumption of three different developing types of territories. It is shown that the average relative errors between the actual data and simulated value are from 4.5% to 5.9% by the autoregressive integrated moving average and from 0.04% to 0.47% by the backpropagation neural network. Specially, this research shows that the backpropagation neural network model presents a better prediction of primary energy requirement when considering gross domestic product, population, and the particular values as predictors. Furthermore, we indicate that the single-input backpropagation neural network model can still work when the particular values have contributed most to the energy consumption.

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Section: Resultsmentioning

confidence: 99%

Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models

Pan

2019

Mathematical Problems in Engineering

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“…Aside from, some discrepancies during the flood and dry seasons when the field observations show severe variations. (Liu, and Zhu, 2019) proposed a combined tidal forecasting model based on harmonic analysis and autoregressive integrated moving average support vector regression (ARIMA-SVR). Liu proposed hybrid model to ameliorate the low precision associated with single prediction models.…”

Section: Yearmentioning

confidence: 99%

A Review of Modelling Approaches on Tidal Analysis and Prediction

Abubakar

Mahmud

Tang

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Tide height depends on both long-term astronomical effects that are principally affected by the moon and sun and short-term meteorological effects caused by severe weather conditions which are very important tasks for human activities, safe marine navigation in shallow areas, oceans and coastal engineering work. Conventional tidal forecasting techniques are based on harmonic analysis, which is a superposition of many sinusoidal constituents with three parameters amplitudes, Phase and frequencies using the least squares method to determine the harmonic parameters. However, harmonic analysis required a large number of parameters and long-term tidal measured for precise tidal level predictions. Furthermore, what seems to stand out by the other researchers on traditional harmonic methods, was its limitation when short data are involved and rely on based on the analysis of astronomical components and they can be insufficient when the influence of non-astronomical components such as the weather, is important. Therefore, conventional harmonic analysis alone does not adequately predict the coastal water level variation, in order to deal with these situations and provide predictions with the desired accuracy, with respect to the length of the available tidal record, an alternative approach has been developed by various tidalist. In this study the state - of - art for tidal analysis and prediction techniques that have proven to be successful in a variety of circumstances have been reviewed in a systematic and consistent way for holistic understanding with a view to provide a reference for future work, showing their main mathematical concepts, model capabilities for tidal analysis and prediction with their limitations.

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“…The main drawback of this model is its overly complex structure, which requires tedious computational steps to derive prediction results. Liu et al [22] proposed a combined tide forecasting model based on harmonic analysis and autoregressive integrated moving average-support vector regression (ARIMA-SVR), which improves the accuracy of singleprediction models. This combined model shows effectively improved prediction accuracy, but the improvement is limited.…”

Section: Introductionmentioning

confidence: 99%

A Modular Tide Level Prediction Method Based on a NARX Neural Network

Yin

et al. 2021

IEEE Access

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Tide variations are affected not only by periodic movement of celestial bodies but also by time-varying interference from the external environment. To improve the accuracy of tide prediction, a modular tide level prediction model (HA-NARX) is proposed. This model divides tide data into two parts: astronomical tide data affected by celestial tide-generating forces and nonastronomical tide data affected by various environmental factors. Final tide prediction results are obtained using a nonlinear autoregressive exogenous model (NARX) neural network combined with harmonic analysis (HA) data. To verify the feasibility of the model, tide data under different climatic and geographical conditions are used to simulate the prediction of tide levels, and the results are compared with those of traditional HA, the genetic algorithm-back propagation (GA-BP) neural network and the wavelet neural network (WNN). The results show that the greater the influence of meteorological factors on tides, the more obvious is the improvement in accuracy and stability of HA-NARX prediction results compared to traditional models, with the highest prediction accuracy improvement of 234%. The proposed model not only has a simple structure but can also effectively improve the stability and accuracy of tide prediction.

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High-Precision Combined Tidal Forecasting Model

Cited by 14 publications

References 23 publications

Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models

Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models

A Review of Modelling Approaches on Tidal Analysis and Prediction

A Modular Tide Level Prediction Method Based on a NARX Neural Network

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