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Open accessJournal ArticleDOI: 10.1080/19475705.2021.1887372

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

02 Mar 2021-Geomatics, Natural Hazards and Risk (Taylor & Francis)-Vol. 12, Iss: 1, pp 653-674
Abstract: 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 techniq...

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Journal ArticleDOI: 10.1080/10106049.2021.1958015
Naheem Adebisi1, Abdul-Lateef Balogun1Institutions (1)
Abstract: In this study, we conducted a holistic evaluation of current and future trend in coastal sea level at the 21 stations along Malaysia’s coastline. For sea level prediction, univariate and 3 scenario...

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Topics: Sea level (52%)

1 Citations


Open accessJournal ArticleDOI: 10.3390/JMSE9060651
Abstract: In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the energy proportion of the intrinsic mode function (IMF); the high-frequency part is denoised by wavelet packet transform (WPT), whereas the denoised high-frequency IMFs and low-frequency IMFs reconstruct the pure sea clutter signal together. According to the chaotic characteristics of sea clutter, we proposed an adaptive training timesteps strategy. The training timesteps of network were determined by the width of embedded window, and the chaotic long short-term memory network detection was designed. The sea clutter signals after denoising were predicted by chaotic long short-term memory (LSTM) network, and small target signals were detected from the prediction errors. The experimental results showed that the CEEMD-WPT algorithm was consistent with the target distribution characteristics of sea clutter, and the denoising performance was improved by 33.6% on average. The proposed chaotic long- and short-term memory network, which determines the training step length according to the width of embedded window, is a new detection method that can accurately detect small targets submerged in the background of sea clutter.

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Topics: Clutter (58%), Chaotic (53%), Wavelet packet decomposition (51%)

1 Citations


Open accessJournal ArticleDOI: 10.3390/RS13183587
09 Sep 2021-Remote Sensing
Abstract: Rising sea level is generally assumed and widely reported to be the significant driver of coastal erosion of most low-lying sandy beaches globally. However, there is limited data-driven evidence of this relationship due to the challenges in quantifying shoreline dynamics at the same temporal scale as sea-level records. Using a Google Earth Engine (GEE)-enabled Python toolkit, this study conducted shoreline dynamic analysis using high-frequency data sampling to analyze the impact of sea-level rise on the Malaysian coastline between 1993 and 2019. Instantaneous shorelines were extracted from a test site on Teluk Nipah Island and 21 tide gauge sites from the combined Landsat 5–8 and Sentinel 2 images using an automated shoreline-detection method, which was based on supervised image classification and sub-pixel border segmentation. The results indicated that rising sea level is contributing to shoreline erosion in the study area, but is not the only driver of shoreline displacement. The impacts of high population density, anthropogenic activities, and longshore sediment transportation on shoreline displacement were observed in some of the beaches. The conclusions of this study highlight that the synergistic use of multi-sensor remote-sensing data improves temporal resolution of shoreline detection, removes short-term variability, and reduces uncertainties in satellite-derived shoreline analysis compared to the low-frequency sampling approach.

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Topics: Shore (52%), Coastal erosion (52%), Tide gauge (51%)

Journal ArticleDOI: 10.1007/S11269-021-02937-W
Abstract: Streamflow forecasting plays a key role in improvement of water resource allocation, management and planning, flood warning and forecasting, and mitigation of flood damages. There are a considerable number of forecasting models and techniques that have been employed in streamflow forecasting and gained importance in hydrological studies in recent decades. In this study, the main objective was to compare the accuracy of four data-driven techniques of Linear Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) network in daily streamflow forecasting. For this purpose, three scenarios were defined based on historical precipitation and streamflow series for 26 years of the Kentucky River basin located in eastern Kentucky, US. Statistical criteria including the coefficient of correlation ( $$R$$ ), Nash-Sutcliff coefficient of efficiency ( $$E$$ ), Nash-Sutcliff for High flow ( $${E}_{H}$$ ), Nash-Sutcliff for Low flow ( $${E}_{L}$$ ), normalized root mean square error ( $$NRMSE$$ ), relative error in estimating maximum flow ( $$REmax$$ ), threshold statistics ( $$TS$$ ), and average absolute relative error ( $$AARE$$ ) were employed to compare the performances of these methods. The results show that the LSTM network outperforms the other models in forecasting daily streamflow with the lowest values of $$NRMSE$$ and the highest values of $${E}_{H}$$ , $${E}_{L}$$ , and $$R$$ under all scenarios. These findings indicated that the LSTM is a robust data-driven technique to characterize the time series behaviors in hydrological modeling applications.

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Open accessDOI: 10.1109/3ICT53449.2021.9581812
Firuz Kamalov1, Ikhlaas Gurrib1, Fadi Thabtah2Institutions (2)
29 Sep 2021-
Abstract: Time series analysis such as stock price forecasting is an important part of financial research. In this regard, autoregressive (AR) and neural network (NN) models offer contrasting approaches to time series modeling. Although AR models remain widely used, NN models and their variant long short-term memory (LSTM) networks have grown in popularity. In this paper, we compare the performance of AR, NN, and LSTM models in forecasting linearly lagged time series. To test the models we carry out extensive numerical experiments based on simulated data. The results of the experiments reveal that despite the inherent advantage of AR models in modeling linearly lagged data, NN models perform just as well, if not better, than AR models. Furthermore, the NN models outperform LSTMs on the same data. We find that a simple multi-layer perceptron can achieve highly accurate out of sample forecasts. The study shows that NN models perform well even in the case of linearly lagged time series.

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Topics: Time series (53%), Autoregressive model (52%)
References
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53 results found


Journal ArticleDOI: 10.1109/TAC.1972.1099963
P. Young1, S. ShellswellInstitutions (1)
Abstract: time series analysis san francisco state university, 6 4 introduction to time series analysis, box and jenkins time series analysis forecasting and, th15 weeks citation classic eugene garfield, proc arima references 9 3 sas support, time series analysis forecasting and control pambudi, time series analysis forecasting and control george e, time series analysis forecasting and control ebook, time series analysis forecasting and control 5th edition, time series analysis forecasting and control fourth, time series analysis forecasting and control amazon, wiley time series analysis forecasting and control 5th, time series analysis forecasting and control edition 5, time series analysis forecasting and control 5th edition, time series analysis forecasting and control abebooks, time series analysis for business forecasting, time series analysis forecasting and control wiley, time series analysis forecasting and control book 1976, time series analysis forecasting and control researchgate, time series analysis forecasting and control edition 4, time series analysis forecasting amp control forecasting, george box publications department of statistics, time series analysis forecasting and control london, time series analysis forecasting and control an, time series analysis forecasting and control amazon it, box g e p and jenkins g m 1976 time series, time series analysis forecasting and control pdf slideshare, time series analysis forecasting and control researchgate, time series analysis forecasting and control 5th edition, time series analysis forecasting and control 5th edition, time series wikipedia, time series analysis forecasting and control abebooks, time series analysis forecasting and control, forecasting and time series analysis using the sca system, time series analysis forecasting and control by george e, time series analysis forecasting and control 5th edition, time series analysis forecasting and control 5th edition, box and jenkins time series analysis forecasting and control, time series analysis forecasting and control ebook, time series analysis forecasting and control, time series analysis and forecasting cengage, 6 7 references itl nist gov, time series analysis forecasting and control george e, time series analysis and forecasting statgraphics, time series analysis forecasting and control fourth edition, time series analysis forecasting and control, time series analysis forecasting and control wiley, time series analysis forecasting and control in

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Topics: Time series (54%)

9,124 Citations



Journal ArticleDOI: 10.2307/2988198
01 Dec 1978-The Statistician
Topics: Time series (59%)

5,338 Citations


Journal ArticleDOI: 10.1016/J.IJFORECAST.2006.03.001
Rob J. Hyndman1, Anne B. Koehler2Institutions (2)
Abstract: We discuss and compare measures of accuracy of univariate time series forecasts. The methods used in the M-competition as well as the M3-competition, and many of the measures recommended by previous authors on this topic, are found to be degenerate in commonly occurring situations. Instead, we propose that the mean absolute scaled error become the standard measure for comparing forecast accuracy across multiple time series.

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Topics: Forecast verification (60%), Forecast skill (57%), Univariate (53%)

3,116 Citations


Journal ArticleDOI: 10.1080/03610927708827533
Paul W. Holland1, Roy E. Welsch2Institutions (2)
Abstract: The rapid development of the theory of robust estimation (Huber, 1973) has created a need for computational procedures to produce robust estimates. We will review a number of different computational approaches for robust linear regression but focus on one—iteratively reweighted least-squares (IRLS). The weight functions that we discuss are a part of a semi-portable subroutine library called ROSEPACK (RObust Statistical Estimation PACKage) that has been developed by the authors and Virginia Klema at the Computer Research Center of the National Bureau of Economic Research, Inc. in Cambridge, Mass. with the support of the National Science Foundation. This library (Klema, 1976) makes it relatively simple to implement an IRLS regression package.

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1,741 Citations