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Journal ArticleDOI

Daily sea level forecast at tide gauge Burgas, Bulgaria using artificial neural networks

01 Aug 2011-Journal of Sea Research (Elsevier)-Vol. 66, Iss: 2, pp 154-161
TL;DR: In this paper, the authors used ANNs to correlate the nonlinear relationship between an input and output of the sea levels by recognizing the historic patterns between them, and obtained results indicate that the artificial neural technique is suitable for short and long-term forecasts of sea level parameters.
About: This article is published in Journal of Sea Research.The article was published on 2011-08-01. It has received 44 citations till now. The article focuses on the topics: Tide gauge.
Citations
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Journal ArticleDOI
01 Feb 2018
TL;DR: In this paper, the applicability and capability of extreme learning machine (ELM) and relevance vector machine (RVM) models for predicting sea level variations and compare their performances with powerful machine learning methods, namely, SVM and RBF.
Abstract: The analysis and the prediction of sea level fluctuations are core requirements of marine meteorology and operational oceanography. Estimates of sea level with hours-to-days warning times are especially important for low-lying regions and coastal zone management. The primary purpose of this study is to examine the applicability and capability of extreme learning machine (ELM) and relevance vector machine (RVM) models for predicting sea level variations and compare their performances with powerful machine learning methods, namely, support vector machine (SVM) and radial basis function (RBF) models. The input dataset from the period of January 2004 to May 2011 used in the study was obtained from the Dongshi tide gauge station in Chiayi, Taiwan. Results showed that the ELM and RVM models outperformed the other methods. The performance of the RVM approach was superior in predicting the daily sea level time series given the minimum root mean square error of 34.73 mm and the maximum determination coefficient of 0.93 (R2) during the testing periods. Furthermore, the obtained results were in close agreement with the original tide-gauge data, which indicates that RVM approach is a promising alternative method for time series prediction and could be successfully used for daily sea level forecasts.

43 citations

Journal ArticleDOI
TL;DR: Artificial Neural Networks (ANNs) were firstly used to model ocean engineering problems in the decade of 1990s as discussed by the authors , and since then, this soft-modeling technique has proved several advantages against traditional approaches.

30 citations

Journal ArticleDOI
TL;DR: The calorific value of synthetic gas (syngas) produced by gasification of dewatered sludge derived from treatment of tannery wastewater, using artificial neural network modeling techniques, is reported on.

28 citations

Posted ContentDOI
Xingsheng Shu1, Wei Ding1, Yong Peng1, Ziru Wang1, Jian Wu1, Min Li1 
TL;DR: The feasibility of a relatively new AI model, namely the convolutional neural network (CNN), is explored for forecasting monthly streamflow and shows better stability in forecasting accuracy.
Abstract: Monthly streamflow forecasting is vital for managing water resources. Recently, numerous studies have explored and evidenced the potential of artificial intelligence (AI) models in hydrological forecasting. In this study, the feasibility of the convolutional neural network (CNN), a deep learning method, is explored for monthly streamflow forecasting. CNN can automatically extract critical features from numerous inputs with its convolution–pooling mechanism, which is a distinct advantage compared with other AI models. Hydrological and large-scale atmospheric circulation variables, including rainfall, streamflow, and atmospheric circulation factors are used to establish models and forecast streamflow for Huanren Reservoir and Xiangjiaba Hydropower Station, China. The artificial neural network (ANN) and extreme learning machine (ELM) with inputs identified based on cross-correlation and mutual information analyses are established for comparative analyses. The performances of these models are assessed with several statistical metrics and graphical evaluation methods. The results show that CNN outperforms ANN and ELM in all statistical measures. Moreover, CNN shows better stability in forecasting accuracy.

27 citations

Journal ArticleDOI
TL;DR: In this paper, the authors proposed a conceptual framework to measure sustainability impacts of Tidal River Management (TRM) from environmental, socio-economic and institutional perspectives, and developed an inclusive conceptual framework for understanding the important impacts of each indicator at various spatial and temporal scales.

20 citations

References
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01 Jan 2007
TL;DR: Drafting Authors: Neil Adger, Pramod Aggarwal, Shardul Agrawala, Joseph Alcamo, Abdelkader Allali, Oleg Anisimov, Nigel Arnell, Michel Boko, Osvaldo Canziani, Timothy Carter, Gino Casassa, Ulisses Confalonieri, Rex Victor Cruz, Edmundo de Alba Alcaraz, William Easterling, Christopher Field, Andreas Fischlin, Blair Fitzharris.
Abstract: Drafting Authors: Neil Adger, Pramod Aggarwal, Shardul Agrawala, Joseph Alcamo, Abdelkader Allali, Oleg Anisimov, Nigel Arnell, Michel Boko, Osvaldo Canziani, Timothy Carter, Gino Casassa, Ulisses Confalonieri, Rex Victor Cruz, Edmundo de Alba Alcaraz, William Easterling, Christopher Field, Andreas Fischlin, Blair Fitzharris, Carlos Gay García, Clair Hanson, Hideo Harasawa, Kevin Hennessy, Saleemul Huq, Roger Jones, Lucka Kajfež Bogataj, David Karoly, Richard Klein, Zbigniew Kundzewicz, Murari Lal, Rodel Lasco, Geoff Love, Xianfu Lu, Graciela Magrín, Luis José Mata, Roger McLean, Bettina Menne, Guy Midgley, Nobuo Mimura, Monirul Qader Mirza, José Moreno, Linda Mortsch, Isabelle Niang-Diop, Robert Nicholls, Béla Nováky, Leonard Nurse, Anthony Nyong, Michael Oppenheimer, Jean Palutikof, Martin Parry, Anand Patwardhan, Patricia Romero Lankao, Cynthia Rosenzweig, Stephen Schneider, Serguei Semenov, Joel Smith, John Stone, Jean-Pascal van Ypersele, David Vaughan, Coleen Vogel, Thomas Wilbanks, Poh Poh Wong, Shaohong Wu, Gary Yohe

7,720 citations

Book
01 Jan 1993
TL;DR: A guide to the fundamental mathematics of neurocomputing, a review of neural network models and an analysis of their associated algorithms, and state-of-the-art procedures to solve optimization problems are explained.
Abstract: From the Publisher: Artificial neural networks can be employed to solve a wide spectrum of problems in optimization, parallel computing, matrix algebra and signal processing. Taking a computational approach, this book explains how ANNs provide solutions in real time, and allow the visualization and development of new techniques and architectures. Features include a guide to the fundamental mathematics of neurocomputing, a review of neural network models and an analysis of their associated algorithms, and state-of-the-art procedures to solve optimization problems. Computer simulation programs MATLAB, TUTSIM and SPICE illustrate the validity and performance of the algorithms and architectures described. The authors encourage the reader to be creative in visualizing new approaches and detail how other specialized computer programs can evaluate performance. Each chapter concludes with a short bibliography. Illustrative worked examples, questions and problems assist self study. The authors' self-contained approach will appeal to a wide range of readers, including professional engineers working in computing, optimization, operational research, systems identification and control theory. Undergraduate and postgraduate students in computer science, electrical and electronic engineering will also find this text invaluable. In particular, the text will be ideal to supplement courses in circuit analysis and design, adaptive systems, control systems, signal processing and parallel computing.

1,522 citations


"Daily sea level forecast at tide ga..." refers background or methods in this paper

  • ...Following routine procedures for the selection of the best ANN suited to the daily sea level data, different activation function options and network architectures are compared (Cichocki & Unbehauen, 1993; Demuth et al., 2008)....

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  • ...The General Regression Neural Network (GRNN)model is a kind of the radial basis network that is often used for any regression problem (Cichocki and Unbehauen, 1993)....

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  • ...Any nonlinear mathematical function of input–output relations among the experimental data can be approximated by artificial neural networks (Cichocki and Unbehauen, 1993)....

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  • ...Conclusions are reported in the last section....

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  • ...To forecast the daily sea levels the following models are developed: (a) ordinary time-dependent function with harmonic terms; (b) multilayer Feed-Forward (FF); (c) Cascade-Feed-Forward (CFF); (d) Feed-Forward Time-Delay (FFTD); (e) Radial Basis Function (RBF); (f) Generalized Regression (GR)…...

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Book
28 Dec 1987
TL;DR: Tidal Patterns Meteorological and Other Non-tidal Disturbances Some Definitions of Common Terms Basic Statistics of Tides as Time Series Observations and Data Reduction Forces Analysis and Prediction Tidal Dynamics Biology: Some Tidal Influences Filters for Tidal Time Series Response Analysis Inputs and Theory Analysis of Currents Theoretical Tidal dynamics Legal Definitions in the Coastal Zone as discussed by the authors.
Abstract: Introduction: Early Ideas and Observations Tidal Patterns Meteorological and Other Non-tidal Disturbances Some Definitions of Common Terms Basic Statistics of Tides as Time Series Observations and Data Reduction Forces Analysis and Prediction Tidal Dynamics Biology: Some Tidal Influences Filters for Tidal Time Series Response Analysis Inputs and Theory Analysis of Currents Theoretical Tidal Dynamics Legal Definitions in the Coastal Zone

987 citations


"Daily sea level forecast at tide ga..." refers background or methods in this paper

  • ...Traditionally, a tidal harmonic analysis is used for the sea-level prediction (Pugh, 1987), but byneglecting thehydro-meteorological effects theprediction error can reach 30% (Ghorbani et al....

    [...]

  • ...The observed sea level can be represented as a sum of a relative mean level at a given location, an astronomical tidal component and a non-tidal residual sum (Pugh, 1987)....

    [...]

  • ...Traditionally, a tidal harmonic analysis is used for the sea-level prediction (Pugh, 1987), but byneglecting thehydro-meteorological effects theprediction error can reach 30% (Ghorbani et al., 2010)....

    [...]