Artificial neural network for tsunami forecasting

Romano, Michele; Liong, Shie‐Yui; Vu, Minh Tue; Zemskyy, Pavlo; Doan, Chi Dung; Dao, My Ha; Tkalich, Pavel

doi:10.1016/j.jseaes.2008.11.003

Cited by 37 publications

(21 citation statements)

References 25 publications

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“…ANNs are being used extensively for solving universal problems intelligently like continuous, discrete, and clustering [5], [6]. ANNs are being applied for different optimisation and mathematical problems such as classification, object and image recognition, signal processing, seismic events prediction, temperature and weather forecasting, bankruptcy, tsunami intensity, earthquake, and sea level [5], [7], [8], [9], [10]. Different techniques are used for optimal network performance for training ANNs such as evolutionary algorithms (EA), genetic algorithms (GA), partial swarm optimisation (PSO), differential evolution (DE), ant colony, and backpropagation algorithm [11], [12], [13], [14], [15], [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Guided Artificial Bee Colony Algorithm for Earthquake Time Series Data Prediction

Shah

Ghazali

Herawan

et al. 2014

Communications in Computer and Information Science

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Nowadays, computer scientists have shown the interest in the study of social insect's behaviour in neural networks area for solving different combinatorial and statistical problems. Chief among these is the Artificial Bee Colony (ABC) algorithm. This paper investigates the use of ABC algorithm that simulates the intelligent foraging behaviour of a honey bee swarm. Multilayer Perceptron (MLP) trained with the standard back propagation algorithm normally utilises computationally intensive training algorithms. One of the crucial problems with the backpropagation (BP) algorithm is that it can sometimes yield the networks with suboptimal weights because of the presence of many local optima in the solution space. To overcome ABC algorithm used in this work to train MLP learning the complex behaviour of earthquake time series data trained by BP, the performance of MLP-ABC is benchmarked against MLP training with the standard BP. The experimental result shows that MLP-ABC performance is better than MLP-BP for time series data.

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Section: Introductionmentioning

confidence: 99%

Hybrid Guided Artificial Bee Colony Algorithm for Earthquake Time Series Data Prediction

Shah

Ghazali

Herawan

et al. 2014

Communications in Computer and Information Science

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“…Singapore is developing its national tsunami research and warning capabilities, which will contribute eventually into the regional scientific and forecasting networks. Some tsunami propagation scenarios from potential sources due to fault rupture along the Sunda Arc (Indian Ocean) were considered in the previous publications by the authors (Tkalich et al, 2007a,b,c;Dao and Tkalich 2007;Dao et al, 2008;Romano et al, submitted for publication). The present paper describes scenario-based tsunami threat analysis for the SCS.…”

Section: Introductionmentioning

confidence: 99%

Tsunami propagation scenarios in the South China Sea

Dao

Tkalich

Chan

et al. 2009

Journal of Asian Earth Sciences

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“…These methods differ in both the formulation used to describe the evolution of the tsunami and the numerical methods used to solve the governing equations. The structure of these models ranges from data-driven neural networks (Romano et al 2009) to non-linear three-dimensional physics-based models (Zhang and Baptista 2008). These models are typically used to predict quantities such as arrival times, wave speeds and heights, as well as inundation extents and heights for developing efficient hazard mitigation plans.…”

mentioning

confidence: 99%

Towards spatially distributed quantitative assessment of tsunami inundation models

et al. 2010

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This paper presents a framework and data for spatially distributed assessment of tsunami inundation models. Our associated validation test is based upon the 2004 Indian Ocean tsunami, which affords a uniquely large amount of observational data for events of this kind. Specifically, we use eyewitness accounts to assess onshore flow depths and speeds as well as a detailed inundation survey of Patong City, Thailand to compare modelled and observed inundation. Model predictions matched well the detailed inundation survey as well as altimetry data from the JASON satellite, eyewitness accounts of wave front arrival times and onshore flow speeds. Important buildings and other structures were incorporated into the underlying elevation model and are shown to have a large influence on inundation extent.

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Artificial neural network for tsunami forecasting

Cited by 37 publications

References 25 publications

Hybrid Guided Artificial Bee Colony Algorithm for Earthquake Time Series Data Prediction

Hybrid Guided Artificial Bee Colony Algorithm for Earthquake Time Series Data Prediction

Tsunami propagation scenarios in the South China Sea

Towards spatially distributed quantitative assessment of tsunami inundation models

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