Machine Learning Algorithms for Real-time Tsunami Inundation Forecasting: A Case Study in Nankai Region

Fauzi, Ardiansyah; Mizutani, Norimi

doi:10.1007/s00024-019-02364-4

Cited by 38 publications

(35 citation statements)

References 42 publications

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“…Once the data is collected they used multilayer perceptron techniques to create the model to develop the model and after they got the best fit, the model can then predict tsunamis accurately. They have conducted experiments based on the data from overseas bases in Japan and have studied the cases there and the results show that their method is very fast and effective [11]. J. K. Roy et.…”

Section: Related Workmentioning

confidence: 99%

NatDisP – An Intelligent Natural Disaster Predictor

Mittapalli

Mutha

Maheswari

2021

Preprint

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Natural disasters are adverse actions that happen due to the natural processes of the earth. In today’s world with so much pollution, global warming and because of so many reasons, natural disasters are happening far more than they used to happen before and many people in the world face problems, lose their houses, livelihoods and even their houses. It is really painful to get to know the effects of these natural disasters. So, this paper proposes a model which helps us predict natural disasters before they happen using wireless technologies. In this paper modern technologies like IOT, artificial intelligence and machine learning are used. Here for the prediction of each disaster, data/signals given by nature are used , for example for the earthquake module the seismic signals from the earth are used, systems like UNITE are used where the sensors placed in the earth to get the seismic data. For the other disasters also such data is taken from signals given by nature. A detailed explanation on how disasters are predicted based on these simple signals and data from nature is given. In this paper solutions based on wireless technologies to solve some after effects of these natural disasters are also suggested . So, basically our idea is to have all disasters predicted in one place using modern computer science technologies.

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Section: Related Workmentioning

confidence: 99%

NatDisP – An Intelligent Natural Disaster Predictor

Mittapalli

Mutha

Maheswari

2021

Preprint

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“…Running the low-resolution tsunami model in real time during an event, using the results of a rapid source inversion as initial conditions, then gives the input used to select the high-resolution model from the database that will be used as a forecast. Fauzi and Mizutani (2020) also considered this approach with a different ML algorithm, and also compared it with an approach that is more similar to the one we adopt, in which the ML algorithm using a shallow neural network directly generates a new forecast rather than selecting a precomputed model from a database.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Machine Learning Approaches for Tsunami Forecasting from Sparse Observations

Liu¹,

Rim²,

Baraldi³

et al. 2022

Preprint

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We have explored various different machine learning (ML) approaches for forecasting tsunami amplitudes at a set of forecast points, based on hypothetical short-time observations at one or more observation points. As a case study, we chose an observation point near the entrance of the Strait of Juan de Fuca, and two forecast points in the Salish Sea, one in Discovery Bay and the other in Admiralty Inlet, the waterway leading to southern Puget Sound. One ML approach considered is to train a support vector machine (SVM) to predict the maximum amplitude at the forecast points. We also explored the use of two deep convolutional neural networks, a denoising autoencoder (DAE) and a variational autoencoder (VAE) to predict the full time se-

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“…More recently, a method proposed by Mulia et al (2018) produced high‐resolution inundation forecasts based on spatial patterns of maximum simulated tsunami elevations in a low‐resolution domain. Here, we improve upon such studies by taking advantage of neural networks modeling and deep learning applied to the same type of database, similar to a study by Fauzi and Mizutani (2019). However, we use a different variant of the deep learning algorithm and apply the method to a real case of the 2011 Tohoku‐oki tsunami.…”

Section: Introductionmentioning

confidence: 99%

“…However, CNN generally possesses more constraints (Ball et al, 2017), requiring input data preprocessing (Dong et al, 2015). In a tsunami forecasting case, Fauzi and Mizutani (2019) reported that more precalculated scenarios are required for their CNN model compared to other methods, although this is likely related to a particular database matching scheme. Therefore, in this study, we apply a relatively more straightforward DNN model.…”

Section: Introductionmentioning

confidence: 99%

Applying a Deep Learning Algorithm to Tsunami Inundation Database of Megathrust Earthquakes

2020

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We explore recent developments in computer science on deep learning to estimate high-resolution tsunami inundation from a quick low-resolution computation result. Deep network architecture is capable of storing large information acquired via a training/learning process by pairing low-and high-resolution deterministic simulation results from precalculated hypothetical scenarios. In a real case, with a real-time source estimate and linear simulation computed on a relatively low-grid resolution, optimized network parameters can be used to rapidly and accurately transform low-resolution simulation outputs to higher-resolution grids. We generate 532 source scenarios of interplate earthquakes (Mw 8-9) along the Japan Trench subduction zone to simulate tsunamis and utilize a precalculated library for deep learning. To test the proposed method, we consider a realistic source model inferred from static ground displacements and offshore tsunami data presumably available in real-time and compare forecasted inundation heights against observations in Rikuzentakata and Otsuchi cities associated with the 2011 Tohoku-oki tsunami. Our results show that the proposed method exhibits comparable accuracy to the conventional physics-based simulation but achieves approximately 90% reduction of real-time computational efforts. Thus, it has good potential as a future tsunami forecasting algorithm.

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Machine Learning Algorithms for Real-time Tsunami Inundation Forecasting: A Case Study in Nankai Region

Cited by 38 publications

References 42 publications

NatDisP – An Intelligent Natural Disaster Predictor

NatDisP – An Intelligent Natural Disaster Predictor

Comparison of Machine Learning Approaches for Tsunami Forecasting from Sparse Observations

Applying a Deep Learning Algorithm to Tsunami Inundation Database of Megathrust Earthquakes

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