A Multiple-Parameter Methodology for Placement of Tsunami Sensor Networks

Meza, Joaquin; Catalán, Patricio A.; Tsushima, Hiroaki

doi:10.1007/s00024-019-02381-3

Cited by 10 publications

(3 citation statements)

References 47 publications

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“…Recently, cost-effective designs of regional tsunami observation networks have been proposed in many tsunami-prone countries, e.g., south of Java of Indonesia 49 , north of Chile 50 , 51 , and the Mediterranean Sea 52 , 53 . Additional inputs from seismic or geodetic data may also be harnessed to complement the scarce offshore tsunami observations 11 , 14 , 15 .…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based tsunami inundation prediction derived from offshore observations

et al. 2022

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The world’s largest and densest tsunami observing system gives us the leverage to develop a method for a real-time tsunami inundation prediction based on machine learning. Our method utilizes 150 offshore stations encompassing the Japan Trench to simultaneously predict tsunami inundation at seven coastal cities stretching ~100 km along the southern Sanriku coast. We trained the model using 3093 hypothetical tsunami scenarios from the megathrust (Mw 8.0–9.1) and nearby outer-rise (Mw 7.0–8.7) earthquakes. Then, the model was tested against 480 unseen scenarios and three near-field historical tsunami events. The proposed machine learning-based model can achieve comparable accuracy to the physics-based model with ~99% computational cost reduction, thus facilitates a rapid prediction and an efficient uncertainty quantification. Additionally, the direct use of offshore observations can increase the forecast lead time and eliminate the uncertainties typically associated with a tsunami source estimate required by the conventional modeling approach.

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Section: Discussionmentioning

confidence: 99%

Machine learning-based tsunami inundation prediction derived from offshore observations

et al. 2022

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“…Due to limited tsunami events in this area and limited tsunami observation data on the east coast of Lembeh Island, the arrival time and time-series tsunami height data at each candidate location are obtained from tsunami propagation modeling. Lee et al (2020) and Meza et al (2020) also use numerical tsunami modeling to determine the best location of tsunami detection instruments in Korea and Chile, respectively. Based on USGS (2020), major earthquakes with a magnitude above 7.0 concentrate in the middle of the Molucca Sea.…”

Section: Methodsmentioning

confidence: 99%

Optimal Tide Gauge Location for Tsunami Validation in The Lembeh Island, North Sulawesi

Sriyanto

Angmalisang

Manu

2022

Indonesian J. Geosci.

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The tsunami early warning system in Bitung does not work optimally, because there is no buoy as a marine equipment for tsunami validation before reaching the coastal area. The lack of buoy can be replaced by placing a tide gauge on the east coast of Lembeh Island. To determine the optimal tide gauge location, the simple additive weighting (SAW) method was used with three criteria. Those three criteria are the potential of tsunami detection, sufficient evacuation time, and an appropriate site for tide gauge installation. Numerical tsunami modeling is used to calculate the first two criteria. The third criterion is a limiting factor, because the tide gauge can only be installed on the dock. Therefore, there were only five candidate locations on the east coast of Lembeh, namely Dorbolang, Pancuran, Posokan, Motto, and Lirang. The result, Posokan is the best location for tide gauge placement with a total score of 2.884. Based on the simulation, an additional tide gauge in Posokan can detect tsunami at the average of 11.4 minutes earlier than use only the tide gauge currently available at Bitung port. It means that people on the coast of Bitung have more evacuation time before the tsunami hits the coastal area.

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“…Schindelé et al (2008) and Omira et al (2009) determined possible gauge locations by considering different epicenter locations and the resulting tsunami travel times. In addition, there are some optimization objectives, such as maximizing the tsunami prediction accuracy (An et al, 2018;Hossen et al, 2018;Meza et al, 2020;Mulia et al, 2017Mulia et al, , 2019 and minimizing the detection time (Ferrolino et al, 2020). Such optimization schemes are expected to be useful for recently developed early forecasting systems based on data assimilation and machine learning techniques (e.g., Heidarzadeh et al, 2019;Liu et al, 2021;Maeda et al, 2015;Makinoshima et al, 2021;Wang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Optimization of a Tsunami Gauge Configuration for Pseudo‐Super‐Resolution of Wave Height Distribution

Fujita,

Nomura,

Moriguchi

et al. 2024

Earth and Space Science

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In this study, we present an optimization method for determining a cost‐effective sparse configuration for tsunami gauges to realize the reconstruction of high‐resolution wave height distribution throughout the target region based on the concept of super‐resolution. This optimization method consists of three procedures. First, we generate time series data of tsunami wave heights at synthetic gauges by conducting numerical simulations of various earthquake and tsunami scenarios at the target site. Next, we apply proper orthogonal decomposition to the synthetic tsunami data to extract the spatial features of the wave height distribution. Finally, according to these spatial features, an optimization process is performed to determine a sparse configuration of synthetic gauges. In the optimization, the optimal gauges are sequentially selected from the set of synthetic gauges to reconstruct the wave height distribution with the highest accuracy. Targeting hypothetical Nankai Trough earthquakes and tsunamis, we determine the optimal configuration near Shikoku and demonstrate the wave height reconstruction capability of the approach by comparing the performance of networks with optimally and randomly placed gauges. The results indicate that coastal gauges contribute more to improving the reconstruction accuracy and that a configuration with 21 optimal gauges has satisfactory performance. In addition, we assess the performance of the existing NOWPHAS network installed in the Shikoku region and find that the reconstruction performance of the existing network is equivalent to that of the optimal gauge network.

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A Multiple-Parameter Methodology for Placement of Tsunami Sensor Networks

Cited by 10 publications

References 47 publications

Machine learning-based tsunami inundation prediction derived from offshore observations

Machine learning-based tsunami inundation prediction derived from offshore observations

Optimal Tide Gauge Location for Tsunami Validation in The Lembeh Island, North Sulawesi

Optimization of a Tsunami Gauge Configuration for Pseudo‐Super‐Resolution of Wave Height Distribution

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