Forecasting Tsunami Hazards Using Ocean Bottom Sensor Data and Classification Predictive Modeling
This study compares the results of analyzing tsunami simulations that are based on two approaches of characterizing earthquake slips, i.e., uniform (simplistic) and heterogeneous (complex) distributions. The aim of this study is to compare how heterogeneous and uniform distributed data affect the cl...
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2023-03-01
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| author | Yao Li |
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| description | This study compares the results of analyzing tsunami simulations that are based on two approaches of characterizing earthquake slips, i.e., uniform (simplistic) and heterogeneous (complex) distributions. The aim of this study is to compare how heterogeneous and uniform distributed data affect the classification of maximum near-shore tsunami amplitudes. Due to the lack of historical earthquake and tsunami data to train the forecasting model, 4000 stochastic tsunami simulations are employed. The focused location is Iwanuma, Japan, where an ocean bottom sensors (OBS) S-net network has been deployed. Multiple linear regression combined with the Akaike information criterion (AIC) is applied to the simulated off-shore wave amplitude data to fit the model. The estimated tsunami amplitude is classified into four levels of warning classes. The performance of the models is quantified by the accuracy of the confusion matrices and is compared with the base model, which only uses earthquake information. The forecasting accuracy can be improved by 30% when the wave amplitude data are used as additional information. The heterogeneous slip-based model reaches a higher accuracy than the uniform-slip based model. The results of this study are particularly valuable for setting up an OBS-based system for monitoring the physical phenomena of tsunamis, and choosing heterogeneous as a preferable slip distribution when tsunami events are simulated. |
| format | Article |
| id | doaj-art-94bcbed1836e4493bcc5e0bfb40a5b70 |
| institution | Kabale University |
| issn | 2504-3900 |
| language | English |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
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| series | Proceedings |
| spelling | doaj-art-94bcbed1836e4493bcc5e0bfb40a5b702025-08-20T03:43:14ZengMDPI AGProceedings2504-39002023-03-01871710.3390/IECG2022-14266Forecasting Tsunami Hazards Using Ocean Bottom Sensor Data and Classification Predictive ModelingYao Li0Department of Statistical and Actuarial Sciences, Western University, London, ON N6A 3K7, CanadaThis study compares the results of analyzing tsunami simulations that are based on two approaches of characterizing earthquake slips, i.e., uniform (simplistic) and heterogeneous (complex) distributions. The aim of this study is to compare how heterogeneous and uniform distributed data affect the classification of maximum near-shore tsunami amplitudes. Due to the lack of historical earthquake and tsunami data to train the forecasting model, 4000 stochastic tsunami simulations are employed. The focused location is Iwanuma, Japan, where an ocean bottom sensors (OBS) S-net network has been deployed. Multiple linear regression combined with the Akaike information criterion (AIC) is applied to the simulated off-shore wave amplitude data to fit the model. The estimated tsunami amplitude is classified into four levels of warning classes. The performance of the models is quantified by the accuracy of the confusion matrices and is compared with the base model, which only uses earthquake information. The forecasting accuracy can be improved by 30% when the wave amplitude data are used as additional information. The heterogeneous slip-based model reaches a higher accuracy than the uniform-slip based model. The results of this study are particularly valuable for setting up an OBS-based system for monitoring the physical phenomena of tsunamis, and choosing heterogeneous as a preferable slip distribution when tsunami events are simulated.https://www.mdpi.com/2504-3900/87/1/7tsunami forecast classificationocean bottom sensorstochastic tsunami simulation |
| spellingShingle | Yao Li Forecasting Tsunami Hazards Using Ocean Bottom Sensor Data and Classification Predictive Modeling Proceedings tsunami forecast classification ocean bottom sensor stochastic tsunami simulation |
| title | Forecasting Tsunami Hazards Using Ocean Bottom Sensor Data and Classification Predictive Modeling |
| title_full | Forecasting Tsunami Hazards Using Ocean Bottom Sensor Data and Classification Predictive Modeling |
| title_fullStr | Forecasting Tsunami Hazards Using Ocean Bottom Sensor Data and Classification Predictive Modeling |
| title_full_unstemmed | Forecasting Tsunami Hazards Using Ocean Bottom Sensor Data and Classification Predictive Modeling |
| title_short | Forecasting Tsunami Hazards Using Ocean Bottom Sensor Data and Classification Predictive Modeling |
| title_sort | forecasting tsunami hazards using ocean bottom sensor data and classification predictive modeling |
| topic | tsunami forecast classification ocean bottom sensor stochastic tsunami simulation |
| url | https://www.mdpi.com/2504-3900/87/1/7 |
| work_keys_str_mv | AT yaoli forecastingtsunamihazardsusingoceanbottomsensordataandclassificationpredictivemodeling |