Tsunami arrival time detection system applicable to discontinuous time series data with outliers

Lee, Jun-Whan; Park, Sun-Cheon; Lee, Duk Kee; Lee, Jong Ho

doi:10.5194/nhess-16-2603-2016

Articles | Volume 16, issue 12

https://doi.org/10.5194/nhess-16-2603-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/nhess-16-2603-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 16, issue 12

Research article

|

09 Dec 2016

Research article |

| 09 Dec 2016

Tsunami arrival time detection system applicable to discontinuous time series data with outliers

Jun-Whan Lee, Sun-Cheon Park, Duk Kee Lee, and Jong Ho Lee

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Views and downloads (calculated since 07 Jun 2016)

Month	HTML	PDF	XML	Total
Jun 2016	41	21	2	64
Jul 2016	11	3	1	15
Aug 2016	21	1	0	22
Sep 2016	4	2	0	6
Oct 2016	9	2	1	12
Nov 2016	5	2	1	8
Dec 2016	74	13	2	89
Jan 2017	49	14	1	64
Feb 2017	17	5	5	27
Mar 2017	19	12	0	31
Apr 2017	12	4	0	16
May 2017	18	5	0	23
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Dec 2018	9	16	0	25
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Feb 2019	8	11	2	21
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Apr 2019	8	13	1	22
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Jul 2019	7	16	1	24
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Jan 2020	9	5	3	17
Feb 2020	5	3	0	8
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Jan 2021	14	7	0	21
Feb 2021	11	8	0	19
Mar 2021	8	3	0	11
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Jul 2022	9	4	1	14
Aug 2022	11	5	1	17
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Oct 2022	6	8	0	14
Nov 2022	18	13	0	31
Dec 2022	3	7	0	10
Jan 2023	8	7	0	15
Feb 2023	10	15	0	25
Mar 2023	7	21	0	28
Apr 2023	1	10	0	11
May 2023	23	11	3	37
Jun 2023	27	9	0	36
Jul 2023	12	8	0	20
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May 2024	11	9	6	26
Jun 2024	17	11	3	31
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Dec 2024	9	7	1	17
Jan 2025	10	5	0	15
Feb 2025	15	5	1	21
Mar 2025	8	9	0	17
Apr 2025	7	12	0	19
May 2025	13	14	0	27
Jun 2025	27	18	3	48
Jul 2025	14	35	1	50
Aug 2025	8	8	2	18
Sep 2025	18	20	0	38
Oct 2025	13	18	1	32
Nov 2025	14	32	8	54
Dec 2025	16	33	4	53
Jan 2026	28	17	7	52
Feb 2026	15	17	0	32
Mar 2026	14	20	4	38
Apr 2026	3	3	0	6

Cumulative views and downloads (calculated since 07 Jun 2016)

Month	HTML	PDF	XML	Total
Jun 2016	41	21	2	64
Jul 2016	11	3	1	15
Aug 2016	21	1	0	22
Sep 2016	4	2	0	6
Oct 2016	9	2	1	12
Nov 2016	5	2	1	8
Dec 2016	74	13	2	89
Jan 2017	49	14	1	64
Feb 2017	17	5	5	27
Mar 2017	19	12	0	31
Apr 2017	12	4	0	16
May 2017	18	5	0	23
Jun 2017	16	6	0	22
Jul 2017	14	10	0	24
Aug 2017	8	12	2	22
Sep 2017	15	10	0	25
Oct 2017	4	5	1	10
Nov 2017	10	7	0	17
Dec 2017	14	14	5	33
Jan 2018	9	9	1	19
Feb 2018	5	6	0	11
Mar 2018	9	5	1	15
Apr 2018	7	6	0	13
May 2018	7	6	0	13
Jun 2018	11	16	1	28
Jul 2018	8	15	2	25
Aug 2018	17	16	1	34
Sep 2018	13	9	1	23
Oct 2018	8	18	0	26
Nov 2018	17	21	0	38
Dec 2018	9	16	0	25
Jan 2019	7	11	1	19
Feb 2019	8	11	2	21
Mar 2019	6	14	1	21
Apr 2019	8	13	1	22
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Jun 2019	6	19	0	25
Jul 2019	7	16	1	24
Aug 2019	4	17	0	21
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Oct 2019	8	8	2	18
Nov 2019	9	8	0	17
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Jan 2020	9	5	3	17
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Mar 2023	7	21	0	28
Apr 2023	1	10	0	11
May 2023	23	11	3	37
Jun 2023	27	9	0	36
Jul 2023	12	8	0	20
Aug 2023	10	4	2	16
Sep 2023	10	6	0	16
Oct 2023	8	13	0	21
Nov 2023	8	6	0	14
Dec 2023	8	13	4	25
Jan 2024	3	7	1	11
Feb 2024	6	9	1	16
Mar 2024	6	4	0	10
Apr 2024	18	9	2	29
May 2024	11	9	6	26
Jun 2024	17	11	3	31
Jul 2024	13	9	3	25
Aug 2024	19	4	5	28
Sep 2024	9	11	1	21
Oct 2024	10	16	1	27
Nov 2024	3	6	0	9
Dec 2024	9	7	1	17
Jan 2025	10	5	0	15
Feb 2025	15	5	1	21
Mar 2025	8	9	0	17
Apr 2025	7	12	0	19
May 2025	13	14	0	27
Jun 2025	27	18	3	48
Jul 2025	14	35	1	50
Aug 2025	8	8	2	18
Sep 2025	18	20	0	38
Oct 2025	13	18	1	32
Nov 2025	14	32	8	54
Dec 2025	16	33	4	53
Jan 2026	28	17	7	52
Feb 2026	15	17	0	32
Mar 2026	14	20	4	38
Apr 2026	3	3	0	6

Latest update: 19 Apr 2026

Short summary

Water level sensors often experience unexpected gaps and outliers that cause major difficulties in detecting tsunamis. Thus, we propose a tsunami arrival time detection system applicable to discontinuous time-series data with outliers. We want to stress that the efficiency and simplicity of the system enable its wide application in tsunami monitoring areas.