Earthquake prediction based on community division

In time-space influence domain, two directed weighted earthquake network are structured based on the earthquake number and the maximum magnitude of Southern California. The earthquake prediction method is proposed based on the minimum edge weight. By CNM (community detection method) community divisi...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Xu, Yanjie [verfasserIn] Ren, Tao [verfasserIn] Liu, Yiyang [verfasserIn] Li, Zhe [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Earthquake network Directed weighted network Prediction Community division

Übergeordnetes Werk:	Enthalten in: Physica / A - Amsterdam : North Holland Publ. Co., 1975, 506, Seite 969-974
Übergeordnetes Werk:	volume:506 ; pages:969-974

DOI / URN:	10.1016/j.physa.2018.05.035

Katalog-ID:	ELV000239925

Internformat


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245	1	0	\|a Earthquake prediction based on community division
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520			\|a In time-space influence domain, two directed weighted earthquake network are structured based on the earthquake number and the maximum magnitude of Southern California. The earthquake prediction method is proposed based on the minimum edge weight. By CNM (community detection method) community division algorithm, the network is divided into several communities and the top 10 communities can be selected according to the number of nodes. Finally, we compare the accuracy of the divided network with the network without community division. The simulation results show that the community division can improve the accuracy of the earthquake prediction.
650		4	\|a Earthquake network
650		4	\|a Directed weighted network
650		4	\|a Prediction
650		4	\|a Community division
700	1		\|a Ren, Tao \|e verfasserin \|4 aut
700	1		\|a Liu, Yiyang \|e verfasserin \|4 aut
700	1		\|a Li, Zhe \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Physica / A \|d Amsterdam : North Holland Publ. Co., 1975 \|g 506, Seite 969-974 \|h Online-Ressource \|w (DE-627)266015077 \|w (DE-600)1466577-3 \|w (DE-576)074959832 \|x 1873-2119 \|7 nnns
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allfields	10.1016/j.physa.2018.05.035 doi (DE-627)ELV000239925 (ELSEVIER)S0378-4371(18)30572-7 DE-627 ger DE-627 rda eng 500 DE-600 33.25 bkl 31.00 bkl Xu, Yanjie verfasserin aut Earthquake prediction based on community division 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In time-space influence domain, two directed weighted earthquake network are structured based on the earthquake number and the maximum magnitude of Southern California. The earthquake prediction method is proposed based on the minimum edge weight. By CNM (community detection method) community division algorithm, the network is divided into several communities and the top 10 communities can be selected according to the number of nodes. Finally, we compare the accuracy of the divided network with the network without community division. The simulation results show that the community division can improve the accuracy of the earthquake prediction. Earthquake network Directed weighted network Prediction Community division Ren, Tao verfasserin aut Liu, Yiyang verfasserin aut Li, Zhe verfasserin aut Enthalten in Physica / A Amsterdam : North Holland Publ. Co., 1975 506, Seite 969-974 Online-Ressource (DE-627)266015077 (DE-600)1466577-3 (DE-576)074959832 1873-2119 nnns volume:506 pages:969-974 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 33.25 Thermodynamik statistische Physik 31.00 Mathematik: Allgemeines AR 506 969-974
spelling	10.1016/j.physa.2018.05.035 doi (DE-627)ELV000239925 (ELSEVIER)S0378-4371(18)30572-7 DE-627 ger DE-627 rda eng 500 DE-600 33.25 bkl 31.00 bkl Xu, Yanjie verfasserin aut Earthquake prediction based on community division 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In time-space influence domain, two directed weighted earthquake network are structured based on the earthquake number and the maximum magnitude of Southern California. The earthquake prediction method is proposed based on the minimum edge weight. By CNM (community detection method) community division algorithm, the network is divided into several communities and the top 10 communities can be selected according to the number of nodes. Finally, we compare the accuracy of the divided network with the network without community division. The simulation results show that the community division can improve the accuracy of the earthquake prediction. Earthquake network Directed weighted network Prediction Community division Ren, Tao verfasserin aut Liu, Yiyang verfasserin aut Li, Zhe verfasserin aut Enthalten in Physica / A Amsterdam : North Holland Publ. Co., 1975 506, Seite 969-974 Online-Ressource (DE-627)266015077 (DE-600)1466577-3 (DE-576)074959832 1873-2119 nnns volume:506 pages:969-974 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 33.25 Thermodynamik statistische Physik 31.00 Mathematik: Allgemeines AR 506 969-974
allfields_unstemmed	10.1016/j.physa.2018.05.035 doi (DE-627)ELV000239925 (ELSEVIER)S0378-4371(18)30572-7 DE-627 ger DE-627 rda eng 500 DE-600 33.25 bkl 31.00 bkl Xu, Yanjie verfasserin aut Earthquake prediction based on community division 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In time-space influence domain, two directed weighted earthquake network are structured based on the earthquake number and the maximum magnitude of Southern California. The earthquake prediction method is proposed based on the minimum edge weight. By CNM (community detection method) community division algorithm, the network is divided into several communities and the top 10 communities can be selected according to the number of nodes. Finally, we compare the accuracy of the divided network with the network without community division. The simulation results show that the community division can improve the accuracy of the earthquake prediction. Earthquake network Directed weighted network Prediction Community division Ren, Tao verfasserin aut Liu, Yiyang verfasserin aut Li, Zhe verfasserin aut Enthalten in Physica / A Amsterdam : North Holland Publ. Co., 1975 506, Seite 969-974 Online-Ressource (DE-627)266015077 (DE-600)1466577-3 (DE-576)074959832 1873-2119 nnns volume:506 pages:969-974 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 33.25 Thermodynamik statistische Physik 31.00 Mathematik: Allgemeines AR 506 969-974
allfieldsGer	10.1016/j.physa.2018.05.035 doi (DE-627)ELV000239925 (ELSEVIER)S0378-4371(18)30572-7 DE-627 ger DE-627 rda eng 500 DE-600 33.25 bkl 31.00 bkl Xu, Yanjie verfasserin aut Earthquake prediction based on community division 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In time-space influence domain, two directed weighted earthquake network are structured based on the earthquake number and the maximum magnitude of Southern California. The earthquake prediction method is proposed based on the minimum edge weight. By CNM (community detection method) community division algorithm, the network is divided into several communities and the top 10 communities can be selected according to the number of nodes. Finally, we compare the accuracy of the divided network with the network without community division. The simulation results show that the community division can improve the accuracy of the earthquake prediction. Earthquake network Directed weighted network Prediction Community division Ren, Tao verfasserin aut Liu, Yiyang verfasserin aut Li, Zhe verfasserin aut Enthalten in Physica / A Amsterdam : North Holland Publ. Co., 1975 506, Seite 969-974 Online-Ressource (DE-627)266015077 (DE-600)1466577-3 (DE-576)074959832 1873-2119 nnns volume:506 pages:969-974 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 33.25 Thermodynamik statistische Physik 31.00 Mathematik: Allgemeines AR 506 969-974
allfieldsSound	10.1016/j.physa.2018.05.035 doi (DE-627)ELV000239925 (ELSEVIER)S0378-4371(18)30572-7 DE-627 ger DE-627 rda eng 500 DE-600 33.25 bkl 31.00 bkl Xu, Yanjie verfasserin aut Earthquake prediction based on community division 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In time-space influence domain, two directed weighted earthquake network are structured based on the earthquake number and the maximum magnitude of Southern California. The earthquake prediction method is proposed based on the minimum edge weight. By CNM (community detection method) community division algorithm, the network is divided into several communities and the top 10 communities can be selected according to the number of nodes. Finally, we compare the accuracy of the divided network with the network without community division. The simulation results show that the community division can improve the accuracy of the earthquake prediction. Earthquake network Directed weighted network Prediction Community division Ren, Tao verfasserin aut Liu, Yiyang verfasserin aut Li, Zhe verfasserin aut Enthalten in Physica / A Amsterdam : North Holland Publ. Co., 1975 506, Seite 969-974 Online-Ressource (DE-627)266015077 (DE-600)1466577-3 (DE-576)074959832 1873-2119 nnns volume:506 pages:969-974 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 33.25 Thermodynamik statistische Physik 31.00 Mathematik: Allgemeines AR 506 969-974
language	English
source	Enthalten in Physica / A 506, Seite 969-974 volume:506 pages:969-974
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authorswithroles_txt_mv	Xu, Yanjie @@aut@@ Ren, Tao @@aut@@ Liu, Yiyang @@aut@@ Li, Zhe @@aut@@
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author	Xu, Yanjie
spellingShingle	Xu, Yanjie ddc 500 bkl 33.25 bkl 31.00 misc Earthquake network misc Directed weighted network misc Prediction misc Community division Earthquake prediction based on community division
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topic_title	500 DE-600 33.25 bkl 31.00 bkl Earthquake prediction based on community division Earthquake network Directed weighted network Prediction Community division
topic	ddc 500 bkl 33.25 bkl 31.00 misc Earthquake network misc Directed weighted network misc Prediction misc Community division
topic_unstemmed	ddc 500 bkl 33.25 bkl 31.00 misc Earthquake network misc Directed weighted network misc Prediction misc Community division
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title	Earthquake prediction based on community division
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title_full	Earthquake prediction based on community division
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title_sort	earthquake prediction based on community division
title_auth	Earthquake prediction based on community division
abstract	In time-space influence domain, two directed weighted earthquake network are structured based on the earthquake number and the maximum magnitude of Southern California. The earthquake prediction method is proposed based on the minimum edge weight. By CNM (community detection method) community division algorithm, the network is divided into several communities and the top 10 communities can be selected according to the number of nodes. Finally, we compare the accuracy of the divided network with the network without community division. The simulation results show that the community division can improve the accuracy of the earthquake prediction.
abstractGer	In time-space influence domain, two directed weighted earthquake network are structured based on the earthquake number and the maximum magnitude of Southern California. The earthquake prediction method is proposed based on the minimum edge weight. By CNM (community detection method) community division algorithm, the network is divided into several communities and the top 10 communities can be selected according to the number of nodes. Finally, we compare the accuracy of the divided network with the network without community division. The simulation results show that the community division can improve the accuracy of the earthquake prediction.
abstract_unstemmed	In time-space influence domain, two directed weighted earthquake network are structured based on the earthquake number and the maximum magnitude of Southern California. The earthquake prediction method is proposed based on the minimum edge weight. By CNM (community detection method) community division algorithm, the network is divided into several communities and the top 10 communities can be selected according to the number of nodes. Finally, we compare the accuracy of the divided network with the network without community division. The simulation results show that the community division can improve the accuracy of the earthquake prediction.
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title_short	Earthquake prediction based on community division
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author2	Ren, Tao Liu, Yiyang Li, Zhe
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doi_str	10.1016/j.physa.2018.05.035
up_date	2024-07-06T17:18:23.811Z
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The earthquake prediction method is proposed based on the minimum edge weight. By CNM (community detection method) community division algorithm, the network is divided into several communities and the top 10 communities can be selected according to the number of nodes. Finally, we compare the accuracy of the divided network with the network without community division. 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Earthquake prediction based on community division

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