Seismic activity prediction using computational intelligence techniques in northern Pakistan

Asim, K. M.; Awais, M.; Martinez-Alvarez, F.; Iqbal, T.

doi:10.1007/s11600-017-0082-1

Artykuł - szczegóły

Tytuł artykułu

Seismic activity prediction using computational intelligence techniques in northern Pakistan

Autorzy

Asim K. M. , Awais M. , Martinez-Alvarez F. , Iqbal T.

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-017-0082-1

Warianty tytułu

Języki publikacji

Abstrakty

Earthquake prediction study is carried out for the region of northern Pakistan. The prediction methodology includes interdisciplinary interaction of seismology and computational intelligence. Eight seismic parameters are computed based upon the past earthquakes. Predictive ability of these eight seismic parameters is evaluated in terms of information gain, which leads to the selection of six parameters to be used in prediction. Multiple computationally intelligent models have been developed for earthquake prediction using selected seismic parameters. These models include feed-forward neural network, recurrent neural network, random forest, multi layer perceptron, radial basis neural network, and support vector machine. The performance of every prediction model is evaluated and McNemar’s statistical test is applied to observe the statistical significance of computational methodologies. Feed-forward neural network shows statistically significant predictions along with accuracy of 75% and positive predictive value of 78% in context of northern Pakistan.

Słowa kluczowe

earthquake prediction computational intelligence seismic activity seismic precursors

przewidywanie trzęsień ziemi inteligencja obliczeniowa aktywność sejsmiczna

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2017

Tom

Vol. 65, no. 5

Strony

919--930

Opis fizyczny

Bibliogr. 56 poz.

Twórcy

autor

Asim K. M.

asim.khawaja@ncp.edu.pk

Centre for Earthquake Studies, National Centre for Physics, Islamabad, Pakistan

autor

Awais M.

Centre for Earthquake Studies, National Centre for Physics, Islamabad, Pakistan

autor

Martinez-Alvarez F.

Department of Computer Science, Pablo de Olavide University, Seville, Spain

autor

Iqbal T.

Centre for Earthquake Studies, National Centre for Physics, Islamabad, Pakistan

Bibliografia

1. Adeli H, Panakkat A (2009) A probabilistic neural network for earthquake magnitude prediction. Neural Netw 22:1018–1024
2. Alarifi ASN, Alarifi NSN, Al-Humidan S (2012) Earthquakes magnitude predication using artificial neural network in northern Red Sea area. J King Saud Univ Sci 24:301–313
3. Ali Z, Qaisar M, Mahmood T, Shah MA, Iqbal T, Serva L, Michetti AM, Burton PW (2009) The Muzaffarabad, Pakistan, earthquake of 8 October 2005: surface faulting, environmental effects and macroseismic intensity. Geol Soc Lond Spec Publ 316(1):155–172
4. Anad M, Dash A, Kumar MSJ, Kesarkar A (2011) Prediction and classification of thunderstorms using artificial neural network. Int J Eng Sci Technol 3(5):4031–4035
5. Arshad J, Zameer A, Khan A (2014) Wind power prediction using genetic programming based ensemble of artificial neural networks (GPeANN). In: Proceedings of the 12th international conference on frontiers of information technology. FIT ’14, pp 257–262
6. Asencio-Cortés G, Martínez-Álvarez F, Morales-Esteban A, Reyes J, Troncoso A (2015a) Improving earthquake prediction with principal component analysis: application to Chile. In: Hybrid artificial intelligent systems: 10th international conference, HAIS 2015, Bilbao, Spain, June 22–24, 2015, Proceedings. Springer International Publishing, pp 393–404
7. Asencio-Cortés G, Martínez-Álvarez F, Troncoso A, Morales-Esteban A (2015b) Medium-large earthquake magnitude prediction in Tokyo with artificial neural networks. Neural Comput Appl 28:1043–1055
8. Asim KM, Idris A, Martínez-Álvarez F, Iqbal T (2016) Short term earthquake prediction in Hindukush region using tree based ensemble learning. In: 2016 International conference on frontiers of information technology (FIT), pp 365–370. doi:10.1109/FIT.2016.073
9. Asim K, Martínez-Álvarez F, Basit A, Iqbal T (2017) Earthquake magnitude prediction in Hindukush region using machine learning techniques. Nat Hazards 85(1):471–486
10. Awais M, Barkat A, Ali A, Rehman K, Ali Zafar W, Iqbal T (2017) Satellite thermal IR and atmospheric radon anomalies associated with Haripur earthquake (Oct 2010; M w 5.2), Pakistan. Adv Space Res. doi:10.1016/j.asr.2017.08.034
11. Barkat A, Ali A, Siddique N, Alam A, Wasim M, Iqbal T (2017) Radon as an earthquake precursor in and around northern Pakistan: a case study. Geochem J 51(4):337–346
12. Bath M (1965) Lateral inhomogeneities in the upper mantle. Tectonophysics 2:483–514
13. Broomhead DS, Lowe D (1988) Radial basis functions, multi-variable functional interpolation and adaptive networks. Technical report, DTIC Document
14. Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge
15. Fausett L (1994) Fundamentals of neural networks: architectures, algorithms, and applications. Prentice-Hall Inc, Upper Saddle River
16. Florido E, Martínez-Álvarez F, Morales-Esteban A, Reyes J, Aznarte JL (2015) Detecting precursory patterns to enhance earthquake prediction in Chile. Comput Geosci 76:112–120
17. Graves A, Mohamed AR, Hinton G (2013) Speech recognition with deep recurrent neural networks. In: 2013 IEEE international conference on acoustics, speech and signal processing, pp 6645–6649. doi:10.1109/ICASSP.2013.6638947
18. Gutenberg B, Richter CF (1954) Seismicity of the Earth. Princeton University, Princeton
19. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. ACM SIGKDD Explor Newsl 11(1):10–18
20. Hassan A, Riaz F, Basit A (2015) A robust classification model with voting based feature selection for diagnosis of epilepsy. In: 2015 IEEE 28th Canadian conference on electrical and computer engineering (CCECE), pp 176–179. doi:10.1109/CCECE.2015.7129181
21. Haykin S (1998) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall PTR, Upper Saddle River, NJ, USA
22. Helmstetter A, Kagan YY, Jackson DD (2007) High-resolution time-independent grid-based forecast for M = 5 earthquakes in California. Seismol Res Lett 78(1):78–86
23. Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20(8):832–844
24. Houlié N, Komorowski JC, de Michele M, Kasereka M, Ciraba H (2006) Early detection of eruptive dykes revealed by normalized difference vegetation index (NDVI) on Mt. Etna and Mt. Nyiragongo. Earth Planet Sci Lett 246(3–4):231–240
25. Idris A, Rizwan M, Khan A (2012) Churn prediction in telecom using Random Forest and PSO based data balancing in combination with various feature selection strategies. Comput Electr Eng 38:1808–1819
26. Ikram A, Qamar U (2015) Developing an expert system based on association rules and predicate logic for earthquake prediction. Knowl Based Syst 75:87–103
27. Jilani Z, Mehmood T, Alam A, Awais M, Iqbal T (2017) Monitoring and descriptive analysis of radon in relation to seismic activity of Northern Pakistan. J Environ Radioact 172:43–51
28. Jouanne F, Awan A, Pêcher A, Kausar A, Mugnier J, Khan I, Khan N, Van Melle J (2014) Present-day deformation of northern Pakistan from Salt Ranges to Karakorum Ranges. J Geophys Res Solid Earth 119(3):2487–2503
29. Kagan YY, Jackson DD, Rong Y (2007) A testable five-year forecast of moderate and large earthquakes in southern California based on smoothed seismicity. Seismol Res Lett 78(1):94–98
30. Kazmi AH, Jan MQ (1997) Geology and tectonics of Pakistan. Graphic Publishers, Karachi
31. Kazmi AH, Rana RA, Asrarullah, (1982) Tectonic map of Pakistan. Government of Pakistan, Ministry of Petroleum and Natural Resources
32. Keilis-Borok V, Kossobokov V (1990) Premonitory activation of earthquake flow: algorithm M8. Phys Earth Planet Inter 61:73–83
33. Keilis-Borok V, Soloviev AA (2003) Nonlinear dynamics of the lithosphere and earthquake prediction. Springer Science & Business Media, Dordrecht
34. Kemal A (1992) Geology and new trends for hydrocarbon exploration in Pakistan. In: Proceedings of international petroleum seminar, 22–24 November 1991, Islamabad, pp 16–57
35. Kossobokov V (1997) User manual for M8 algorithms for earthquake statistics and prediction. IASPEI Softw Libr 6:1–33
36. Kossobokov V, Romashkova L, Panza G, Peresan A (2002) Stabilizing intermediate-term medium-range earthquake predictions. J Seismol Earthq Eng 4:11–19
37. Last M, Rabinowitz N, Leonard G (2016) Predicting the maximum earthquake magnitude from seismic data in Israel and its neighboring countries. PLoS One 11(1):e0146101
38. Levenberg K (1944) A method for the solution of certain non-linear problems in least squares. Q Appl Math 2:164–168
39. Martínez-Álvarez F, Reyes J, Morales-Esteban A, Rubio-Escudero C (2013) Determining the best set of seismicity indicators to predict earthquakes. Two case studies: Chile and the Iberian Peninsula. Knowl Based Syst 50:198–210
40. Matthews BW (1975) Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta Protein Struct 405:442–451
41. Morales-Esteban A, de Justo JL, Martínez-Álvarez F, Azañón JM (2012) Probabilistic method to select calculation accelerograms based on uniform seismic hazard acceleration response spectra. Soil Dyn Earthq Eng 43:174–185
42. Morales-Esteban A, Martínez-Álvarez F, Reyes J (2013) Earthquake prediction in seismogenic areas of the Iberian Peninsula based on computational intelligence. Tectonophysics 593:121–134
43. Murtza I, Abdullah D, Khan A, Arif M, Mirza SM (2017) Cortex-inspired multilayer hierarchy based object detection system using PHOG descriptors and ensemble classification. Vis Comput 33(1):99–112
44. Panakkat A, Adeli H (2007) Neural network models for earthquake magnitude prediction using multiple seismicity indicators. Int J Neural Syst 17(1):13–33
45. Pêcher A, Seeber L, Guillot S, Jouanne F, Kausar A, Latif M, Majid A, Mahéo G, Mugnier JL, Rolland Y, van der Beek P, Van Melle J (2008) Stress field evolution in the northwest Himalayan syntaxis, northern Pakistan. Tectonics 27(6):n/a–n/a. doi:10.1029/2007TC002252
46. Petersen MD, Cao T, Campbell KW, Frankel AD (2007) Time-independent and time-dependent seismic hazard assessment for the state of California: uniform California earthquake rupture forecast model 1.0. Seismol Res Lett 78(1):99–109CrossRefGoogle Scholar
47. Pulinets S, Ouzounov D (2011) Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) model-an unified concept for earthquake precursors validation. J Asian Earth Sci 41(4):371–382
48. Rafiei MH, Adeli H (2017) NEEWS: a novel earthquake early warning model using neural dynamic classification and neural dynamic optimization. Soil Dyn Earthq Eng 100:417–427
49. Ramar K, Mirnalinee TT (2012) An ontological representation for tsunami early warning system. In: IEEE-international conference on advances in engineering, science and management (ICAESM -2012), pp 93–98
50. Rehman K, Ali W, Ali A, Ali A, Barkat A (2017) Shallow and intermediate depth earthquakes in the Hindu Kush region across the Afghan-Pakistan border. J Asian Earth Sci. doi:10.1016/j.jseaes.2017.09.005
51. Reyes J, Morales-Esteban A, Martínez-Álvarez F (2013) Neural networks to predict earthquakes in Chile. Appl Soft Comput 13(2):1314–1328
52. Sercombe WJ, Pivnik DA, Wilson WP, Albertin ML, Beck RA, Stratton MA (1998) Wrench faulting in the northern Pakistan foreland. AAPG Bull 82(11):2003–2030
53. Utsu T, Ogata Y, Matsu’ura RS (1995) The centenary of the Omori formula for a decay law of aftershock activity. J Phys Earth 43:1–33
54. Wang Q, Guo Y, Yu L, Li P (2017) Earthquake prediction based on spatio-temporal data mining: an LSTM network approach. IEEE Trans Emerg Top Comput PP(99):1–1. doi:10.1109/TETC.2017.2699169
55. Williams RJ, Zipser D (1989) A learning algorithm for continually running fully recurrent neural networks. Neural Comput 1(2):270–280
56. Yan T, Pietrafesa LJ, Dickey DA, Gayes PT, Bao S (2015) Seasonal prediction of landfalling hurricanes along Eastern Seaboard of the United States. Int J Climatol 35(9):2647–2653

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018)

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-c34cd939-a187-48d1-9425-9fe6afa4ef20