Top

Published in:

04-06-2019 | Original Article

El Niño-Southern Oscillation forecasting using complex networks analysis of LSTM neural networks

Authors: Clifford Broni-Bedaiko, Ferdinand Apietu Katsriku, Tatsuo Unemi, Masayasu Atsumi, Jamal-Deen Abdulai, Norihiko Shinomiya, Ebenezer Owusu

Published in: Artificial Life and Robotics | Issue 4/2019

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Arguably, El Niño-Southern Oscillation (ENSO) is the most influential climatological phenomenon that has been intensively researched during the past years. Currently, the scientific community knows much about the underlying processes of ENSO phenomenon, however, its predictability for longer horizons, which is very important for human society and the natural environment is still a challenge in the scientific community. Here we show an approach based on using various complex networks metrics extracted from climate networks with long short-term memory neural network to forecast ENSO phenomenon. The results suggest that the 12-network metrics extracted as predictors have predictive power and the potential for forecasting ENSO phenomenon longer multiple steps ahead.

previous article Complex systems approaches to temporal soundspace partitioning in bird communities as a self-organizing phenomenon based on behavioral plasticity

next article Spike-timing-dependent plasticity model for low-frequency pulse waveform

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

World Meteorological Organization (2014) El Niño-Southern Oscillation. WMO-No. 1145, ISBN: 978-92-63-11145-6

Barnston AG, Tippett MK, L’Heureux ML, Li S, DeWitt DG (2012) Skill of real-time seasonal ENSO model predictions during 2002–2011: is our capability increasing? Bull Am Meteorol Soc 93(5):631–651CrossRef

Ludescher J, Gozolchiani A, Bogachev MI, Bunde A, Havlin S, Schellnhuber HJ (2014) Very early warning of next El Niño. Proc Natl Acad Sci USA 111(6):2064–2066

Ben Taieb S, Bontempi G, Atiya AF, Sorjamaa A (2012) A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition. Expert Syst Appl 39(8):7067–7083CrossRef

Zebiak SE, Cane MA (1987) A model El Niñ-Southern Oscillation. Mon Weather Rev 115:2262–2278CrossRef

Gers FA, Schmidhuber J, Cummins F (2000) Learning to forget: continual prediction with LSTM. Neural Comput 12(10):2451–2471CrossRef

Akram Zaytar M, El Amrani C (2016) Sequence to sequence weather forecasting with long short-term memory recurrent neural networks. IJCA 143(11):7–11CrossRef

Zhang Q, Wang H, Dong J, Zhong G, Sun X (2017) Prediction of sea surface temperature using long short-term memory. IEEE Geosci Remote Sens Lett 14(10):1745–1749CrossRef

Lipton ZC, Berkowitz J, Elkan C (2015), A critical review of recurrent neural networks for sequence learning. arXiv:1506.00019v4

10.

Li Y, Yang R, Yang C, Yu M, Hu F, Jiang Y (2017) Leveraging LSTM for rapid intensifications prediction of tropical cyclones. ISPRS Ann Photogramm Remote Sens Spatial Inf Sci IV-4/W2:101–105CrossRef

11.

Hong C, Cho K-D, Kim H-J (2001) The relationship between ENSO events and sea surface temperature in the East (Japan) Sea. Prog Oceanogr 49(1–4):21–40CrossRef

12.

Catto JL, Nicholls N, Jakob C (2012) North Australian sea surface temperatures and the El Niño–Southern Oscillation in observations and models. J Clim 25(14):5011–5029CrossRef

13.

Tsonis AA, Roebber PJ (2004) The architecture of the climate network. Phys A 333:497–504CrossRef

14.

Albert R, Barabási A-L (2002) Statistical mechanics of complex networks. Rev Mod Phys 74(1):47–97MathSciNetCrossRef

15.

Steinhaeuser K, Chawla NV, Ganguly AR (2010) Complex networks in climate science: progress, opportunities and challenges. In: Proceedings of the 2010 conference on intelligent data understanding, USA, pp 16–26

16.

Donner RV, Wiedermann M, Donges JF (2017) Complex network techniques for climatological data analysis. In: Franzke CLE, O’Kane TJ (eds) Nonlinear and stochastic climate dynamics. Cambridge University Press, Cambridge, pp 159–183CrossRef

17.

Zanin M, Papo D, Sousa PA, Menasalvas E, Nicchi A, Kubik E, Boccaletti S (2016) Combining complex networks and data mining: why and how. Phys Rep 635:1–44MathSciNetCrossRef

18.

Steinhaeuser K, Chawla NV, Ganguly AR (2011) Complex networks as a unified framework for descriptive analysis and predictive modeling in climate science. Stat Anal Data Min 4(5):497–511MathSciNetCrossRef

19.

Saha M, Mitra P (2015) Climate network based index discovery for prediction of Indian monsoon. In: Kryszkiewicz M, Bandyopadhyay S, Rybinski H, Pal SK (eds) Pattern recognition and machine intelligence 9124. Springer International Publishing, Cham, pp 554–564CrossRef

20.

Sencan H, Chen Z, Hendrix W, Pansombut T, Semazzi F, Choudhary A, Kumar V, Melechko AV, Samatova NF (2011), Classification of emerging extreme event tracks in multivariate spatio-temporal physical systems using dynamic network structures: application to hurricane track prediction. In: IJCAI’11 proceedings of the twenty-second international joint conference on artificial intelligence, Barcelona, Spain, vol 2, pp 1478–1484

21.

van der Linden JH, Narsilio GA, Tordesillas A (2016) Machine learning framework for analysis of transport through complex networks in porous, granular media: a focus on permeability. Phys Rev E 94(2–1):022904CrossRef

22.

Jamal W, Das S, Oprescu I-A, Maharatna K, Apicella F, Sicca F (2014) Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates. J Neural Eng 11(4):046019CrossRef

23.

Dijkstra HA (2006) The ENSO phenomenon: theory and mechanisms. Adv Geosci 6:3–15CrossRef

24.

Landsea CW, Knaff JA (2000) How much skill was there in forecasting the very strong 1997–1998 El Niño? Bull Am Meteorol Soc 81:2107–2119CrossRef

25.

Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Leetmaa A, Reynolds B, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Roy J, Dennis J (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77:437–472CrossRef

26.

Huang B, Thorne PW, Banzon VF, Boyer T, Chepurin G, Lawrimore JH, Menne MJ, Smith TM, Vose RS, Zhang H-M (2017) Extended reconstructed sea surface temperature, version 5: upgrades, validations, and intercomparisons. J Clim 30(20):8179–8205CrossRef

27.

Radebach A, Donner RV, Runge J, Donges JF, Kurths J (2013) Disentangling different types of El Niño episodes by evolving climate network analysis. Phys Rev E Stat Nonlin Soft Matter Phys 88(5):052807CrossRef

28.

Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780CrossRef

29.

Gers FA, Eck D, Schmidhuber J (2002) Applying LSTM to time series predictable through time-window approaches. In: Tagliaferri R, Marinaro M (eds) Neural nets WIRN Vietri-01. Springer, London, London, pp 193–200CrossRef

30.

Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. arXiv:1409.3215

31.

Donges JF, Zou Y, Marwan N, Kurths J (2009) Complex networks in climate dynamics: comparing linear and nonlinear network construction methods. Eur Phys J Spec Topics 174(1):157–179CrossRef

32.

Donges JF, Heitzig J, Beronov B, Wiedermann M, Runge J, Feng QY, Tupikina L, Stolbova V, Donner RV, Marwan N, Dijkstra HA, Kurths J (2015) Unified functional network and nonlinear time series analysis for complex systems science: the pyunicorn package. Chaos 25(11):113101MathSciNetCrossRef

33.

LdaF Costa, Rodrigues FA, Travieso G, Villas Boas PR (2007) Characterization of complex networks: a survey of measurements. Adv Phys 56(1):167–242CrossRef

34.

Barnston A (2014) How good have ENSO forecasts been lately?. National Oceanic and Atmospheric Administration (NOAA). https://www.climate.gov/news-features/blogs/enso/how-good-have-enso-forecasts-been-lately. Accessed 20 Mar 2017

Title: El Niño-Southern Oscillation forecasting using complex networks analysis of LSTM neural networks
Authors: Clifford Broni-Bedaiko
Ferdinand Apietu Katsriku
Tatsuo Unemi
Masayasu Atsumi
Jamal-Deen Abdulai
Norihiko Shinomiya
Ebenezer Owusu
Publication date: 04-06-2019
Publisher: Springer Japan
Published in: Artificial Life and Robotics / Issue 4/2019
Print ISSN: 1433-5298
Electronic ISSN: 1614-7456
DOI: https://doi.org/10.1007/s10015-019-00540-2

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Other articles of this Issue 4/2019

Analysis of push-forward model for swarm-like collective motions

A modified cascaded neuro-computational model applied to recognition of connected spoken Japanese prefecture words

The effect of different types of acupuncture manipulations on shoulder pain and cardiovascular circulation dynamics

Neural network-based construction of inverse kinematics model for serial redundant manipulators

Spike-timing-dependent plasticity model for low-frequency pulse waveform

Wireless sensor monitoring system of Canadian Poplar Forests based on Internet of Things