Heavy Rainfall in Kenya and Its Predictability Using Artificial Neural Networks

James Akuma; Mwendwa Moreen

doi:doi:10.11648/j.awcn.20200602.11

| Peer-Reviewed

Heavy Rainfall in Kenya and Its Predictability Using Artificial Neural Networks

James Akuma, Mwendwa Moreen

Published in Advances in Wireless Communications and Networks (Volume 6, Issue 2)

Received: 15 June 2020 Accepted: 28 June 2020 Published: 31 December 2020

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Heavy rainfall occurs twice a year in the country and lately, thousands of people are always left homeless and hundreds lose life due to floods and landslides where rivers, dams, lakes and sewages overflow enhancing the spread of corona virus in slums. Agricultural products in the farms are also destroyed by floods, affecting agricultural performance to decline as it the key driver of the economy growth. Therefore we used inter-crossed model which was the combination of autoregressive moving average and artificial neural network. Zebiak cane model was also used for selection of variables that were associated to physical processes and testing the network variables. Climate networks were found to be effective tool for more qualitative El Niño Southern Oscillation prediction, by looking at a warning of the oncoming of El Niño when a predestined network attribute surpasses some critical value and also feed forward artificial neural network structures were found to be the first performing structure in terms of normalized root mean squared error at a three month head time prediction. By adding the network variable, we came up with a twelve month lead time prediction with same skill to the predictions at lower set times.

Published in	Advances in Wireless Communications and Networks (Volume 6, Issue 2)
DOI	10.11648/j.awcn.20200602.11
Page(s)	10-13
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Rainfall, Zebiak Cane, Neural Network, Climate Networks, El Nino, Inter-crossed Model

References

[1]	Nieuwolt, S. (1980). The interpolation of rainfall in the Nairobi area. East African Institute for Meteorological Training and Research, Report No. 8, Nairobi.
[2]	Griffiths, J. F. (1972). Climate of Africa. World Survey of Climatology, Elsevier Publishing Co. Vol. 10, Amsterdam - London - New York.
[3]	Funk, C., Dettinger, M. D., Michaelsen, J. C., Verdin, J. P., Brown, M. E., Barlow, M., Hoell, A., (2008). Warming of the Indian Ocean threatens eastern and southern African food security but could be mitigated by agricultural development. Proceedings of the National Academy of Sciences, U.S. 105, p. 11, 081–11, 086, ftp://chg.geog.ucsb.edu/pub/pubs/ PNAS_2008.pdf.
[4]	Williams, A. P., and Funk, Chris, (2010). A westward extension of the tropical Pacific warm pool leads to March through June drying in Kenya and Ethiopia. U.S. Geological Survey Open-File Report 2010–1199, http://pubs.usgs.gov/of/2010/1199.
[5]	Christensen, J. H., Hewitson, B., Busuioc, A., Chen, X. G., Held, I., Jones, R., Kolli, R. K., Kwon, W-T., Laprise, R., Rueda, V. M., Mearns, L., Menéndez, C. G., Räisänen, J., Rinke, A., Sarr, A., Whetton-Christiansen, J. H., Hewitson A., Busuioc, A., and others, (2007). Regional climate projections, Cambridge University Press, Cambridge, U. K., p. 849–940, http://www.ipcc.ch/publications_and_data/ar4/wg1/en/ch11.html.
[6]	Funk, C., Senay, G., Asfaw, A., Verdin, J., Rowland, J., Michaelsen, J., Korecha, D., Choularton, R. (2005). Recent drought tendencies in Ethiopia and equatorial-subtropical eastern Africa. U.S. Agency for International Development, Washington, D.C., ftp://chg.geog.ucsb.edu/pub/pubs/FEWSNET_2005.pdf.
[7]	Philander, S. G. (1990). El Nino, La Nina, and the Southern Oscillation, International Geophysics Series, vol. 46, San Diego.
[8]	Bjerknes J. (1969). Atmospheric Tele-connections from the Equatorial Paciﬁc, Mon. Weather Rev., 97, 163–172, https://doi.org/10.1175/15200493097<0163: ATFTEP>2.3.CO; 2, 1969.
[9]	Wu, A., Hsieh, W. W., and Tang, B. (2006). Neural network forecasts of the tropical Paciﬁc sea surface temperatures, neural networks, 19, 145–154, https://doi.org/0.1016/j.neunet.2006.0.004.
[10]	Feng, Q. Y. and Dijkstra, H. A. (2016). Climate Network Stability Measures of El Niño Variability, Chaos, 27, 035801, https://doi.org/10.1063/1.4971784.
[11]	Fountalis, I., Bracco, A., and Dovrolis, C. (2015). ENSO in CMIP5 simulations: network connectivity from the recent past to the twenty-third century, Climate Dynamic, 45, 511–538, https://doi.org/10.1007/s00382-014-2412-1.
[12]	Meng, J., Fan, J., Ashkenazy, Y., and Havlin, S. (2017). Percolation framework to describe El Niño conditions, Chaos, 27, 1–15, https://doi.org/10.1063/1.4975766.
[13]	Doblas-Reyes F. J., Garcia-Serrano J., Lienert F., Biescas F. P., & Rodrigues L. R. L. (2013). Seasonal climate predictability and forecasting. Status and prospects. WIREs Climate Change, 4, 245–268. https://doi.org/10.1002/wcc.217.
[14]	Adams, R. M., Chen, C. C., McCarl, B. A., Weiher, R. F. (1999). The economic consequences of ENSO events for agriculture. Clim Res. 13: 165–72.
[15]	Von der Heydt, A. S., Nnaﬁe, A., and Dijkstra, H. A. (2011). Cold tongue/Warm pool and ENSO dynamics in the Pliocene, Clim. Past, 7, 903–915. https://doi.org/10.5194/cp-7-903-2011.
[16]	Berezin, Y., Gozolchiani, A., Guez, O., and Havlin, S. (2012). Stability of Climate Networks with Time, Sci. Rep.-UK, 2, 1–8, https://doi.org/10.1038/srep00666.
[17]	Rodríguez-Méndez, V., Eguíluz, V. M., Hernández-García, E., and Ramasco, J. J. (2016). Percolation-based precursors of transitions in extended systems, Sci. Rep.-UK, 6, 29552, https://doi.org/10.1038/srep29552.
[18]	Hibon, M. and Evgeniou, T. (2005). To combine or not to combine: Selecting among forecasts and their combinations, Int. J. Forecasting, 21, 15–24, https://doi.org/10.1016/j.ijforecast.2004.05.002.

Cite This Article

Plain Text BibTeX RIS

APA Style

James Akuma, Mwendwa Moreen. (2020). Heavy Rainfall in Kenya and Its Predictability Using Artificial Neural Networks. Advances in Wireless Communications and Networks, 6(2), 10-13. https://doi.org/10.11648/j.awcn.20200602.11

Copy | Download

ACS Style

James Akuma; Mwendwa Moreen. Heavy Rainfall in Kenya and Its Predictability Using Artificial Neural Networks. Adv. Wirel. Commun. Netw. 2020, 6(2), 10-13. doi: 10.11648/j.awcn.20200602.11

Copy | Download

AMA Style

James Akuma, Mwendwa Moreen. Heavy Rainfall in Kenya and Its Predictability Using Artificial Neural Networks. Adv Wirel Commun Netw. 2020;6(2):10-13. doi: 10.11648/j.awcn.20200602.11

Copy | Download

@article{10.11648/j.awcn.20200602.11,
  author = {James Akuma and Mwendwa Moreen},
  title = {Heavy Rainfall in Kenya and Its Predictability Using Artificial Neural Networks},
  journal = {Advances in Wireless Communications and Networks},
  volume = {6},
  number = {2},
  pages = {10-13},
  doi = {10.11648/j.awcn.20200602.11},
  url = {https://doi.org/10.11648/j.awcn.20200602.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.awcn.20200602.11},
  abstract = {Heavy rainfall occurs twice a year in the country and lately, thousands of people are always left homeless and hundreds lose life due to floods and landslides where rivers, dams, lakes and sewages overflow enhancing the spread of corona virus in slums. Agricultural products in the farms are also destroyed by floods, affecting agricultural performance to decline as it the key driver of the economy growth. Therefore we used inter-crossed model which was the combination of autoregressive moving average and artificial neural network. Zebiak cane model was also used for selection of variables that were associated to physical processes and testing the network variables. Climate networks were found to be effective tool for more qualitative El Niño Southern Oscillation prediction, by looking at a warning of the oncoming of El Niño when a predestined network attribute surpasses some critical value and also feed forward artificial neural network structures were found to be the first performing structure in terms of normalized root mean squared error at a three month head time prediction. By adding the network variable, we came up with a twelve month lead time prediction with same skill to the predictions at lower set times.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Heavy Rainfall in Kenya and Its Predictability Using Artificial Neural Networks
AU  - James Akuma
AU  - Mwendwa Moreen
Y1  - 2020/12/31
PY  - 2020
N1  - https://doi.org/10.11648/j.awcn.20200602.11
DO  - 10.11648/j.awcn.20200602.11
T2  - Advances in Wireless Communications and Networks
JF  - Advances in Wireless Communications and Networks
JO  - Advances in Wireless Communications and Networks
SP  - 10
EP  - 13
PB  - Science Publishing Group
SN  - 2575-596X
UR  - https://doi.org/10.11648/j.awcn.20200602.11
AB  - Heavy rainfall occurs twice a year in the country and lately, thousands of people are always left homeless and hundreds lose life due to floods and landslides where rivers, dams, lakes and sewages overflow enhancing the spread of corona virus in slums. Agricultural products in the farms are also destroyed by floods, affecting agricultural performance to decline as it the key driver of the economy growth. Therefore we used inter-crossed model which was the combination of autoregressive moving average and artificial neural network. Zebiak cane model was also used for selection of variables that were associated to physical processes and testing the network variables. Climate networks were found to be effective tool for more qualitative El Niño Southern Oscillation prediction, by looking at a warning of the oncoming of El Niño when a predestined network attribute surpasses some critical value and also feed forward artificial neural network structures were found to be the first performing structure in terms of normalized root mean squared error at a three month head time prediction. By adding the network variable, we came up with a twelve month lead time prediction with same skill to the predictions at lower set times.
VL  - 6
IS  - 2
ER  -

Copy | Download

Author Information

James Akuma

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Mwendwa Moreen

Natural Resource Management, University of Eldoret, Eldoret, Kenya

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

James Akuma, Mwendwa Moreen. (2020). Heavy Rainfall in Kenya and Its Predictability Using Artificial Neural Networks. Advances in Wireless Communications and Networks, 6(2), 10-13. https://doi.org/10.11648/j.awcn.20200602.11

Copy | Download

ACS Style

James Akuma; Mwendwa Moreen. Heavy Rainfall in Kenya and Its Predictability Using Artificial Neural Networks. Adv. Wirel. Commun. Netw. 2020, 6(2), 10-13. doi: 10.11648/j.awcn.20200602.11

Copy | Download

AMA Style

James Akuma, Mwendwa Moreen. Heavy Rainfall in Kenya and Its Predictability Using Artificial Neural Networks. Adv Wirel Commun Netw. 2020;6(2):10-13. doi: 10.11648/j.awcn.20200602.11

Copy | Download

@article{10.11648/j.awcn.20200602.11,
  author = {James Akuma and Mwendwa Moreen},
  title = {Heavy Rainfall in Kenya and Its Predictability Using Artificial Neural Networks},
  journal = {Advances in Wireless Communications and Networks},
  volume = {6},
  number = {2},
  pages = {10-13},
  doi = {10.11648/j.awcn.20200602.11},
  url = {https://doi.org/10.11648/j.awcn.20200602.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.awcn.20200602.11},
  abstract = {Heavy rainfall occurs twice a year in the country and lately, thousands of people are always left homeless and hundreds lose life due to floods and landslides where rivers, dams, lakes and sewages overflow enhancing the spread of corona virus in slums. Agricultural products in the farms are also destroyed by floods, affecting agricultural performance to decline as it the key driver of the economy growth. Therefore we used inter-crossed model which was the combination of autoregressive moving average and artificial neural network. Zebiak cane model was also used for selection of variables that were associated to physical processes and testing the network variables. Climate networks were found to be effective tool for more qualitative El Niño Southern Oscillation prediction, by looking at a warning of the oncoming of El Niño when a predestined network attribute surpasses some critical value and also feed forward artificial neural network structures were found to be the first performing structure in terms of normalized root mean squared error at a three month head time prediction. By adding the network variable, we came up with a twelve month lead time prediction with same skill to the predictions at lower set times.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Heavy Rainfall in Kenya and Its Predictability Using Artificial Neural Networks
AU  - James Akuma
AU  - Mwendwa Moreen
Y1  - 2020/12/31
PY  - 2020
N1  - https://doi.org/10.11648/j.awcn.20200602.11
DO  - 10.11648/j.awcn.20200602.11
T2  - Advances in Wireless Communications and Networks
JF  - Advances in Wireless Communications and Networks
JO  - Advances in Wireless Communications and Networks
SP  - 10
EP  - 13
PB  - Science Publishing Group
SN  - 2575-596X
UR  - https://doi.org/10.11648/j.awcn.20200602.11
AB  - Heavy rainfall occurs twice a year in the country and lately, thousands of people are always left homeless and hundreds lose life due to floods and landslides where rivers, dams, lakes and sewages overflow enhancing the spread of corona virus in slums. Agricultural products in the farms are also destroyed by floods, affecting agricultural performance to decline as it the key driver of the economy growth. Therefore we used inter-crossed model which was the combination of autoregressive moving average and artificial neural network. Zebiak cane model was also used for selection of variables that were associated to physical processes and testing the network variables. Climate networks were found to be effective tool for more qualitative El Niño Southern Oscillation prediction, by looking at a warning of the oncoming of El Niño when a predestined network attribute surpasses some critical value and also feed forward artificial neural network structures were found to be the first performing structure in terms of normalized root mean squared error at a three month head time prediction. By adding the network variable, we came up with a twelve month lead time prediction with same skill to the predictions at lower set times.
VL  - 6
IS  - 2
ER  -

Copy | Download