Earthquakes and tremors are a common occurrence throughout the world, mostly in China, Japan and Indonesia. In Kenya, we experience a lot of tremors and landslides during the rainy seasons that have extensive negative social, economic, and environmental impacts. These damages include loss of human life, financial loss and destruction of infrastructure. This becomes a lagging factor towards achieving the Vision 2030 and Sustainable Development Goals (SDGs). This study used secondary data, obtained from World Wide Standardized Seismograph Station (WWSSSN) in Kilimambogo. Stochastic artificial neural network was adopted to identify prone areas to the said natural disasters, measure the socioeconomic impacts and build a predictive model for landslides, tremor and earthquakes in Kenya. It was evident that landslides are destructive in nature through observable measurable impacts on people. They increase the social and economic burden on the affected people. 64.76% of the measurable impacts affect human beings directly while the rest affect cattle and crops. Along the Great rift valley, most earthquakes and landslides took place. This is attributed to the active seismic activities. Kenya experiences earthquakes of magnitude m < 4. Our model achieved root mean square of 0.435. Furthermore, we got R2=0.80 for testing dataset. This implied that 80% of data was trainable by the model. Therefore, the predictive neural network model is efficient and accurate in forecasting, and more importantly is a good fit model.
| Published in | International Journal of Data Science and Analysis (Volume 7, Issue 5) |
| DOI | 10.11648/j.ijdsa.20210705.11 |
| Page(s) | 117-121 |
| 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), 2021. Published by Science Publishing Group |
Earthquake, Landslide, Neural Network
| [1] | Adeli, H. and Panakkat, A. (2009). A probabilistic neural network for earthquake magnitude prediction. Neural networks, 22 (7): 1018–1024. |
| [2] | Ambraseys, N. (1991). Earthquake hazard in the kenya rift: the subukia earthquake 1928. Geophysical Journal International, 105 (1): 253–269. |
| [3] | Asim, K. M., Idris, A., Iqbal, T., and Martínez-Álvarez, F. (2018). Earthquake prediction model using support vector regressor and hybrid neural networks. PloS one, 13 (7): e0199004. |
| [4] | Bennett, G. L., Miller, S. R., Roering, J. J., and Schmidt, D. A. (2016). Landslides, threshold slopes, and the survival of relict terrain in the wake of the mendocino triple junction. Geology, 44 (5): 363–366. |
| [5] | Gurney, K. (1997). An introduction to neural networks. CRC press. |
| [6] | He, K., Zhang, X., Ren, S., and Sun, J. (2015). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE international conference on computer vision, pages 1026–1034. |
| [7] | Jafri, Y. Z., Sami, M., Waseem, A., Murtaza, G., and Akbar, S. (2012). Stochastic approaches for time series forecasting of rate of dust fall: A case study of northwest of Balochistan, Pakistan. International Journal of Physical Sciences, 7 (4): 676–686. |
| [8] | Kajitani, Y., Chang, S. E., and Tatano, H. (2013). Economic impacts of the 2011 tohoku-oki earthquake and tsunami. Earthquake Spectra, 29 (1_suppl): 457–478. |
| [9] | Koubi, V. (2018). Climate change and disasters: Sustainable development impacts, trends, interlinkages with migration and conflicts, and likely scenarios. |
| [10] | Lee, S. and Evangelista, D. (2006). Earthquake-induced landslide-susceptibility mapping using an artificial neural network. Natural Hazards and Earth System Sciences, 6 (5): 687–695. |
| [11] | Moreiras, S. M. (2005). Climatic effect of enso associated with landslide occurrence in the central andes, Mendoza province, Argentina. Landslides, 2 (1): 53–59. |
| [12] | Mulwa, J. K., Kimata, F., Suzuki, S., and Kuria, Z. N. (2014). The seismicity in kenya (east africa) for the period 1906–2010: A review. Journal of African Earth Sciences, 89: 72–78. |
| [13] | Panakkat, A. and Adeli, H. (2007). Neural network models for earthquake magnitude prediction using multiple seismicity indicators. International journal of neural systems, 17 (01): 13–33. |
| [14] | Petley, D. N., Hearn, G. J., Hart, A., Rosser, N. J., Dunning, S. A., Oven, K., and Mitchell, W. A. (2007). Trends in landslide occurrence in Nepal. Natural hazards, 43 (1): 23–44. |
| [15] | Wu, J., Li, N., Hallegatte, S., Shi, P., Hu, A., and Liu, X. (2012). Regional indirect economic impact evaluation of the 2008 Wenchuan earthquake. Environmental Earth Sciences, 65 (1): 161–172. |
| [16] | Zarola, A. and Sil, A. (2017). Artificial neural networks (ann) and stochastic techniques to estimate earthquake occurrences in northeast region of India. Annals of Geophysics, 60 (6): 0658. |
APA Style
Moses Kung'u Githu, Edwin Kagereki, Serah Munyua. (2021). Seismic Detection Model Using Machine Learning to Protect the Public from Landslide and Earthquake Disasters in Kenya. International Journal of Data Science and Analysis, 7(5), 117-121. https://doi.org/10.11648/j.ijdsa.20210705.11
ACS Style
Moses Kung'u Githu; Edwin Kagereki; Serah Munyua. Seismic Detection Model Using Machine Learning to Protect the Public from Landslide and Earthquake Disasters in Kenya. Int. J. Data Sci. Anal. 2021, 7(5), 117-121. doi: 10.11648/j.ijdsa.20210705.11
AMA Style
Moses Kung'u Githu, Edwin Kagereki, Serah Munyua. Seismic Detection Model Using Machine Learning to Protect the Public from Landslide and Earthquake Disasters in Kenya. Int J Data Sci Anal. 2021;7(5):117-121. doi: 10.11648/j.ijdsa.20210705.11
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Download@article{10.11648/j.ijdsa.20210705.11,
author = {Moses Kung'u Githu and Edwin Kagereki and Serah Munyua},
title = {Seismic Detection Model Using Machine Learning to Protect the Public from Landslide and Earthquake Disasters in Kenya},
journal = {International Journal of Data Science and Analysis},
volume = {7},
number = {5},
pages = {117-121},
doi = {10.11648/j.ijdsa.20210705.11},
url = {https://doi.org/10.11648/j.ijdsa.20210705.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20210705.11},
abstract = {Earthquakes and tremors are a common occurrence throughout the world, mostly in China, Japan and Indonesia. In Kenya, we experience a lot of tremors and landslides during the rainy seasons that have extensive negative social, economic, and environmental impacts. These damages include loss of human life, financial loss and destruction of infrastructure. This becomes a lagging factor towards achieving the Vision 2030 and Sustainable Development Goals (SDGs). This study used secondary data, obtained from World Wide Standardized Seismograph Station (WWSSSN) in Kilimambogo. Stochastic artificial neural network was adopted to identify prone areas to the said natural disasters, measure the socioeconomic impacts and build a predictive model for landslides, tremor and earthquakes in Kenya. It was evident that landslides are destructive in nature through observable measurable impacts on people. They increase the social and economic burden on the affected people. 64.76% of the measurable impacts affect human beings directly while the rest affect cattle and crops. Along the Great rift valley, most earthquakes and landslides took place. This is attributed to the active seismic activities. Kenya experiences earthquakes of magnitude m 2=0.80 for testing dataset. This implied that 80% of data was trainable by the model. Therefore, the predictive neural network model is efficient and accurate in forecasting, and more importantly is a good fit model.},
year = {2021}
}
TY - JOUR T1 - Seismic Detection Model Using Machine Learning to Protect the Public from Landslide and Earthquake Disasters in Kenya AU - Moses Kung'u Githu AU - Edwin Kagereki AU - Serah Munyua Y1 - 2021/10/15 PY - 2021 N1 - https://doi.org/10.11648/j.ijdsa.20210705.11 DO - 10.11648/j.ijdsa.20210705.11 T2 - International Journal of Data Science and Analysis JF - International Journal of Data Science and Analysis JO - International Journal of Data Science and Analysis SP - 117 EP - 121 PB - Science Publishing Group SN - 2575-1891 UR - https://doi.org/10.11648/j.ijdsa.20210705.11 AB - Earthquakes and tremors are a common occurrence throughout the world, mostly in China, Japan and Indonesia. In Kenya, we experience a lot of tremors and landslides during the rainy seasons that have extensive negative social, economic, and environmental impacts. These damages include loss of human life, financial loss and destruction of infrastructure. This becomes a lagging factor towards achieving the Vision 2030 and Sustainable Development Goals (SDGs). This study used secondary data, obtained from World Wide Standardized Seismograph Station (WWSSSN) in Kilimambogo. Stochastic artificial neural network was adopted to identify prone areas to the said natural disasters, measure the socioeconomic impacts and build a predictive model for landslides, tremor and earthquakes in Kenya. It was evident that landslides are destructive in nature through observable measurable impacts on people. They increase the social and economic burden on the affected people. 64.76% of the measurable impacts affect human beings directly while the rest affect cattle and crops. Along the Great rift valley, most earthquakes and landslides took place. This is attributed to the active seismic activities. Kenya experiences earthquakes of magnitude m 2=0.80 for testing dataset. This implied that 80% of data was trainable by the model. Therefore, the predictive neural network model is efficient and accurate in forecasting, and more importantly is a good fit model. VL - 7 IS - 5 ER -
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