Landslide Susceptibility Mapping in Nigeria Using Remote Sensing, GIS, and Machine Learning Models
DOI:
https://doi.org/10.70112/tarce-2024.13.2.4237Keywords:
Landslide Susceptibility, Remote Sensing, GIS, Random Forest, Risk ManagementAbstract
Landslide situations remain a significant concern in Nigeria, as they pose a great risk to human lives, property, and the natural environment, particularly in regions with steep slopes, heavy rainfall, and unfavorable human interference, including deforestation and urbanization. Nigeria’s diverse geographical structure, ranging from urban areas such as Lagos and Abuja to rural regions, underscores the importance of accurate predictions for risk management and avoidance techniques. This research utilized remote sensing and GIS assessment to analyze landslide susceptibility in several regions of Nigeria. Data related to terrain, vegetation, soil moisture, and ground deformation were gathered using high-resolution satellite imagery from sources such as SPOT, ASTER, differential synthetic aperture radar interferometry (D-InSAR), and Landsat TM. GIS data layers included DEM, LULC, soil and geological maps, as well as hydrological maps and data. Methods applied in this study include logistic regression, STAT-R, frequency ratio, and the Random Forest tree-based model. The research produced detailed landslide susceptibility maps for various regions in Nigeria and identified significant factors such as slope, elevation, land use, precipitation, and access to transportation facilities. The Random Forest model demonstrated the most robust predictive capability. The integration of remote sensing with GIS was particularly significant, enhancing the precision of predictions and improving the efficacy of planning and management strategies. By incorporating remote sensing, GIS, and various machine learning algorithms, the researchers have developed a reliable tool for landslide risk prediction and management in Nigeria. Future research should focus on improving data quality and enhancing the generalizability of results to other regions.
References
I. Alcántara-Ayala, “Integrated landslide disaster risk management (ILDRiM): The challenge to avoid the construction of new disaster risk,” Environ. Hazards, vol. 20, no. 3, pp. 323-344, 2021.
M. G. Anderson and E. Holcombe, Community-based landslide risk reduction: Managing disasters in small steps. World Bank Publications, 2013.
S. Arunachalam, R. Sakthivel, and J. R. Murugadoss, “Geospatial modeling of rainfall distribution and groundwater fluctuation using GIS: A case study for hard rock terrain of Upper Ponniyar Watershed, Tamil Nadu, India,” Asian Review of Civil Engineering, vol. 3, no. 1, pp. 1-7, 2014.
L. Ayalew, H. Yamagishi, et al., “The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan,” Geomorphology, vol. 65, no. 1-2, pp. 15-31, 2005.
O. B. Akintan, J. A. Olusola, O. P. Imole, and M. O. Adeyemi, “Geotechnical and GIS-based environmental factors and vulnerability studies of the Okemesi landslide, Nigeria,” Reg. Sustain., vol. 4, no. 3, pp. 249-260, 2023.
O. Bamisaiye, “Landslide in parts of southwestern Nigeria,” SN Appl. Sci., vol. 1, no. 7, p. 745, 2019. [Online]. Available: https://doi.org/10.1007/s42452-019-0757-0
M. Bensaibi, S. Saadi, and A. Boulder, “Automatic detection of seismic damages from high monoscopic spatial images,” Asian J. Civ. Eng., vol. 21, pp. 1039-1050, 2020. [Online]. Available: https://doi.org/10.1007/s42107-020-00260-0
Z. Chang, Z. Du, F. Zhang, F. Huang, J. Chen, W. Li, and Z. Guo, “Landslide susceptibility prediction based on remote sensing images and GIS: Comparisons of supervised and unsupervised machine learning models,” Remote. Sens., vol. 12, p. 502, 2020. [Online]. Available: https://doi.org/10.3390/rs12030502
Charles et al., “Predictive analysis of landslide susceptibility with GIS-DEMs in Uyo, South Eastern Nigeria,” J. Geodesy Geomatics Eng., vol. 1, pp. 42-50, 2014. [Online]. Available: https://doi.org/10.17265/2332-8223/2014.12.006
I. Chirisa, “Peri-urban dynamics and regional planning in Africa: Implications for building healthy cities,” J. Afr. Earth Sci., vol. 55, no. 1, pp. 15-25, 2010.
J. Efiong, D. I. Eni, J. N. Obiefuna, and S. J. Etu, “Geospatial modelling of landslide susceptibility in Cross River State of Nigeria,” Sci. Afr., vol. 14, p. e01032, 2021.
F. Guzzetti, “On the prediction of landslides and their consequences,” in Understanding and Reducing Landslide Disaster Risk: Volume 1 Sendai Landslide Partnerships and Kyoto Landslide Commitment 5th, pp. 3-32, 2021.
A. M. Gbadebo, O. H. Adedeji, and A. S. Edogbo, “GIS-based landslide susceptibility assessment in Eyinoke Hilly Area of Okeigbo, SW, Nigeria,” J. Appl. Sci. Environ. Manage., vol. 22, no. 6, pp. 917-924, 2018.
J. Y. Gwanshak and E. S. Danbauchi, “Geospatial surface area mapping of Bokkos Local Government Area (LGA) of Plateau State of Nigeria,” Int. J. Res. Soc. Sci. Humanit. (IJRSS), vol. 2, no. 2, pp. 1-6, 2021. [Online]. Available: https://doi.org/10.47505/IJRSS
E. Holcombe, M. Anderson, and N. Holm-Nielsen, “Learning by Doing: Community based landslide risk reduction,” pp. 297-302, 2013. [Online]. Available: https://doi.org/10.1007/978-3-642-31313-4_39
Y. Hong, R. Adler, and G. Huffman, “Use of satellite remote sensing data in the mapping of global landslide susceptibility,” Nat. Hazards, vol. 43, pp. 245-256, 2007. [Online]. Available: https://doi.org/10.1007/S11069-006-9104-Z
O. Igwe, S. Onwuka, I. Oha, and O. Nnebedum, “WCoE/IPL projects in West Africa: Application of Landsat ETM+ and ASTER GDEM data in evaluating factors associated with long runout landslides in Benue Hills, North-central Nigeria,” Landslides, vol. 13, pp. 617-627, 2016.
O. Igwe, “The study of the factors controlling rainfall-induced landslides at a failure-prone catchment area in Enugu, Southeastern Nigeria using remote sensing data,” Landslides, vol. 12, no. 5, pp. 1023-1033, 2015.
O. Igwe, B. A. Effiong, M. R. Iweanya, and O. I. Andrew, “Landslide investigation of Ikwette, Obudu local government area of Cross River State, Nigeria,” IOSR J. Appl. Geol. Geophy. (IOSR-JAGG), vol. 3, no. 3, pp. 1-12, 2015.
O. Igwe and C. O. Una, “Landslide impacts and management in Nanka area, Southeast Nigeria,” Geoenvironmental Disasters, vol. 6, no. 1, pp. 1-12, 2019.
M. G. Kitutu, A. Muwanga, J. Poesen, and J. A. Deckers, “Influence of soil properties on landslide occurrences in Bududa District, Eastern Uganda,” Afr. J. Agric. Res., vol. 4, no. 7, pp. 611-620, 2009.
Y. Liu, Y. Liu, Z. Shi, M. López-Vicente, and G. Wu, “Effectiveness of re-vegetated forest and grassland on soil erosion control in the semi-arid Loess Plateau,” Catena, vol. 195, p. 104787, 2020. [Online]. Available: https://doi.org/10.1016/j.catena.2020.104787
G. Metternicht, L. Hurni, and R. Gogu, “Remote sensing of landslides: An analysis of the potential contribution to geo-spatial systems for hazard assessment in mountainous environments,” Remote Sens. Environ., vol. 98, no. 2-3, pp. 284-303, 2005.
M. Rashwan, L. Mohamed, A. Hassan, M. A. Youssef, M. E. M. Sabra, and A. K. Mohamed, “Landslide susceptibility assessment along the Red Sea Coast in Egypt, based on multi-criteria spatial analysis and GIS techniques,” Sci. Afr., vol. 23, p. e02116, 2024.
J. Nyssen et al., “Land degradation in the Ethiopian highlands,” in Landscapes and Landforms of Ethiopia, pp. 369-385, 2015.
U. E. Nnanwuba, S. Qin, O. A. Adeyeye, N. C. Cosmas, J. Yao, S. Qiao, J. S. Jingbo, and E. M. Egwuonwu, “Prediction of spatial likelihood of shallow landslide using GIS-based machine learning in Awgu, Southeast Nigeria,” Sustainability, vol. 14, no. 19, p. 12000, 2022. [Online]. Available: https://doi.org/10.3390/su141912000
V. E. Nwazelibe and J. C. Egbueri, “Geospatial assessment of landslide-prone areas in the southern part of Anambra State, Nigeria using classical statistical models,” Environ. Earth Sci., vol. 83, no. 7, p. 220, 2024.
O. A. Oluwafemi, T. A. Yakubu, M. U. Muhammad, N. Shitta, and A. S. Akinwumiju, “Mapping landslides susceptibility in a traditional Northern Nigerian city,” in Proc. ICA, vol. 1, p. 85, Göttingen, Germany, May 2018.
O. Ozioko and O. Igwe, “GIS-based landslide susceptibility mapping using heuristic and bivariate statistical methods for Iva Valley and environs, Southeast Nigeria,” Environ. Monit. Assess., vol. 192, pp. 1-19, 2020. [Online]. Available: https://doi.org/10.1007/s10661-019-7951-9
S. Plank, J. Singer, C. Minet, and K. Thuro, “Pre-survey suitability evaluation of the differential synthetic aperture radar interferometry method for landslide monitoring,” Int. J. Remote Sens., vol. 33, pp. 6623-6637, 2012. [Online]. Available: https://doi.org/10.1080/01431161.2012.693646
H. Shahabi and M. Hashim, “Landslide susceptibility mapping using GIS-based statistical models and remote sensing data in tropical environment,” Sci. Rep., vol. 5, 2015. [Online]. Available: https://doi.org/10.1038/srep09899
S. N. Ayonghe, E. B. Ntasin, P. Samalang, and C. E. Suh, “The June 27, 2001 landslide on volcanic cones in Limbe, Mount Cameroon, West Africa,” J. Afr. Earth Sci., vol. 39, no. 3-5, pp. 435-439, 2004. [Online]. Available: https://doi.org/10.1016/j.jafrearsci.2004.07.022
C. Udosen, A. I. S. Etok, and A. Essiett, “Predictive analysis of landslide susceptibility with GIS-DEMs in Uyo, South Eastern Nigeria,” J. Geodesy Geomatics Eng., vol. 1, pp. 42-50, 2014.
C. Wu, J. Qiao, and A. Zhu, “Application of 3S technology in landslide forecast,” pp. 165-172, 2006. [Online]. Available: https://doi.org/10.1061/40863(195)16.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Centre for Research and Innovation
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
