Mapping Landslide Susceptibility Areas in Onitsha Metropolis of Anambra State Nigeria Using GIS
Abstract
Landslides constitute one of the most destructive geomorphic processes, exerting far-reaching socio-economic and environmental impacts in susceptible regions worldwide. The frequency and magnitude of landslide occurrences have increased significantly in recent decades, driven by evolving climatic patterns and intensifying anthropogenic interventions on environmentally sensitive terrains. This study is aimed at a GIS based landslide susceptibility mapping of Onitsha north local government area, Anambra State Nigeria. The objectives of the study are to; identify and characterize the key physical factors contributing to landslide occurrences in Onitsha Metropolis; evaluate and rank these factors according to their level of influence; systematically classify the factors into distinct levels of susceptibility and delineate and map the landslide-susceptible areas within Onitsha Metropolis. The methodology incorporated seven conditioning factors: slope, elevation, rainfall, aspect, soil, curvature and geology. Spatial datasets were standardized, reclassified, and weighted using analytical hierarchy process (AHP) to produce a composite landslide susceptibility index. The results generated through Weighted Linear Combination (WLC) analysis delineated four risk zones: Very Low Risk, Low Risk, Moderate Risk and High Risk. The landslide susceptibility analysis showed that very low-risk zones constituted only 0.36% (0.147 km²) of Onitsha Metropolis, primarily located on flat valley bottoms and depositional surfaces where slope-driven processes are minimal. Low-risk zones covered 45.40% (18.747 km²), occurring on gently undulating terrain with limited slope-induced gravitational forces, offering relative safety for development when supported by proper drainage and slope management. Moderate-risk zones accounted for the largest proportion, covering 50.82% (20.985 km²). These areas, found on transitional slopes and concave landforms, are moderately stable but prone to landslides under persistent rainfall or anthropogenic alteration. High-risk zones spanned 3.42% (1.414 km²), concentrated along steep slopes and poorly drained hill flanks characterized by topographic instability. Spatial overlay with settlement areas revealed that GRA, Okpoko, Army Barracks, and Nkwelle were located within very high-risk zones, necessitating urgent mitigation measures. Conversely, Trans Nkisi, Umuaroli, Omogba, Fegge, and Odoakpu were situated in moderate-risk zones, where preventive land-use planning and slope monitoring are recommended.
How to Cite This Article
Nkanu BI, Emengini EJ, Idhoko KE (2026). Mapping Landslide Susceptibility Areas in Onitsha Metropolis of Anambra State Nigeria Using GIS . International Journal of Multidisciplinary Research and Growth Evaluation (IJMRGE), 7(3), 452-471.
References
- 1. Amah VE, Egboka BCE, Okeke HC, Nwankwoala HO. GIS and statistical based assessment of landslide susceptibility in southeastern Nigeria. Environ Earth Sci. 2020;79(14):1–16.
- 2. Ayadiuno RU, Ndulue EL, Mozie AT, Ndichie CC. Machine learning approaches for landslide susceptibility mapping in southeastern Nigeria. Arab J Geosci. 2021;14(18):1–20.
- 3. Hall JW, Huang H, Timokhin D, Hammel N. Global natural hazard risk assessment and urban resilience analysis using geospatial technologies. Int J Disaster Risk Reduct. 2020;50:101714.
- 4. Ifeka AC, Akinbobola A. Rainfall variability and climatic characteristics of southeastern Nigeria. J Geogr Reg Plan. 2015;8(5):98–107.
- 5. Luo X, Chen G, Zhang L, Wang H. Urbanization effects on slope instability and hydrological response in rapidly developing cities. Sustainability. 2022;14(9):5321.
- 6. Metternicht G, Hurni L, Gogu R. Remote sensing of landslides: An analysis of the potential contribution to geospatial systems for hazard assessment in mountainous environments. Remote Sens Environ. 2005;98(2–3):284–303.
- 7. Nebeokike SC, Igwe O, Egbueri JC, Ifediegwu SI. Landslide susceptibility modelling using Naïve Bayes algorithm and GIS techniques in southeastern Nigeria. Model Earth Syst Environ. 2020;6(4):2345–2360.
- 8. Nnadi EO. Climatic variability and rainfall characteristics in Anambra State, Nigeria. J Environ Earth Sci. 2019;9(4):45–56.
- 9. Nnanwuba CC, Nwosu JI, Okeke FI, Ezeh CU. Comparative analysis of machine learning techniques for landslide susceptibility mapping in southeastern Nigeria. Geocarto Int. 2022;37(12):3501–3520.
- 10. Obeta MC. Rainfall intensity and environmental implications in southeastern Nigeria. J Hydrol Reg Stud. 2022;41:101066.
- 11. Okeke FC, Eze JN, Nwankwo CO. Urban climate characteristics and rainfall variability in Onitsha Metropolis, Nigeria. Afr J Environ Sci Technol. 2019;13(6):221–232. International Journal of Multidisciplinary Research and Growth Evaluation www. allmultidisciplinaryjournal. com 471 | P a g e
- 12. Oloruntade AJ. Seasonal rainfall variability and runoff generation in southeastern Nigeria. Hydrol Sci J. 2018;63(11):1657–1670.
- 13. Ozioko RE, Igwe O. GIS-based heuristic and bivariate statistical modelling of landslide susceptibility in Iva Valley, southeastern Nigeria. Environ Monit Assess. 2020;192(9):1–19.
- 14. Saaty TL. Decision making with dependence and feedback: The analytic network process. 2nd ed. Pittsburgh: RWS Publications; 2001.
- 15. Ulakpa ROE, Okwu VI, Chukwu KE, Eyankware MO. Application of GIS and remote sensing in landslide susceptibility mapping and hazard assessment. Int J Sci Technol Res. 2020;9(3):5404–5412.