Original Research

Geographic information systems-based expert system modelling for shoreline sensitivity to oil spill disaster in Rivers State, Nigeria

Olanrewaju Lawal, Charles U. Oyegun
Jàmbá: Journal of Disaster Risk Studies | Vol 9, No 1 | a429 | DOI: https://doi.org/10.4102/jamba.v9i1.429 | © 2017 Olanrewaju Lawal, Charles U. Oyegun | This work is licensed under CC Attribution 4.0
Submitted: 22 January 2017 | Published: 28 July 2017

About the author(s)

Olanrewaju Lawal, Department of Geography and Environmental Management, University of Port Harcourt, Nigeria
Charles U. Oyegun, Department of Geography and Environmental Management, University of Port Harcourt, Nigeria


In the absence of adequate and appropriate actions, hazards often result in disaster. Oil spills across any environment are very hazardous; thus, oil spill contingency planning is pertinent, supported by Environmental Sensitivity Index (ESI) mapping. However, a significant data gap exists across many low- and middle-income countries in aspect of environmental monitoring. This study developed a geographic information system (GIS)-based expert system (ES) for shoreline sensitivity to oiling. It focused on the biophysical attributes of the shoreline with Rivers State as a case study. Data on elevation, soil, relative wave exposure and satellite imageries were collated and used for the development of ES decision rules within GIS. Results show that about 70% of the shoreline are lined with swamp forest/mangroves/nympa palm, and 97% have silt and clay as dominant sediment type. From the ES, six ranks were identified; 61% of the shoreline has a rank of 9 and 19% has a rank of 3 for shoreline sensitivity. A total of 568 km out of the 728 km shoreline is highly sensitive (ranks 7–10). There is a clear indication that the study area is a complex mixture of sensitive environments to oil spill. GIS-based ES with classification rules for shoreline sensitivity represents a rapid and flexible framework for automatic ranking of shoreline sensitivity to oiling. It is expected that this approach would kick-start sensitivity index mapping which is comprehensive and openly available to support disaster risk management around the oil producing regions of the country.


evolutionary studies institute; shoreline sensitivity; expert systems; geographic information system; decision tree; oiling; disaster


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