Geographic information systems-based expert system modelling for shoreline sensitivity to oil spill disaster in Rivers State, Nigeria
| Autor: | Charles Uwadiae Oyegun, Olanrewaju Lawal |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0106 biological sciences
Geographic information system 010504 meteorology & atmospheric sciences lcsh:Risk in industry. Risk management expert systems Management Monitoring Policy and Law Silt 01 natural sciences Swamp evolutionary studies institute Environmental monitoring decision tree geographic information system 0105 earth and related environmental sciences Original Research Shore Hydrology geography Contingency plan shoreline sensitivity geography.geographical_feature_category oiling business.industry 010604 marine biology & hydrobiology Environmental resource management Elevation Decision rule lcsh:HD61 disaster Environmental science business Safety Research |
| Zdroj: | Jàmbá: Journal of Disaster Risk Studies, Volume: 9, Issue: 1, Pages: 1-8, Published: 2017 Jàmbá : Journal of Disaster Risk Studies, Vol 9, Iss 1, Pp e1-e8 (2017) Jàmbá : Journal of Disaster Risk Studies |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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