




































Asian Review of Environmental 

and Earth Sciences 
ISSN: 2313-8173 
Vol. 1, No. 1, 1-4, 2014 

http://www.asianonlinejournals.com/index.php/AREES 

 
 

 

 

 

 

1 

 

Multi Hazard Risk Assessment Using GIS Techniques in the 

Mbo Area of Nigeria   
 

Okpo O. Ekere
1 

--- Joseph C. Udoh
2
 

 
1,2Department of Geography and Regional Planning Faculty of Social Sciences, University of Uyo, Uyo, Nigeria 

 

Abstract 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
This work is licensed under a Creative Commons Attribution 3.0 License 

Asian Online Journal Publishing Group 

 

1. Introduction 
Natural hazards including erosion and flooding are part and parcel of the environment in which we live. They do 

not discriminate between people or countries. However, no disaster is entirely natural. For an example, although 

erosion is a natural process, it has increased dramatically by human land use, especially industrial agriculture, 

deforestation and urban sprawl. Land that is used for agricultural generally experience a significant greater rate of 

erosion   than that of land under natural vegetation, or land used for sustainable agricultural practices [1]. Equally, 

floods are caused by a variety of factors, both natural and manmade. Apart from the obvious      causes of floods like  

heavy rainfall, melting snow and ice, and frequent storms within a short period of time duration; the common 

practice of humans to build homes  and towns near rivers and other bodies of water have contributed to the disastrous 

consequences of floods.  Generally, increased urbanization, the settlement and industrialization of highly exposed 

regions, the vulnerability of modern technologies and anthropogenic changes in the environment, have led to  

increased levels of disasters globally [2].  Risk is the expected lose as a result of potentially damaging phenomena 

within a given  time period  and within  a given area  [3]. It can be analyzed by assessing three major components – 

the probability of an event with a certain magnitude (hazard); the vulnerability of the elements at risk that are 

exposed to the event to the event with  a certain magnitude (vulnerability); and, the costs relating to these elements at 

risk (risk).  

Risk assessment forms an important input in disaster management, in the design of development plans and in 

emergency response    planning. It combines information on the nature hazard with information on vulnerability of 

the targets. It  is helping to clarify decision making and the development of mitigation strategies [2]. Vulnerability is 

an important concept in hazard research and is central to hazard mitigation strategies. Within the framework of the 

United Nation’s International Decade of natural Disaster reduction  (IDNUR), for an example, vulnerability 

assessments are used  to determine the potential  damage and loss of life  from extreme  natural events. They are 

important  as well in proposing hazard  reduction alternatives where mitigation normally takes the form of structural  

(engineered) approaches  to hazard reduction [4]. 

Most of the data required for disaster management have spatial components and also change  over time. Hence, 

remote sensing and Geographic Information System (GIS) are very useful for analyzing various components of risk. 

While remote sensing help collect environmental data, the GIS helps store, integrate and display the data. Various 

GIS based studies exist on hazard, vulnerability and risk assessment. Examples include, multi-hazard risk assessment 

in urban areas [5]; landslide hazard zonation and analysis  [6] and, vulnerability to environmental hazard  [7]. 

 

The study area Mbo and environs of South Eastern Nigeria are experiencing frequent 

inundation by flood and erosion hence this research is aimed at generating vulnerability 

hazard and risk maps as decision support tools for policy makers. Rainfall, elevation, 

slope, soil association and hydrology were used as input layers to model the hazard layers 

while population density, vegetation and Normalized Difference Vegetation Index (NDVI) 

were used to model the vulnerability layers. Risk maps were generated weighting and 

combining the Hazard and vulnerability layers using the Single Output Map Algebra 

function of Arcmap soft ware. The final multi hazard risk map , derived by combining the 

flood and erosion risk maps shoed that 119.62km
2
  (15.12%), 193.94km

2 
(24.52%), 

292.33km
2
 (36.95%) and 185.20km

2
 (23.41%). The study, using GIS, has revealed an 

objective way of understanding and dealing with the impact of hazards in the study area. 
 

      Keywords: Hazard, Vulnerability, Risk assessment, GIS, AkwaIbom State. 

http://creativecommons.org/licenses/by/3.0/


Asian Review of Environmental and Earth Sciences, 2014, 1(1):1-4 

 

 

 

 

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2. Aim and Objectives 
The aim of this work was to carry out a multi - hazard risk assessment using GIS techniques in Mbo and environs 

in Akwa Ibom State of Nigeria. Specific objectives of the study include: to quantify and model hazard and risk; make 

an inventory of these hazards; map the elements at risk; and, produce a multi – risk map that would inform the 

decisions and developments of effective response measures, for the study area 

 

3. The Study Area  
The study area (Fig.1) is the south eastern Akwa Ibom state comprising four local government areas (LGAs) – 

Mbo, UdungUko, Oron and Urue Offong Oruko. It lies within latitude 4
0
 30

I
 - 5

0
 0

I
 North and longitudes 8

0
 0

I
 - 8

0
 20

I
 

East. With a total landmass of 791.1m
2
, it is bounded on the north by Okobo LGA and on the South by Ibeno LGA,  

to the south by Cross River and to the west Esit Eket and Eket LGAs as shown in Figure 1. It has a population 

density 399.33 people per square kilometers with the people being predominantly fishermen  and farmers. 

The landscape of the study area comprises of generally low lying plain and riverine areas with no portion 

exceeding 175meters above sea level. The area is traversed and criss-crossed by a large number of creeks, rivers, 

streams and canals. The likelihood of erosion and flooding is high in the study area because of the intricately woven 

network of creeks, rivers and inlets. Rich in crude oil and gas, the area is noted for its wetlands, sandy coastal ridge 

barriers, brackish or saline mangroves, fresh water swamp forests as well as lowland rainforests.  

 

4. Methodology 
Analogue maps on vegetation and land use, soil association, hydrology, rainfall, relief and drainage, and 

population density were scanned into a computer, opened on a GIS environment and digitized to create a digital map 

layers that were used for the analysis. Equally, Landsat TM of 2003 with 30m x 30m resolution were used to extract 

the vegetation and Normalized Difference Vegetation Index (NDVI) used too for the analysis. The Digital Elevation 

Model of the study area was used to extract slope, elevation and aspects also used as input in the analysis. Hazard 

and vulnerability analysis are needed to model risk.  

 

4.1. Vulnerability Modeling 
For risk assessment to be valid, certain elements must be critical to that assessment. In carrying out  multi – 

hazard  risk assessment of the study area, what are likely to be affected by the hazard is  of prominence to the study. 

Population; vegetation and land use, and NDVI were identified and used as the vulnerability elements at risk  to the 

hazards. The vulnerability layer was modeled by weighting and combining the elements using the Single Output  

Map Algebra function of Arcmap soft ware thus: 

Vulnerability layer = (population density x 0.4) + (vegetation & Land use x 0.2) x (NDVI x 0.4). 

The vulnerability map displayed in Fig. 2 was the resultant output. 

 

4.2. Multi - Hazard Modeling 
Hazards for this study were limited to flood and erosion. In the absence of data for these hazards, the following 

were used as proxies to model them: slope, rainfall, hydrology soil association and elevation. The elements were 

weighted and used to model the hazard as follow: 

Erosion hazard = (Rainfall x 0.3) + ( Slope x 0.25 ) + (Soil association x 0.20) + (Hydrology x 0.25) 

Flood  hazard = (Rainfall x 0.3) + ( Elevation x 0.25 ) + (soil association x 0.20) + (Hydrology x 0.25) 

 

4.3. Risk Layer 
The final risk layer of the study area (Fig. 5) was gotten by deriving, combining and classifying the erosion and 

flood risk layers of the study area using the Single Output  Map Algebra function of Arcmap soft ware as follows: 

Erosion Risk layer = (Erosion Hazard x.05) + (Vulnerable layer + 0.5) 

Flood  Risk layer = (Flood Hazard x.05) + (Vulnerable layer + 0.5) 

The resultant output were Erosion (Fig. 3) and Flood risk (Fig. 4)  maps respective. These were combined to form the 

multi – risk map as follows: 

 

Multi – hazard  risk map = (Multi hazard layer x.05) + (Vulnerable layer + 0.5). 

 



Asian Review of Environmental and Earth Sciences, 2014, 1(1):1-4 

 

 

 

 

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The vulnerability, hazard and risk maps were all classified into Low, Moderate, high and very high zones. The 

multi risk classification of the zones is displayed on Table 1 to show the areal extent of each zone in the study area. 
 

Table-1.Tale showing the multi risk classes 

Risk class Area (km2) % 

Low 119.62 15.12 

Moderate 193.94 24.52 

High 292.33 36.95 

Very High 185.20 23.41 

Total 791.09 100 
Source: Analysis by researchers. 

 

5. Discussion 
This study generated a multi-hazard risk map of Mbo and environments in a GIS environment using a data base 

created with vegetation and land use, soil association, hydrology, rainfall, relief and drainage, and population density 

data of the study area. Generally, GIS based risk is assessed by combining vulnerability and hazard layers as no risk 

is encountered if there hazard events but no vulnerable population or if there is a vulnerable people but no hazard 

event. Hence risk is a function of a varying degrees  of  hazard and varying degree of vulnerability [8]. 

The study has revealed risk zones in the study area that would help policy makers identify specific risk zones for 

intervention measures. For an example, the High and Very High risk zones being  60.36 % it means that there is need 

for mitigation measures in order to preserve the study area from the harmful effects of erosion and flood. The study 

area being in a low – lying coastal needs such measures as the erosion and flood maps have shown that these hazards 

are found in all the 4 LGA’s located here. The study  has also shown that the areas of moderate (24.52%) and low 

(15.12%) risk have to be preserved  and well  maintained so that erosion and flood does not spread.A combination of 

remote sensing and GIS techniques was used in the risk assessment. Remote sensing LANSAT imagery and DEM 

data has been used in providing timely information on the state of the environment hence making the monitoring and 

detection of environmental hazards  easier than conventional text based methods. The remote sensing and other 

spatial data of the study area were stored and integrated in a GIS data base for subsequent analysis and presentation. 

 

6. Conclusion 
The study has revealed the use of GIS for multi – hazard risk assessment for effective management of the study 

area. Risk, hazard and vulnerability maps were generated that could serve as useful decision support system for 

planners and policy makers. With these, areas for mitigation measures could easily be identified and tackled easily.  

 



Asian Review of Environmental and Earth Sciences, 2014, 1(1):1-4 

 

 

 

 

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7. Dedication 
This paper is dedicated to the memory Okpo O. Ekere, the co-author who died before the publication of this 

work. 

 

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