Assessing Potential Flood Risk Zones Around AGULU Lake Using High Resolution Satellite Image and Digital Terrain Model (DTM) Ekweonu, F.K1, Igbokwe, J

Research Article Volume 9 Issue No.8 Assessing Potential Flood Risk Zones around AGULU Lake using High Resolution Satellite Image and Digital Terrain Model (DTM) Ekweonu, F.K1, Igbokwe, J. I2, Baywood, C. N3 Department of Surveying & Geoinformatics Nnamdi Azikiwe University Awka, Nigeria

Abstract: The aim of this thesis is to assess potential flood risk zones around AGULU Lake using High Resolution Satellite image and Digital Terrain Model DTM. The study area is AGULU Lake, in Agulu town under Anaocha Local Government Area of Anambra state, in south east Nigeria. AGULU Lake lies within the Anambra Basin, where monstrous effects of flood, soil and gully erosion and landslides destroy lives and property. The objectives of the research are to map the landcover/landuse pattern around Agulu. Access the accuracy of the resultant landcover/landuse map using kappa statistics. To model the risk zones around Agulu lake using elevation, slope, flow accumulation and distance to drainage channels. To delineate the areas affected by the various risk zones around Agulu lake. The methodology flow chart shows different stages and analysis involved in delineating flood zone area within the study area, data requirements includes QuickBird 2017 imagery covering the study area, 2meter Digital Terrain model covering the study area and shape file of 2km by 2km land area covering Agulu Lake and Environ. Software and hardware requirements that were used in carrying out such study, the software requirement includes ArcGIS 10.2, ENVI 5.2, Microsoft excel. The methodology adopted image processing and classification using object-based classifier into 5 Class categories, Agulu Lake, Residential Area, Farm Land, Low Forest and Road. Ground truthing picking of sample points for accuracy assessment and to identify the features on ground to determine the landcover/ landuse of the study area. The hydrological elements were created from the DTM after filling the void. These elements include flow accumulation, Slope, Elevation and Drainage Network. The different data sets were reclassified for information generation such as DTM and slope creation, all data was integrated in a GIS environment using multi-criterion decision tools (WLC) Weight Linear Combination for preparation of flood risk maps into very high risk, high risk, moderate risk and low risk zones using equal interval of separation based on elevation. The results indicated that very high-risk zone occupied 22.53% of the entire study area, covering an area of 96.88 hectares, while high risk zone occupied 23.80%, covered an area of 102.34 hectares. Moderate risk zone occupied 25.61%, covering 110.13 hectares while low risk zone occupied 28.04% covering an area of 120.55 hectares. The very high-risk zone also covers 6 buildings and 8 plots of farmland, these features are particularly at very high risk of potential flooding in the study area. However, it is recommended that adequate measures be taken by the state government and local government to inform and relocate buildings in area of very high-risk zones. The study clearly shows that satellite remote sensing data have emerged as a viable alternative for assessing flood risk. Different techniques exist that manage and analyze the impact of flood some of these techniques have not been effective in management of flood disaster. Remote sensing technique presents itself as an effective and efficient means of managing flood disaster.

Keywords: Flooding, Flood Risk, Risk Zones, DTM.

I. INTRODUCTION Ayhan 2006). Floods are among the most recurring and devastating natural hazards, impacting human lives and Flood is considered to be one the most devastating and causing severe economic damage throughout the world. It is frequently occurring natural hazards in the world (Komolafe et understood that flood risks will not subside in the future, and al 2015). The devastating effects of flood are recorded in terms with the onset of climate change, flood intensity and frequency of mortality and economic risk by both national and will threaten many regions of the world. In Nigeria, flood international agencies (Akanni and Bilesanmi, 2011). Although accounts for the highest occurring natural hazards, with great research claims that the mortality rate is reducing globally due consequences on the life and property (Aderogba, 2012). Due to the established early warning systems in some countries to the torrential over the Niger River and its tributaries located (mostly the developed), but in some localities, especially in the in Benin and Nigeria, led to the opening of flood gates of the developing and under developed countries, those living in the Kainji, Jebba and Shiriro Dams, resulting to a dearth and coastal areas, increasing deaths are witnessed because of their considerable material loss in 1999. Shortly after that level of exposures and vulnerability(Komolafe et al 2015). Kumadugu Yobe valley (Northern Nigeria) experienced a Floods can be generally considered in two categories: flash devastating flood again in 2001 (Amaize, 2011). The 2012 floods, the product of heavy localized precipitation in a short flood in Nigeria was believed to have resulted from the time period over a given location; and general floods, caused combination of intense rainfall and Cameron Lagdo Dam by precipitation over a longer time period and over a given effect, these devastating floods affected about 14 states that river basin (Atay and Ayhan 2006). Flooding is the most border the Niger-Benue River. The worst affected state common environmental hazard, due to the widespread includes Kogi, Edo, Anambra and Delta States. This flood geographical distribution of river valleys and coastal areas, and incident has been characterized as the most devastating since the attraction of human settlements to these areas (Atay and the last 40 years (Felix Ndidi 2013). The current trend and

IJESC, August 2019 23481 http://ijesc.org/ future scenarios of flood risks demand accurate spatial The methodology adopted image processing and classification information on the potential hazards and risks of floods. using object-based classifier into 5 Class categories, Agulu Techniques utilizing satellite remote sensing data can provide Lake, Residential Area, Farm Land, Low Forest and Road. objective information that help to detect floods and to monitor Ground truthing picking of sample points for accuracy their spatiotemporal evolution. This research emphasizes on assessment and to identify the features on ground to determine the use of remote sensing techniques in analyzing potential the landcover/ landuse of the study area. The hydrological flood risk in Agulu Lake. Satellite images have shown the elements were created from the DTM after filling the void. capabilities to extract relevant information needed to model These elements include flow accumulation, Slope, Elevation and manage the impact of flood. Currently there has not been and Drainage Network. The different data sets were any flood assessment carried out in Agulu Lake using remote reclassified for information generation such as DTM and slope sensing tools. So, the need to carry out this research is if not creation, all data was integrated in a GIS environment mandatory for future prediction of flood risk around Agulu and using multi-criterion decision tools (WLC) Weight Linear also produce a map showing possible flood zone areas to create Combination for preparation of flood risk maps into very high awareness for the individuals, cooperate organizations, risk, high risk, moderate risk and low risk zones using equal government and nongovernmental organization, industries etc. interval of separation based on elevation. flood prone areas within the community. IV. RESULTS II. STUDY AREA In this section, results of image analysis as obtained from the The study area is Agulu lake in Agulu Town under Anaocha image segmentation and flood potential risk mapping are Local Government Area of Anambra State in south east presented. Most of the discussions are supported by maps, Nigeria. Agulu Lake lies within the Anambra Basin, where tables and illustrative graphs. monstrous effects of flood, soil and gully erosion and landslides destroy lives and property. The study area lies A. Land Cover/Land Use Mapping within latitudes 60 06’-60 08’ N and longitudes 70 00’- 70 In mapping the landcover/landuse 2km by 2km around Agulu 02’E, in the Anambra Basin. Existence of Anambra Basin is as Lake, four different classes were identified to include a result of folding and uplift of Abakaliki-Benue fold belt in residential, low forest, farmland, lake and roads. the santonian stage (Egboka 2006). Early rainfall occurs usually in January/February with full commencement of rainy season in March and stopping in November of each year. The dry season commences from November and ends in February. The mean highest annual rainfall usually recorded around July to October is about 1952mm (Igbokwe, 2010). Fig2d shows the map of Agulu lake and its environs at a scale of 1:12500.

Figure.4.1 Landcover / Landuse map of 2km by 2km area covering Agulu Lake and Environ

Fig 4.1 shows the results of the landcover/landuse classification of 2km by 2km area around Agulu lake, the results indicate that residential accounted for the 36.50% of the land cover/use with an area of 156.95 hectares while low forest had the 48.48 % with an area of 208.44 hectares, farmland had 6.53% with an area of 28.09 hectares and Agulu lake had 8.47% with an area of 36.42 hectares. The landcover / landuse distribution is shown in table 4.1.

Figure.1. (a) Map of Nigeria showing Anambra State, (b) Anambra Table.4.1. Landcover /Landuse distribution of 2km by 2km Showing Anaocha LGA, (c) Anaocha LGA showing Agulu Lake; (d) land area covering Agulu lake and environ Satellite image of Agulu Lake. Class Name Area (Ha) Percentage (%)

III. METHODOLOGY Residential 156.95 36.50

3.1 Data Requirements Low Forest 208.44 48.48 Data used in this research include: Farmland 28.09 6.53 I. Quick Bird 2017 imagery covering the study area II. 2meter Digital Terrain model covering the study area. Agulu Lake 36.42 8.47 III. Shape file of 2km by 2km land area covering Agulu Lake and Environ Total 429.90 100

IJESC, August 2019 23482 http://ijesc.org/ B. Hydrological Modeling 3) Flow Accumulation and Distance from Stream Network 1) Filling Sinks Flow accumulation is a process that assigns every cell with a A digital terrain model is a topographical model with elevation value equals to number of cells flow into it. The catchment records of cells in certain size. However, there is still potential area cane calculated by multiplying value in accumulated flow of existence off sunken areas because of data error. Data error matrix with the area of a single cell. Since areas of is mainly caused by the resolution limitation on both vertical concentrated surface water, river over-flows are crucial for the and horizontal direction and system error during the generation initiation of a flood event and often the inundation emanates of DEM. Due to existence of these sinks, unreasonable flow from riverbeds and expands in the surroundings. Flow direction may be generated during the calculation. If these accumulation is the most important parameter in defining flood sinks are not filled by technical process, then the generated risks. Accumulated flow sums the water flowing down slope drainage network will not be continuous. The main purpose of into cells of the output raster. High values of accumulated flow elevation smoothing is to reduce the number of artificial will indicate areas of concentrated flow and consequently depressions generated by data collection system. The DTM higher flood risk. The results of the flow accumulation created was filled to remove any potential voids in the data. is shown in fig. 4.4 2) Elevation and Slope Reclassification After the DTM was filled, it was reclassified into the following elevation class as shown in figure 4.2. i. 104-110m (Very High Risk) ii. 110m -141m (High Risk) iii. 141m -166m (Moderate Risk) iv. 166m -207m (Low Risk) These categories of risk were created based on the elevation and ground information obtained within the catchment during the ground truthing.

Figure.4.4 Risk Zone by Flow Accumulation

Apart from areas of concentrated surface water, river- overflows are crucial for the initiation of a flood event. Often the inundation emanates from riverbeds and expands in the surroundings. The role of riverbed decreases as the distance increases. That explains why “distance from the drainage network” has been assigned a high weight in the methodology. It appears that areas near the river network < 200 m are highly flood hazard, whereas the effect of this parameter decreases in Figure.4.2 Risk zones by Elevation distances > 2000 m. the result of the distance from drainage network created at < 200m is shown in fig 4.5 The DTM was also used to create the slope gradient of the study area as shown in figure 4.3. The slope gradient obtained from the DTM was reclassified using the following five classes based on Food and Agricultural Organization (FAO) classification of slopes (www.fao.org): i. 0-2% = Flat (Very High Risk) ii. 2- 4% = Undulating (High Risk) iii. - 8% = Rolling (Moderate Risk) iv. 8 - 10% = Hilly (Low Risk)

Figure.4.5 Distance from Drainage Network

C. Overlay Weighted Analysis 1) Development of the Pairwise Comparison Matrix In order to ensure that each criterion was evaluated on the basis of its relative importance, two approaches were considered; i. Selecting the same numerical range (0–255) for each Figure. 4.3 Risk zone by Slope of the various criteria (standardization), assigning

IJESC, August 2019 23483 http://ijesc.org/ each criterion a score based on its relative importance a) Computation of the Criterion Weights (weight) and multiplying each standardized criterion This procedure involved the following operation by the value assigned to its relative weight to a) Sum the values in each column of the pairwise calculate its suitability index or comparison matrix; ii. Using a variable numeric range for the various criteria b) Divide each element in the matrix by its column total depending upon the relative importance of each (the resulting matrix is referred to as the normalized pairwise criterion. comparison matrix) In this research, the latter method was adopted, the pairwise c) Compute the average of the elements in each row of comparison method was adopted to assign weights to each the normalized matrix, that is, divide the sum of normalized criterion. This method provides an organized structure for scores for each row by 3 (the number of criteria). These group discussions and helps the decision-making group focus averages provide an estimate of the relative weights of the on areas of agreement and disagreement when setting criterion criteria being compared. Using this method; the weights are weights. Saaty (1990) proposed the pairwise comparison interpreted as the average of all possible ways of comparing method in the context of the analytical hierarchy process. This the criteria. The criterion weights are 0.45, 0.34, 0.16 and method is an effective method for the determination of relative 0.0325 for slope, elevation, flow accumulation and distance importance. The method uses a ratio matrix to compare one from drainage network respectively (Table-4.4). This means criterion with another. The matrix of pairwise comparisons elevation is the most important criterion, followed by slope, represents the intensities of the expert’s preference between flow accumulation and distance from drainage network. individual pairs of criteria. They are usually chosen according to a given scale ranging from 1 to 9 for a given ‘n’ number of Table. 4.4. Relative Weight of Criteria criteria, where 1 represents criteria of equal importance and 9 Slope Elevatio Flow Distance Relativ represents a criterion with extreme importance compared to the n Accumula from e tion drainage Weight other. Network Network

Elevatio 0.51 0.60 0.36 0.36 0.45 Table.4.2. Scale for Pairwise Comparison, Saaty (1990) n INTENSITY OF DEFINITION IMPORTANCE 1 Equal importance Slope 0.25 0.30 0.49 0.32 0.34 2 Equal to moderate importance 3 Moderate importance Flow 0.17 0.07 0.12 0.28 0.16 4 Moderate to strong importance Accumul ation 5 Strong importance Distance 0.05 0.03 0.01 0.04 0.0325 6 Strong to very strong importance from drainage 7 Very strong importance network 8 Very to extremely strong importance 9 Extreme importance b) Estimation of the Constituency Ratio The value of pairwise comparison relies on subjective The weights for the four criteria were determined as shown in judgment which might lead to arbitrary result which could be Table 4.3. The judgment table (comparison matrix) was bias. A numerical index, called consistency ratio (CR) is used represented by a 4 x 4 matrix and then multiplied by itself to for evaluating the consistency of pairwise comparison matrix obtain eigenvectors. Suppose that slope is extremely important (Saaty 1990). The index indicates the ration of the consistency over the flow accumulation attribute; that is the comparison index (CI) to the average consistency index, which is also result is a value of 9. Further, suppose that slope is moderately called Random Index (RI). important preferred to Distance from drainage network then a This is given as: numerical score of 3 is assigned. Finally, consider another :CR = Consistency index (CI)/Random Consistency Index (RI) pairwise comparison, which is the flow accumulation attribute …. Equation 1 compared to distance from drainage network and suppose that The value of Random Consistency Index (RI) can be found in the latter is strongly preferred to the former, then a score of 7 is the table, prepared according to number of criteria involved assigned. These scores are places in the upper right corner of (Saaty, 1990), as shown in table 4.5. the pairwise comparison matrix. Table.4.5. Random Consistency Index N 1 2 3 4 5 6 7 8 9 10 Table.4.3. Pairwise Comparison of the Evaluation Criteria Criterion Elevation Slope Flow Distance RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49 Accumulation from drainage Network The value of Consistency index, CI can be calculated from the Elevation 1 2 3 9 preference matrix according to equation 2 Slope ½ 1 4 8 : Equation 2 Flow 1/3 ¼ 1 7 λmax – n Accumulation λmax is the Principal Eigen Value; n is the number of factors λmax퐶퐼 = = Σ ofn−1 the products between each element of the priority Distance from 1/9 1/8 1/7 1 vector and relative weights drainage λmax = (1.94*0.45) + (3.32*0.34) + (8.14*0.16) + (25*0.0325) Network = 0.873 + 1.1288 + 1.3024 + 0.8 Total 1.94 3.32 8.14 25 λmax = 4.1042

IJESC, August 2019 23484 http://ijesc.org/ CI = (4.1042 – 4)/ (4-1) = 0.1042/3 = 0.034 high-risk zone see table 4.7 and figure 4.7. The very high-risk CR = 0.034/0.90 = 0.037 zone also covers 6 buildings and 8 plots of farmland, these CR = 0.037 < 0.10 (Acceptable) features are particularly at very high risk of potential flooding The consistency ratio (CR) is designed in such a way that if in the study area. This area is will be the first to experience CR<0.10, the ratio indicates a reasonable level of consistency flood due to its low terrain and close proximity to the lake and in the pairwise comparisons; if, however, CR ≥ 0.10, the drainage channel, so is therefore classified as having a very values of the ratio are indicative of inconsistent judgments. high potential of flooding in the study area. From the judgment a Consistency Ratio (CR) of 0.017 was achieved which was less than the maximum allowable ratio of Table.4.8. Potential Flood Areas at High Risk Zone 0.10. Following this, the weighted linear combination (WLC) model was then used to compute the constraints with their weights in ArcGIS raster calculator to produce Flood risk zones using the formula below. S = ((F1 * 0.45) + (F2 * 0.34) + (F3 * 0.16) + (F4 * 0.0325)) Note: F1, F2, F3 & F4 are thematic layers representing the constraints.

D. Potential Flood Risk Area The results of the weighted linear combination analysis produced a layer showing four potential risk zones; namely very high risk, high risk, moderate risk and low risk flood zones in the study area. The results indicated that very high- risk zone occupied 22.53% of the entire study area, covering an area of 96.88 hectares, while high risk zone occupied 23.80%, covered an area of 102.34 hectares. Moderate risk zone occupied 25.61%, covering 110.13 hectares while low risk zone occupied 28.04% covering an area of 120.55 hectares. This is distribution is also represented in table 4.6 and fig. 4.6.

Table.4.6. Flood risk Zone Distribution Area Class Name Percentage (%) (Hectares)

Very High Risk 96.88 22.53 Figure. 4.7 Very High-Risk Zone High Risk 102.34 23.80 Moderate Risk 110.13 25.61 The results also indicated that extent of high-risk zone covered 22.53% of the entire study area, with an area coverage of Low Risk 120.55 28.04 102.34 hectares, with 29.88% of residential, 22.76% of Total 429.90 100 farmland, and 49.7% of low forest all covered within high risk zone see table 4.8 and figure 4.8. The high-risk zone also covers 8 buildings and 11 plots of farmland, these features are particularly at high risk of potential flooding in the study area. This area follows immediately after the very high risk zone, this area will be the second to experience flooding if the flood levels from very high risk zone rises, and since the very high risk zone has a flat terrain, there stands a high possibility of the flood levels rising beyond the very high risk zone to high risk zone, so is therefore this area is too, classified as having a potential of experiencing flooding in the study area.

Figure.4.6 Histogram of Potential Risk Zones

Table.4.7. Potential Flood Areas at Very High-Risk zone

The results indicated that the extent of very high-risk zone covered 22.53% of the entire study area, with an area coverage of 96.88 hectares, with 25.23% of residential, 19.54% of farmland, and 52.11% of low forest all covered within very Figure. 4.8 High Risk Zone

IJESC, August 2019 23485 http://ijesc.org/ The results further revealed that the extent of moderate risk Table.4.10. Potential Flood Areas at Low Risk Zone zone covered 25.61% of the entire study area, with an area Class Area Percentage Features at coverage of 110.13 hectares, with 45.54% of residential, Name (Ha) (%) Low Risk 29 26.89% of farmland, and 37.7% of low forest all covered Residential 64.26 53.30 within moderate risk zone see table 4.9 and figure 4.9. The Buildings, Low 9 plots of moderate risk zone also covers 21 buildings and 14 plots of Farmland 25.99 21.56 farmland, these features are particularly at low potential of Risk Farmlands Low Forest 30.3 25.13 flooding in the study area. This area follows immediately after Total 120.55 100 the high-risk zone, this area will be the third to experience flooding if and only if the flood levels rises beyond the high- risk zone. But given that the nature the terrain of the moderate risk zone is much more higher that the high risk zone high, there stands little possibility of the flood levels rising beyond the high risk zone to moderate risk zone, so therefore this area is classified as having a little potential of flooding in the study area and all feature covered within are adjudged to be safe.

Table.4.9. Potential Flood Areas at Moderate Risk Zone Features Class Area Percentage at Name (Ha) (%) Moderate

Risk 21 Residential 45.54 41.35 Buildings, Moderate 14 plots of Figure.4.10 Low Risk Zones Farmland 26.89 24.42 Risk Farmlands Low Forest 37.7 34.23 Total 110.13 100 Agukwu-Nri Ezinano Junction Agukwu-Nri

Umuowelle village

Agululake Resort Enugukwu Umubialla Village Okpuifite Village

Figure.4.11. Composite Flood Risk Map of the Area

V. CONCLUSION

Figure. 4.9 Moderate Risk Zone Potential flood risk mapping is a vital component for appropriate landuse planning in flood-prone areas. It creates The results also indicated that the extent of low risk zone easily-read, rapidly-accessible charts and maps which facilitate covered 28.08% of the entire study area, with an area coverage the identification of areas at risk of flooding and also helps of 120.55 hectares, with 64.26% of residential, 25.99% of prioritize mitigation and response efforts. This study has been farmland, and 30.3% of low forest all covered within low risk able display the usefulness of Remote sensing and GIS zone, see table 4.10 and figure 4.10. The low risk zone also technologies in classifying and in identifying areas with very covers 29 buildings and 9 plots of farmland, these features are high, high, moderate, low risk of potential flooding within the particularly at no risk of potential flooding in the study area. study area. The classification achieved an overall classification This area follows immediately after the moderate risk zone, accuracy of 91.01% and the overall kappa was 0.9288. The this area will be the last to experience flooding if and only if image segmentation results indicated that residential accounted the flood levels rises beyond the moderate risk zone. But given for the 36.50% of the landcover/landuse with an area of 156.95 that the nature the terrain of the moderate risk zone is much hectares while low forest had the 48.48 % with an area of more higher that the high risk zone, there stands little 208.44 hectares, farmland had 6.53% with an area of 28.09 possibility of the flood levels rising beyond the high risk zone hectares and Agulu lake had 8.47% with an area of 36.42 to moderate risk zone, therefore there is no possibility that this hectares. The flood potential risk mapping results indicated area will be flooded as the terrain of this area has the highest that very high-risk zone occupied 22.53% of the entire study elevation of the whole study area and then since there is little area, covering an area of 96.88 hectares, while high risk zone possibility of flood in the moderate zone, it is therefore occupied 23.80%, covered an area of 102.34 hectares. adjusted that this area bears no possibility of flood in the study Moderate risk zone occupied 25.61%, covering 110.13 area and all features covered within are said to be safe. hectares while low risk zone occupied 28.04% covering an

IJESC, August 2019 23486 http://ijesc.org/ area of 120.55 hectares. The results also indicated that the Nigeria" Journal of Apply. Sci. Environ. Mgt. Vol. 9, No. 12, extent of very high-risk zone covered 22.53% of the entire pp. 01-04. Vol. 10 (3) 27 – 30, Cognition. (2008). In Oxford study area, with an area coverage of 96.88 hectares, with reference online premium dictionary. Retrieved from http: // 25.23% of residential, 19.54% of farmland, and 52.11% of low www.oxfordreference.com forest all covered within very high-risk zone. The very high- risk zone also covered 6 buildings and 8 plots of farmland, [6]. Felix N.N., Philip.J.H.,&Vincent N. O., (2013). Geospatial these features are at very high risk of potential flooding in the Techniques for the Assessment and Analysis of Flood Risk study area. The results also indicated that extent of high-risk along the Niger-Benue Basin in Nigeria.” Journal of zone covered 22.53% of the entire study area, with an area Geographic Information System, Vol.5, No.3, pp123-135 coverage of 102.34 hectares, with 29.88% of residential, http://dx.doi.org/10.4236/jgis.2013.52013 Published Online 22.76% of farmland, and 49.7% of low forest all covered (http://www.scirp.org/journal/jgis) within high risk zone. The high-risk zone also covered 8 buildings and 11 plots of farmland; these features are at high [7].Komolafe, A., Adegboyega, S & Akinluyi, F. (2015). A risk of potential flooding in the study area. The results further Review of Flood Risk Analysis in Nigeria. American journal revealed that the extent of moderate risk zone covered 25.61% of environmental sciences. 11.157 -166.10.3 844/ ajessp. 20 15 of the entire study area, with an area coverage of 110.13 .157.166. hectares, with 45.54% of residential, 26.89% of farmland, and 37.7% of low forest all covered within moderate risk zone. The [8]. Saaty, T. L., (1990). “How to make a decision: the analytic moderate risk zone also covered 21 buildings and 14 plots of hierarchy process” (ISSN 03772217). farmland; these features are particularly at low potential of flooding in the study area.The results also indicated that the extent of low risk zone covered 28.08% of the entire study area, with an area coverage of 120.55 hectares, with 64.26% of residential, 25.99% of farmland, and 30.3% of low forest all covered within low risk zone. The low risk zone also covered 29 buildings and 9 plots of farmland; these features are particularly at no risk of potential flooding in the study area.

Based on the results and analysis obtained, the following recommendations were made: 1. It is recommended that the results achieved in this research be used as a decision base to help identify and quantify of what is at risk of being flooded in the study area 2. The flood potential risk zone maps produced in this research are beneficial and are recommended that they be used in encouraging hazard zone residents to prepare for the occurrence of flooding. In order to achieve this however, local authorities must ensure that emergency procedures are established, and that information about what to do in the event of a flood is made available to the general public. 3. Developments along the edges of Agulu Lake and on drainage channels in the study area should be discouraged in order to minimize the risk of damage if flooding should happen.

VI. REFERENCES

[1]. Aderogba, K. A. (2012). Global warming and challenges of flood in Lagos Metropolis, Nigeria. Academic Research International. Vol. 2 No 1 pp. 448 – 468.

[2]. Akanni, O. & Bilesanmi, L. (2011). Flood: Lagos residents forced to relocate….Drowning teenager rescued” in Vanguard: Towards a Better Life for the People. Lagos: Vanguard Media Limited. (Friday, July10), p. 20.

[3]. Amaize, E. (2011). “Flood displaces 50 Villagers in Delta State”, in Vanguard: Towards a Better Life for the People. Lagos: Vanguard Media Limited. (Monday, July, 4). p. 9.

[4]. Atay, G. & Ayhan, E., (2000). “Using satellite images to determination environmental characteristics of an area. An Application.” commission VII/ WGVII/ 4Perfect, T. J., & Schwartz,B.L. (Eds.) (2002). Applied metacognition Retrieved from http://www.questia.com/read/107598848

[5]. Egboka, B. C., Nfor, B. N. & Banlanjo, E. W., (2006). "Water Budget Analysis of Agulu Lake in Anambra State,

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