Figure 2: Diagram of flood risk mapping development
Figure 3: Base map of Maran district
Figure 4 shows the location of hydrological stations in Maran district.
The average annual precipitation intensity data obtained from DID for
the years 2017 to 2021 are shown in Figure 5. Spatial interpolation is
a technique for estimating values at additional unknown locations by
developing points with known values. In the IDW approach, sample points
are weighted during interpolation such that the effect of one point
relative to another decreases with distance to construct an unknown
point value. The interpolation results are often presented in QGIS as a
two-dimensional raster layout. The process of generating mean annual
precipitation data using the IDW method was repeated for 2017 through
2021.
Figure 4: Location of hydrological stations
Figure 5: Annual rainfall intensity in Maran district
during 2017 - 2021 [15]
2.2.3 Land Use Map
Flood risk mapping is primarily concerned with identifying the areas of
zones that contribute to high vulnerability to flooding [16]. Floods in
different locations with different characteristics have different
impacts [17]. In this study, four classes of land use were identified,
namely water bodies, urban areas, agricultural areas, and forest areas.
Land use data for the study area were obtained from the Copernicus
Global Land Service website [18]. This website provides biogeophysical
products of the global land surface that can be used for work and
research.
2.2.4 DEM and Slope Map
On the other hand, the Digital Elevation Model (DEM) takes an important
part in hydrological and hydraulic modeling, especially in flood risk
mapping [19]. DEM is a raster representation of the surface information
of the earth's terrain. The data format used for this study, DEM, is
the Shuttle Radar Topography Mission (SRTM) 30-m spatial resolution
data from the United States Geological Survey (USGS) EarthExplorer
website [20], which provides free access to DEM data for research
purposes.
The purpose of the data from DEM is to determine the elevation and
slope of the study area's terrain using the dataset of elevations in
Cartesian coordinates. The elevation and contour of DEM can be
clarified by changing the color to a different scale. One of the DEM
products is the slope data that can be obtained from the dataset, which
represents the rate of change of elevation for each DEM cell [21]. The
raster data are processed in QGIS to generate the slope data. The slope
angle can be used as a surface indicator to determine flood hazard
[22]. The classification of flood risk according to the slope angle
criterion is categorized as low, slightly low, medium, slightly medium,
and finally high. The lowest ground surface has the highest potential
for flood risk.
2.2.5 Soil type map
The type of soil map obtained at Digital Soil Map of the World (DSMW)
is one of the most important factors in the occurrence of flooding, as
it determines the water-holding capacity and permeability of the study
area. According to the Soil Taxonomy of the United States Department of
Agriculture (USDA), the infiltration of a soil type is determined by
the classification values of the different classes, with the higher
values showing a high degree of infiltration. The weighted soil map was
created by assigning a value to each soil class, with the soil type with
the highest potential to generate extremely high flood risk rated as 3
and the lowest capacity to generate flood risk rated as 1. In general,
clay soils tend to drain more quickly than sandy soils during heavy
rainfall events, and the volume of runoff also tends to be higher.
2.3 GIS Data Base
GIS is an information system that uses geographic data or spatial data
that can be processed in various forms. GIS is different from other
types of information because information can be linked or used as a
reference to determine a location in space [11] and [23]. GIS is a tool
or system that uses computers to collect, store, retrieve, analyze, and
display spatial data from a map to the real world or from the real
world in the form of a map [24].
There are several steps involved in converting spatial data for use in
a GIS environment. QGIS V3.22.12 was used as a professional GIS package
for manipulating and managing data in a GIS environment. QGIS is a free
open-source geographic information system for creating, editing,
visualizing, analyzing, and publishing geospatial data on Windows. The
entire map with the four flood hazard criteria is converted to a raster
to merge the layers for the flood risk index and flood mapping. The
coordinate system used in the QGIS software is the World Geodetic System
(WGS) 1984 ensemble (EPSG: 4326), which has a limited accuracy of 2
meters at best.
2.4 Pairwise Comparison Method
The pairwise comparison method is used to determine the weights for the
criteria. This method compares the criteria and allows two criteria to
be compared simultaneously. This method can be used to convert
subjective relative importance ratings into a linear collection of
weights. It was developed by Saaty [25] in the context of a
decision-making process called the Analytical Hierarchy Process (AHP).
The AHP provides a mathematical way to convert the matrix of pairwise
comparisons of criteria into a vector of relative weights for the
criteria that accepts the pairwise comparisons as input and produces
the relative weights as output.
2.4.1 Pairwise comparison matrix
The four flood hazard criteria are compared using the pairwise
comparison matrix shown in Table 2. Precipitation, land use, slope, and
soil are listed in the column of the table and are compared with the
criteria in the column. The total row value was calculated to complete
the pairwise comparison matrix method.
2.4.2 Normalized matrix
After filling in the pairwise comparison matrix, the matrix value was
normalized by adding the numbers in each column. Each value in the
column was then divided by the column sum to obtain its normalized
value. Table 3 shows the method of normalization matrix.
Table 2: Pairwise comparison matrix [26]
|
Criteria
|
Rainfall
|
Land Use
|
Slope
|
Soil
|
|
Rainfall
|
1
|
3
|
2
|
1/2
|
|
Land Use
|
1/3
|
1
|
1/2
|
1/2
|
|
Slope
|
1/2
|
2
|
1
|
1/3
|
|
Soil
|
2
|
2
|
3
|
1
|
Table 3: Normalized matrix [26]
|
Criteria
|
Rainfall
|
Land Use
|
Slope
|
Soil
|
Priority
Vector
|
|
Rainfall
|
0.26
|
0.38
|
0.31
|
0.21
|
0.29
|
|
Land Use
|
0.09
|
0.13
|
0.08
|
0.21
|
0.12
|
|
Slope
|
0.13
|
0.25
|
0.15
|
0.14
|
0.17
|
|
Soil
|
0.52
|
0.25
|
0.46
|
0.43
|
0.42
|
|
Total
|
1.00
|
1.00
|
1.00
|
1.00
|
1.00
|
Table 4: Random index [25]
|
n
|
1
|
2
|
3
|
4
|
5
|
6
|
7
|
8
|
9
|
10
|
|
RI
|
0.00
|
0.00
|
0.58
|
0.90
|
1.12
|
1.24
|
1.32
|
1.41
|
1.45
|
1.49
|
2.5 Consistency Index
The consistency of the comparisons was checked using the Consistency
Ratio (CR). The CR must be less than 0.1 for the comparisons to be
consistent and thus acceptable. Thus, the consistency of the pairwise
comparison matrix is checked using the numerical index CR, which is
defined as in equation 1:
Equation 1
Where:
CIis Consistency Index, RI is Random Index, whose
value is determined by the number of samples (n). Equation 2
is used to determine the CI:
Equation 2
Where:
n is total number of criteria
λmax is principal eigenvalue, which is defined as
in equation 3
Equation 3
Where:
aj is element of pairwise matrix
j is row number of pairwise matrix
w is priority vector
W is criteria weight
t is corresponding criteria
The principal eigenvalue is obtained, as λmax=
4.165. The RI describes the consistency index of a randomly
generated pairwise comparison matrix. The number of components that is
compared determines the RI, which uses the values from Table
4. The CR was measured to determine the consistency of the assessment
for all flood criteria. Assessment consistency is acceptable if the CR
value is less than or equal to 10% [23]. The CR value is 6.1% and is
included in the acceptance criterion.
2.6 Flood Risk Index
The flood risk index analysis is used to determine the flood risk value
in the study area. The flood risk value was determined from the total
value of the scores for the four flood hazard criteria. The flood risk
index value K was calculated using quation 4:
Equation 4
With:
Wi is weighting value of criteria
Xi is influencing factors
3. Results and Discussion
In this contribution examine four types of flood hazard criteria only
to develop the flood risk map. The land use map was generated from land
use data obtained from the Copernicus Global Land Service website. The
raster map DEM from USGS EarthExplorer was used to form the contour and
slope map of the study area. Annual precipitation intensity data from
2017 to 2021 from DID were used to construct a precipitation map. The
soil type data obtained from DSMW was used to visualize a soil type map
distribution in the Maran district. All maps were merged to calculate
the value of flood risk in the study area.
3.1 Land Use Map
Figure 6 shows the land use map with a color indicator that classifies
the land use area as forest (dark green), agriculture (light green),
urban (red) and water body (blue). According to the pairwise
classification, the water area has the highest flood risk value,
followed by the city, agriculture, and finally forest. According to the
AHP result, land use changes have the greatest impact on flooding and
sedimentation in the study area.
Figure 6: Land use map of Maran district
Figure 7: Slope map of Maran district
Deforestation will lead to an increase in surface runoff that can cause
flash flooding in urban areas. Usually, urban areas are prone to the
effect of the flood due to the possible impact its cause in economy and
living matters. During heavy rains, flood possibly hits if the urban
wastewater or drainage system is unable to handle the amount of
rainfall. The need for infrastructure due to
development and
land use changes resulting in more impervious surfaces is mainly
responsible for increased flooding in urban areas. Land use conversion
from forestland to agriculture, residential, or industrial are said to
be a major cause of flooding disasters. Open land is prone to erosion
because the soil has lost its ability to absorb water. Consequently, as
surface runoff increases, peak runoff in the watershed also increases.
[27][28][29] and [30].
3.2 Slope Map
The slope map in Maran district was formed using the elevation data
from the DEM map. Figure 7 shows the slope map in Maran, where the red
color indicates the high value of the slope angle, and the green color
indicates the low value of the slope angle. The area with a high slope
angle is not suitable for residential areas, while the area with a
lower slope angle is suitable for urban and residential development.
Figure 8: Rainfall depth map of Maran district
3.3 Rainfall Intensity Map
The IDW function in QGIS was used to interpolate a point vector layer
to create a precipitation intensity map. Four hydrologic stations in
the study area were used as the point vector layer to interpolate the
unknown value of the grid cells in the study area. All hydrological
stations collected annual precipitation intensity data at a specific
location in the study area. The map of precipitation intensity was
constructed, where the red color indicator shows the high intensity of
precipitation, and the green color indicator shows the low intensity of
precipitation. The intensity of precipitation was divided into five
categories, which are shown in the map in Figure 8.
3.4 Soil Type Map
In general, rainwater infiltration is also influenced by the types of
soil. They determine the water storage capacity and the ability to
retain water during rainfall [28] and [31]. In Maran district,
particularly, soil permeability plays an important role in estimating
the occurrence of flooding. When the soil is saturated, surface runoff
increases. Larger particles such as sand and gravel have higher
permeability than clay and silt. Figure 9 shows the distribution of soil
types in Maran district, which generally composed of clay, loam, and
sandy clay loam. Clay is known to have slow permeability, which makes
the soil has big resistance to absorb running water. This condition, in
other words, is known as low infiltration rates and resulting in rapid
runoff during intense rainfall. While loam and sandy clay loam are
classified in moderate permeability.
3.5 Analytical Hierarchy Process
AHP was conducted to compare the flood hazard criteria with the total
area and distribution of data to show the influence of flood risk.
Table 5 shows the percentage influence of flood risk on the four flood
hazard criteria. The flood risk areas are significantly influenced by
soil type criteria (41.97%), followed by Rainfall intensity (28.92%),
slope (16.78%) and the least influence of flood risk in the study area
is land use criteria (12.33%). The value of CR from the priority matrix
result is about 6.2%, which is acceptable because the value is less than
10%. Table 6 describes the percentages of four parameters in generating
flood risk. Based on the previous AHP matrix for pairwise comparison in
Table 2, the MCDA sorted from the parameters with the most likely
influence to the parameters with the least influence. However, all
these parameters influence each other proportionally. No parameter can
stand alone as the sole factor for a flood event. Our result shows that
the soil type criterion accounts for the highest percentage. Numerous
studies have shown that soil type is an important factor for water
absorption [32] [33] and [34]. Different surface types have different
effects on runoff water. Soil with small pores tends to absorb water
slowly.
Figure 9: Soil type map of Maran district
Table 6: Slope flood risk classification scores
|
Slope(o)
|
Area (km2)
|
Scores
|
|
0-12
|
1725.78
|
5
|
|
12-25
|
158.81
|
4
|
|
25-37
|
82.65
|
3
|
|
37-49
|
24.84
|
2
|
|
>49
|
4.00
|
1
|
Table 7: Annual rainfall intensity flood risk
classification scores
|
Rainfall depth (mm)
|
Classification
|
Scores
|
|
≤1500
|
Low
|
1
|
|
1500-2000
|
Slightly low
|
2
|
|
2000-2500
|
Medium
|
3
|
|
2500-3000
|
Slightly high
|
4
|
|
>3000
|
High
|
5
|
Nevertheless, special attentions should be given to areas with
categories of high and very high-risk. The one-to-one mapping shows
that our flood risk map has over 75 percent similarity to the Maran
County data, which is still below our expectation of at least 80
percent similarity or more. These percentages refer to the areas
classified as high and very high-risk index in Figure 10, while the rest
are in very low, low, and moderate-risk index according to our map. The
areas along the Pahang Basin are classified in the very high-risk index
category, as shown in Figure 10. This means that the information
contained in our map is still considered suboptimal. The suboptimal
similarity of the result is understandable since we use only four
criteria to create the flood risk map. Nevertheless, the studied method
can be considered a success, even though it considers only a minimum of
four criteria. It is important to note that the spatial data such as
the DEM and the land use map are imported from open service providers
under the U.S. government agency and the European Commission, which
means that we have limitations in terms of spatial resolution. This
study has also shown that integrating GIS and MCDA could map and
analyze the potential flood risk area for a better natural disaster
planning and mitigation [41][42] and [43].
4. Conclusion
As a conclusion in our preliminary result of integrating the GIS and
MCDA, flood risk maps can be produced and used as monitoring tools. A
flood risk map indicates flood risk locations and provides important
data for mitigation measures. The result of this study achieved the
first objective by creating the flood risk map for 2017 to 2021 to
analyze and compare with the actual flood event. Data on land use,
slope, rainfall intensity from 2017 to 2021, and soil type were
collected to create a five-year flood risk map. The results of the map
show that the floodplain develops differently in each year due to the
different annual rainfall intensity. The flood risk weighing value for
land use, slope, soil type, and rainfall intensity can be determined by
the MCDA method. There are more flood vulnerability criteria that can
be used as a parameter to measure the risk value of flood occurrence in
the study area, such as elevation, distance to the river, and
Topographic Wetness Index (TWI) to get better and more precise results,
but this study is focusing on the use of the MCDA method in GIS system
to generated flood risk map in the study area. The value of the flood
risk index can be determined by calculating the total sum of the points
and the weighted parameters. The flood risk map for the study area can
be obtained from the flood index value. The map view was improved by
the color-coded classification of the flood risk index as very low,
low, moderate, high, and very high.
We have also shown here that, despite a similarity of less than 80
percent, the four main parameters for the preparation of a flood risk
map are considered satisfactory to show the basic concept of AHP
analysis in the preparation of the flood risk map. Therefore, it is
important to conduct some tests in the future either relying strictly
on the four significant parameters but with higher spatial resolution
of the data or keeping the standard spatial data but including some
additional parameters in the AHP. The accuracy of the information
provided, and the reliability of the map depend not only on the
parameters included in the AHP analysis, but also to a large extent on
the spatial accuracy of the map and the quality of the input data of
the AHP parameters.
It is important to note that the four parameters we included in the
analysis steps of AHP and MCDA in this study are able to show the
preliminary flood risk map for the mitigation plan. It opens higher
options to include more parameters and better spatial resolution of the
maps.
Acknowledgments
Communication of this research is made possible through monetary
assistance from the Universiti Tun Hussein Onn Malaysia and the UTHM
Publisher's Office through the Publication Fund E15216.
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