Physical Flood Vulnerability Mapping Applying Geospatial Techniques in Okazaki City, Aichi Prefecture, Japan

Flooding has been increasing since 2004 in Japan due to localized heavy rainfall and geographical conditions. Determining areas vulnerable to flooding as one element of flood hazard maps related to disaster management for urban development is necessary. This research integrated Remote Sensing data, the Geography Information System (GIS) method and Analytical Hierarchy Process (AHP) calculation to determine the physical flood-vulnerable area in Okazaki City. We developed this research by applying data from the Geospatial Information Authority of Japan (GSI) to generate the slope map and drainage density; AMEDAS (Automated Meteorological Data Acquisition System) from the Japan Meteorological Agency (JMA) to generate the rainfall data; Soil map from the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) data; and Sentinel-2 imagery to generate the land cover map. We applied the AHP calculation for weighting pairwise the parameters by comparing five iterations of the normalized matrix. We utilized the spatial analysis tool in ArcGIS to run the pairwise comparison to adjudicate the distribution of flooding according to the AHP procedure. The percentage of relative weight was slope (43%), drainage density (20%), rainfall intensity (17%), then both infiltration rate and land cover (10%). The consistency value was reasonable: consistency index (CI—0.007) and consistency ratio (CR—0.6%). We generated high accuracy for flood vulnerability prediction; 0.88 for Probability of Detection (POD), 0.28 for Probability of False Detection (POFD), 0.44 for Critical Success Index (CSI), 1.9 for Bias, and 95 of Area under Curve (AUC). The flood vulnerability was matched to the flood inundation survey of Okazaki City in August 2008 and indicated an excellent Relative Operating Characteristic (ROC).


Introduction
Japan is particularly vulnerable to flooding due to its steep geography and humid climate characterized by torrential rains and typhoons [1].Approximately 50% of the total population in Japan and approximately 75% of its assets are located in flood-vulnerable areas [2].Moreover, the vulnerable areas are primarily in the alluvial plains [3].The number of floods, and enhanced damage due to of urban growth and light industry development [32].Additionally, some researchers studied flood-risk assessment as a prominent component of urban planning [33][34][35].Thus, a spatial, multi-criteria index has been proposed to characterize such areas by integrating the technology of remote sensing and GIS to measure the physical parameters of flood vulnerability.The physical parameters of flood vulnerability were rainfall, drainage density, slope, soil and land cover.The rainfall, drainage density, slope, and land cover data were generated from satellite images; the soil was digitalized using the GIS method.All the parameters were combined by the GIS method and pairwise using AHP procedure.All data were downloaded from public open access data.The study area is located in Okazaki City, Aichi Prefecture Japan.Even though the index is tailored to the specific geography and land cover characteristics of the study area, it can be modified and applied in other areas.

Study Area and Flood History
The study area is located in the middle part of Aichi Prefecture Japan and has an area of 387.20 km 2 [36] from Latitude 34 • 53 30" N to 35 • 2 30" N and Longitude 137 • 7 0" E to 137 • 25 0" E. Okazaki City is the third biggest city in Aichi Prefecture [36] with a population of 382,846 people [37].It has a humid subtropical climate and an elevation ranging from 0 m to 789 m above sea level [36].The central city consists of alluvial plains.The eastern part of the city is a mountainous area which has an altitude of 789 m (i.e., Mt.Hongu) and the rest is lowland area which has an altitude ranging between 0 and 300 m [37].We selected the lowland area as the study area as shown in Figure 1.The study area is passed by big rivers, namely the Yahagi River which flows from North to South.Thus, the land use within this area is dominated by agriculture, particularly paddy fields.Precipitation occurs throughout the year with the heaviest in the summer season (i.e., June to September) and during typhoon phenomena.The temperature range is from −1 • C to 36 • C with an average annual temperature of 17.0 • C, and the annual rainfall is about 1200 mm/year.
From 1996 to 2008, severe heavy rains and flooding due to typhoons occurred 18 times [38].Large flood impact occurred on 11-13 September 2000 and 30 August 2008.However, the flood in 2008 was the most severe; the rainfall intensity was about 146 mm/h at 2:00 a.m.[4].Around 1110 houses were flooded above floor level, and 2255 houses were flooded below floor level [38,39].In addition, some areas of the neighboring city (i.e., Anjo City) were also flooded.The flood survey was conducted during the flood by the local government of Okazaki City and Ministry of Land, Infrastructure, Transportation and Tourism (MLIT) [40].This survey was used to verify the results of this study.
Urban Sci.2017, 1, 7 3 of 22 suitability of urban growth and light industry development [32].Additionally, some researchers studied flood-risk assessment as a prominent component of urban planning [33][34][35].Thus, a spatial, multi-criteria index has been proposed to characterize such areas by integrating the technology of remote sensing and GIS to measure the physical parameters of flood vulnerability.The physical parameters of flood vulnerability were rainfall, drainage density, slope, soil and land cover.The rainfall, drainage density, slope, and land cover data were generated from satellite images; the soil was digitalized using the GIS method.All the parameters were combined by the GIS method and pairwise using AHP procedure.All data were downloaded from public open access data.The study area is located in Okazaki City, Aichi Prefecture Japan.Even though the index is tailored to the specific geography and land cover characteristics of the study area, it can be modified and applied in other areas.

Study Area and Flood History
The study area is located in the middle part of Aichi Prefecture Japan and has an area of 387.20 km 2 [36] from Latitude 34°53′30″ N to 35°2′30″ N and Longitude 137°7′0″ E to 137°25′0″ E. Okazaki City is the third biggest city in Aichi Prefecture [36] with a population of 382,846 people [37].It has a humid subtropical climate and an elevation ranging from 0 m to 789 m above sea level [36].The central city consists of alluvial plains.The eastern part of the city is a mountainous area which has an altitude of 789 m (i.e., Mt.Hongu) and the rest is lowland area which has an altitude ranging between 0 and 300 m [37].We selected the lowland area as the study area as shown in Figure 1.The study area is passed by big rivers, namely the Yahagi River which flows from North to South.Thus, the land use within this area is dominated by agriculture, particularly paddy fields.Precipitation occurs throughout the year with the heaviest in the summer season (i.e., June to September) and during typhoon phenomena.The temperature range is from −1 °C to 36 °C with an average annual temperature of 17.0 °C, and the annual rainfall is about 1200 mm/year.
From 1996 to 2008, severe heavy rains and flooding due to typhoons occurred 18 times [38].Large flood impact occurred on 11-13 September 2000 and 30 August 2008.However, the flood in 2008 was the most severe; the rainfall intensity was about 146 mm/h at 2:00 a.m.[4].Around 1110 houses were flooded above floor level, and 2255 houses were flooded below floor level [38,39].In addition, some areas of the neighboring city (i.e., Anjo City) were also flooded.The flood survey was conducted during the flood by the local government of Okazaki City and Ministry of Land, Infrastructure, Transportation and Tourism (MLIT) [40].This survey was used to verify the results of this study.

Overview
Vulnerability maps can be utilized in all steps of disaster management: prevention, mitigation, preparedness, operations, relief, recovery and lessons learned [41].In recent years, urban areas have faced increased risk of disasters and the potential of economic and human losses from natural hazards.To build sustainable communities in the urban area, integration between urban planning and hazard mitigation effort is necessary [20,32].Thus, the vulnerability map can be used as one of the strategies for scenario modeling for urban planning.Integrating the remote sensing data, GIS and AHP are quite effective tools to generate flood vulnerability data for urban areas.The overall method description for flood vulnerability is explained in Figure 2.There are five physical parameters for flood vulnerability utilized in this study, namely, rainfall, drainage density, slope, soil and land cover.We generated the slope map and drainage density from Digital Elevation Model (DEM) data from the Geospatial Information Authority of Japan (GSI).Soil map distribution was generated from MLIT.The rainfall spatial distribution was generated from AMEDAS (Automated Meteorological Data Acquisition System) data from the Japan Meteorology Agency (JMA).The land cover was generated from Sentinel-2 imagery which was verified by Google Earth image.Then, by integrating all of those components, the flood-vulnerable area was assessed by AHP.Finally, the floodvulnerable area was verified by using field survey data taken during the flood event in Okazaki City that was conducted by the local government of Okazaki City and MLIT.We utilized three software systems to process the data; ESA SNAP and ENVI 4.8 for processing land cover and ArcGIS 10.2.2 (ESRI, Redlands, CA, USA) for processing hydrological parameter and vulnerability index.

Overview
Vulnerability maps can be utilized in all steps of disaster management: prevention, mitigation, preparedness, operations, relief, recovery and lessons learned [41].In recent years, urban areas have faced increased risk of disasters and the potential of economic and human losses from natural hazards.To build sustainable communities in the urban area, integration between urban planning and hazard mitigation effort is necessary [20,32].Thus, the vulnerability map can be used as one of the strategies for scenario modeling for urban planning.Integrating the remote sensing data, GIS and AHP are quite effective tools to generate flood vulnerability data for urban areas.The overall method description for flood vulnerability is explained in Figure 2.There are five physical parameters for flood vulnerability utilized in this study, namely, rainfall, drainage density, slope, soil and land cover.We generated the slope map and drainage density from Digital Elevation Model (DEM) data from the Geospatial Information Authority of Japan (GSI).Soil map distribution was generated from MLIT.The rainfall spatial distribution was generated from AMEDAS (Automated Meteorological Data Acquisition System) data from the Japan Meteorology Agency (JMA).The land cover was generated from Sentinel-2 imagery which was verified by Google Earth image.Then, by integrating all of those components, the flood-vulnerable area was assessed by AHP.Finally, the flood-vulnerable area was verified by using field survey data taken during the flood event in Okazaki City that was conducted by the local government of Okazaki City and MLIT.We utilized three software systems to process the data; ESA SNAP and ENVI 4.8 for processing land cover and ArcGIS 10.2.2 (ESRI, Redlands, CA, USA) for processing hydrological parameter and vulnerability index.Integrating the AHP procedure, GIS and remote sensing could preserve the up-to-date information, with low cost and good visualization.Integrating the AHP procedure, GIS and remote sensing could preserve the up-to-date information, with low cost and good visualization.

Rainfall
Floods are related to extremes in precipitation (from tropical storms, thunderstorms, orographic rainfall, widespread extratropical cyclones, etc.) [57].A combination of precipitation characteristics (e.g., the amount of rainfall, intensity, duration and spatial distribution) influences the flood events [43,46].Flash floods due to relatively small cells of conventional activity have received much attention and are deemed characteristic of small to medium basins [58].The rainfall cannot immediately infiltrate as ground water, i.e., runoff.It converges to the catchment area.The heavy rainfall rises the amount of discharge from rivers and causes overflowing.According to the flood record by MLIT, this case often occurs in Japan.Table 1 lists the rainfall intensity classification by JMA [4].

Rainfall
Floods are related to extremes in precipitation (from tropical storms, thunderstorms, orographic rainfall, widespread extratropical cyclones, etc.) [57].A combination of precipitation characteristics (e.g., the amount of rainfall, intensity, duration and spatial distribution) influences the flood events [43,46].Flash floods due to relatively small cells of conventional activity have received much attention and are deemed characteristic of small to medium basins [58].The rainfall cannot immediately infiltrate as ground water, i.e., runoff.It converges to the catchment area.The heavy rainfall rises the amount of discharge from rivers and causes overflowing.According to the flood record by MLIT, this case often occurs in Japan.Table 1 lists the rainfall intensity classification by JMA [4].

Drainage Density
Drainage density (DD) is the ratio of the sum length of streams in the watershed over its contributing area [46] as shown in Equation ( 1) where ∑ is the total length of streams and A is the area.∑ and A must be measured in the same units.In this study, the unit was m/m 2 .Drainage density influences the water output and sediment from the system [59].Low drainage density area is commonly found in high permeable soil and high-density vegetation; high drainage density area is commonly found in the impermeable surface material (e.g., rocky hill slopes), arid area and areas with sparse vegetation cover [47,51].Integration of drainage density and rainfall were considered by many researchers as a parameter of flooding [49,50,60].
Drainage density has two influences regarding peak of the flood [47] i.e., direct (e.g., the length of stream network and hillslope paths) and indirect (e.g., geomorphology) [48].
Figure 4 displays the drainage density, it was generated from GSI DEM.We extracted the elevation from the DEM data and created the stream from the elevation map.We generated the stream order and calculated the catchment area.Rationing the stream total length and catchment area followed Equation (1) to generate the drainage density.

Drainage Density
Drainage density (DD) is the ratio of the sum length of streams in the watershed over its contributing area [46] as shown in Equation ( 1) where ∑ L is the total length of streams and A is the area.∑ L and A must be measured in the same units.In this study, the unit was m/m 2 .Drainage density influences the water output and sediment from the system [59].Low drainage density area is commonly found in high permeable soil and high-density vegetation; high drainage density area is commonly found in the impermeable surface material (e.g., rocky hill slopes), arid area and areas with sparse vegetation cover [47,51].Integration of drainage density and rainfall were considered by many researchers as a parameter of flooding [49,50,60].
Drainage density has two influences regarding peak of the flood [47] i.e., direct (e.g., the length of stream network and hillslope paths) and indirect (e.g., geomorphology) [48].
Figure 4 displays the drainage density, it was generated from GSI DEM.We extracted the elevation from the DEM data and created the stream from the elevation map.We generated the stream order and calculated the catchment area.Rationing the stream total length and catchment area followed Equation (1) to generate the drainage density.

Slope
The slope is the ratio of steepness or the degree of inclination of a feature relative to the horizontal plane.Gradient, grade, incline and pitch are used interchangeable with slope [61].The slope is typically denoted as a percentage, an angle, or a ratio.The average slope of a topographical (topo) feature can be measured from contour lines on a topo map or by DEM.The slope is achieved by dividing the rise over run.Multiply this ratio by 100 to expose slope as a percentage.The slope angle represented in degrees is obtained by taking the arctangent of the ratio between rise and run [61].Table 2 lists the slope classification in Okazaki City.
Figure 5 shows the distribution of slope in Okazaki City.It was generated from the elevation map extracted from GSI DEM data.

Slope
The slope is the ratio of steepness or the degree of inclination of a feature relative to the horizontal plane.Gradient, grade, incline and pitch are used interchangeable with slope [61].The slope is typically denoted as a percentage, an angle, or a ratio.The average slope of a topographical (topo) feature can be measured from contour lines on a topo map or by DEM.The slope is achieved by dividing the rise over run.Multiply this ratio by 100 to expose slope as a percentage.The slope angle represented in degrees is obtained by taking the arctangent of the ratio between rise and run [61].Table 2 lists the slope classification in Okazaki City.

Soil
Soils in Japan are grouped into soil groups [63].Soil groups were divided according to their morphology and parent material; soil series groups were divided according to the variation of the thickness of humus layer, freckling and texture; soil series is basic unit classification (grouped according to parent material, morphology and process) [63,64].Table 3 lists the soil groups in Okazaki City.

Ando soils
Ando soils are from volcano parent material and unconsolidated soils.Their colors are dark rich from organic matter and have well-drained conditions.At an altitude of 3300 m, their textures are sandy-silt regosoils [63,65].

Ando soils (wet)
Ando soils wet have the same parent material as the Ando soils but poorly drained conditions and are freckled due to groundwater or irrigation water.Their colors are dark and rich from organics.They distribute in alluvial bottom land and are utilized as a paddy field [63].

Brown forest soils
Brown Forest soils are composed by various parent material.Their colors are yellowbrown, and they are well drained and spread in a mountainous area as upland crops, tree crops and pasture.Small parts of these soils are utilized for cultivating [63].

Gley soils
Gley soils are poorly drained alluvial plains and high groundwater tables.These soils are broadly used for paddy rice cultivation [63].

Gray lowland soils
Gray Lowland soils are from Holocene alluvial plains or polders under well-to imperfectly drained conditions.These soils are the most productive for paddy cultivated soil in Japan [63].

Muck soils
Muck soils decompose more than 20% of organic matter from back marshes, the margin of peat moor, etc.Compared to Ando soils, they have low phosphate retention and are widely utilized as a paddy field [63].

Red soils
Red soils are decomposed from various parent materials.These soils decompose due to humid conditions and the warm to temperate climate in western Japan.Red soils

Soil
Soils in Japan are grouped into soil groups [63].Soil groups were divided according to their morphology and parent material; soil series groups were divided according to the variation of the thickness of humus layer, freckling and texture; soil series is basic unit classification (grouped according to parent material, morphology and process) [63,64].Table 3 lists the soil groups in Okazaki City.

Ando soils
Ando soils are from volcano parent material and unconsolidated soils.Their colors are dark rich from organic matter and have well-drained conditions.At an altitude of 3300 m, their textures are sandy-silt regosoils [63,65].

Ando soils (wet)
Ando soils wet have the same parent material as the Ando soils but poorly drained conditions and are freckled due to groundwater or irrigation water.Their colors are dark and rich from organics.They distribute in alluvial bottom land and are utilized as a paddy field [63].

Brown forest soils
Brown Forest soils are composed by various parent material.Their colors are yellow-brown, and they are well drained and spread in a mountainous area as upland crops, tree crops and pasture.Small parts of these soils are utilized for cultivating [63].

Gley soils
Gley soils are poorly drained alluvial plains and high groundwater tables.These soils are broadly used for paddy rice cultivation [63].

Gray lowland soils
Gray Lowland soils are from Holocene alluvial plains or polders under well-to imperfectly drained conditions.These soils are the most productive for paddy cultivated soil in Japan [63].

Muck soils
Muck soils decompose more than 20% of organic matter from back marshes, the margin of peat moor, etc.Compared to Ando soils, they have low phosphate retention and are widely utilized as a paddy field [63].

Red soils
Red soils are decomposed from various parent materials.These soils decompose due to humid conditions and the warm to temperate climate in western Japan.Red soils are spread in terraces and hills of low altitude near seacoasts and are suitable for upland crops, tree crops and grasses [63].

Yellow soils
Yellow soils decompose due to humid warm and humid temperate climates.Their characteristics are similar to Red soils except for their yellow color.Some of them are poorly drained and have freckles [63].

Manmade soils (artificially flattened areas)
Manmade soils are emplaced soil at least to a depth of 35 cm from the surface.These soils are deeply disturbed or reshaped by humans.There are two types of manmade soils according to their altitude, i.e., Manmade Upland soils and Manmade Lowland soils [63].Soil water infiltration is used as a flood parameter.It influences the runoff, soil loss, aquifer recharge, irrigation management and plant water availability.The rate of water infiltration depends on external factors and soil properties, which vary based on the type of soil [66].Thus, a literature review was done to investigate the characteristic of the soil type [63,[66][67][68][69][70].Soil water infiltration is a parameter that needs to be known to explain many processes such as runoff, soil loss, and aquifer recharge, as well as for irrigation management and plant water availability [66].The water levels in the level area are very low [71].This condition becomes worse during the rainy season.The infiltrating process from the previous rainy day is still continuing when the new rainy day comes, or the rainfall intensity is higher than the infiltration rate, and this condition could lead to flooding.Soil rate infiltration according to the slope is listed in Table 4. Soil water infiltration is used as a flood parameter.It influences the runoff, soil loss, aquifer recharge, irrigation management and plant water availability.The rate of water infiltration depends on external factors and soil properties, which vary based on the type of soil [66].Thus, a literature review was done to investigate the characteristic of the soil type [63,[66][67][68][69][70].Soil water infiltration is a parameter that needs to be known to explain many processes such as runoff, soil loss, and aquifer recharge, as well as for irrigation management and plant water availability [66].
The water levels in the level area are very low [71].This condition becomes worse during the rainy season.The infiltrating process from the previous rainy day is still continuing when the new rainy day comes, or the rainfall intensity is higher than the infiltration rate, and this condition could lead to flooding.Soil rate infiltration according to the slope is listed in Table 4.

Land Cover
Land use is the surface cover of the earth in a specific location (e.g., vegetation type, a manmade structure, etc.) [73].It can be derived directly from remote sensing data and it needs to be verified by survey [73][74][75].Furthermore, the land use can represent the socioeconomic condition in a certain area [6,74].The classification of land use is listed in Table 5.
Table 5. Land use/land cover classification scheme by land use cover types stratified according to the U.S. Geological Survey's (USGS) land use and land cover classification system for utilizing with remote sensor data [44,76].

Land Cover Description
Built-up land Area that has been populated (e.g., land use: residential, commercial, industrial, transportation and facilities).
Forest Figure 7 shows the distribution of a land cover map that was generated with Sentinel-2 imagery.We downloaded the Level-2A (bottom-of-atmosphere reflectance in cartographic geometry, prototype product).It was recorded on 22 November 2016.It is a panchromatic image with 10 m for visible and VNIR band (Band 2, 3, 4 and 8) [77].

Analytical Hierarchy Process (AHP)
AHP is a decision-making technique utilized for solving complex problems, with many parameters of interrelated objectives or concerned criteria.The level of each parameter is not equal; some parameters are dominant over others.Different weights can generate the difference in the level

Analytical Hierarchy Process (AHP)
AHP is a decision-making technique utilized for solving complex problems, with many parameters of interrelated objectives or concerned criteria.The level of each parameter is not equal; some parameters are dominant over others.Different weights can generate the difference in the level of susceptibility [27].It is based on ranking from the experts, literature reviews and previous study.The AHP pairwise have to be consistent with all pairwise comparisons.The AHP parameter for flooding was adapted from Ouma and Tateishi (2014) [6], and we modified by interviewing two hydrologists.Ouma et al. formulated the parameter with 16 experts (four hydrologists, four engineers and eight end-users).They selected six high-influence parameters (rainfall, drainage density, elevation, slope, soil and land use).We modified the flooding parameters as a result of the literature review and additional evaluation from experts.Thus, we utilized rainfall, drainage density, slope, soil and land cover as the parameters.

Relative Weight of the Parameters
The ranking is related to their relative importance as listed in Table 6.The relative importance has a range from 1 to 9; 1 means an equal contribution of the pairwise parameter and 9 means a very important parameter [14,15].Table 6.Nine-point pairwise comparison scale [14,15].

Intensity of Importance
Definition Explanation The number of parameter influences the matrix; 3 × 3 for three parameters, 4 × 4 for four parameters, 5 × 5 for five parameters and so on.The proposed method used a 5 × 5 matrix.The comparisons of parameters were generated from the expert judgment, literature review and the previous study.The weighting calculation follows Equation (2).
where A is the weight of the parameter, a kl (kl = 11, 12, . . .54,55).The right eigenvalue (v) corresponding to the maximum eigenvalue (λ max ) is calculated to normalize and find the relative weight (A v ) of the matrix by following Equation (3).To calculate the weight and ranking in each factor, the pairwise comparison matrix and factor map are employed.The weight value provided the prioritize factor expressed as a percentage value between 0 and 100%.Using a linear weighted combination, the sum of weight was expressed as 100%.A summary of targeted factors, their weights and rankings are listed in Table 10.We applied the information in this table to generate the distribution of areas vulnerable to flooding.The ranking of each reclassified factor is based on the literature review.The range of ranking was 1 to 5; the highest influence factor was rank 5 and the lowest influence factor was 1.The order of normalized weight was lope parameter (43%), Drainage density (DD) parameter (20%), Rainfall intensity (17%) then Urban Sci.2017, 1, 7 infiltration rate (10%) and land cover (10%).Looking at the weight of each factor, one can see that slope has the highest weight.It implies that slope has more contribution to flooding than other factors.Slope influences the direction of the runoff or subsurface drainage.Furthermore, the slope has dominant control of the rainfall of stream flow, duration of flow and duration of infiltration process.
Once the weight in each factor was determined, the multi-criteria analysis was performed to produce a flood-vulnerable area by using the GIS approach.To compute the vulnerable area, a weight linear combination was applied as shown in Equation (6).
The result is the flood-vulnerable area in the lowland of Okazaki City, shown in Figures 8 and 10.It was categorized into four vulnerable classes, e.g., "very low", "low", "moderate" and "high", according to the expert's knowledge-based classification.The result described that 28% of the area was classified as a high hazard zone.This area was close to the big river and in the lowland area; 5% of the lowland area of Okazaki City was categorized as a moderate level of vulnerability to flooding in the lowland area.The difference of infiltration rate categorized the lowland area into high and moderate flood-vulnerable levels.In contrast, 67% of Okazaki City was categorized as very low to low level of flood hazard.Most of the area was located at a higher elevation and far from the DD area.As shown in Figures 8 and 10, the main city of Okazaki is prone to high vulnerability, due to very slow infiltration rates and near level slope.A single map from a combination of multi-parameter and multi-criteria analysis is shown in Figure 8, as the representation of flood prediction map for the study.In order to validate our assessment, a quantitative method was applied.This method is based on the contingency table as shown in Table 11.The flood threshold used for flood discrimination is highvulnerability area (red color) and non-flood discrimination is the rest (very low to moderate).In Table 11, a, b, c and d stand for "hit", "false alarm", "miss" and "correct negative".The "Hit" represents correctly estimated flood area, "miss" represents when flooding is not estimated but actual flooding occurs, "false alarm" represents when flooding is estimated but flooding does not actually occur, and "correct negative" describes correctly estimated non-flooded area.Using the result shown in Table 11, we calculated the Probability of Detection (POD), Probability of False Detection (POFD), Critical

Flood Validation
The flood-vulnerable area was distributed in the level to near level slope of 0%-2%; drainage density was more than 0.00574 m/m 2 ; rainfall was over 80 mm/h; there was a very slow infiltration rate (1-5 mm/h) and land surface was covered by rice fields and open area.In addition, to perform the validation, flooding-based field verification was conducted by the local government of Okazaki City and MLIT was used.The survey data of flood extent was employed for verification.Figure 8 shows the flood vulnerability map, survey data of flood extent in 2008 and sample area for accuracy assessment.
In order to validate our assessment, a quantitative method was applied.This method is based on the contingency table as shown in Table 11.The flood threshold used for flood discrimination is high-vulnerability area (red color) and non-flood discrimination is the rest (very low to moderate).In Table 11, a, b, c and d stand for "hit", "false alarm", "miss" and "correct negative".The "Hit" represents correctly estimated flood area, "miss" represents when flooding is not estimated but actual flooding occurs, "false alarm" represents when flooding is estimated but flooding does not actually occur, and "correct negative" describes correctly estimated non-flooded area.Using the result shown in Table 11, we calculated the Probability of Detection (POD), Probability of False Detection (POFD), Critical Success Index (CSI), Bias and Relative Operating Characteristic (ROC) [80][81][82] as explained in Equations ( 7)- (10).
POD explained how good the model estimates are in detecting flooding area; the range is 0 (false prediction) to 1 (perfect prediction).POFD shows false alarm of flood; its range 0 (no false prediction) to 1 (perfectly false prediction).Bias measures the similarity of "yes" model and "yes" survey.A perfect score is 1, <1 means under forecast and >1 over forecast.CSI means how well the "yes" model corresponds to "yes" survey; 0 indicates no correspondent and 1 indicates perfect.
We generated accurate results; POD (0.88) means high accuracy of flood event which occurred in predicted flood area, POFD (0.28) means low probability of false alarm of the flood, CSI 0.44 means normal correspondent and Bias (1.9) means over prediction of flooded area.The ROC can be seen in Figure 9 and the Area Under Curve (AUC) was 0.95.
We generated accurate results; POD (0.88) means high accuracy of flood event which occurred in predicted flood area, POFD (0.28) means low probability of false alarm of the flood, CSI 0.44 means normal correspondent and Bias (1.9) means over prediction of flooded area.The ROC can be seen in Figure 9 and the Area Under Curve (AUC) was 0.95.ROC curves represent excellent, good, and worthless for an accuracy assessment.The accuracy is measured by the area under the ROC curve which was called AUC.An area of 1 represents a perfect test; an area of 0.5 represents a worthless test.The accuracy classification of AUC was explained as follows: 0.9-1 = excellent (A); 0.80-0.90= good; 0.70-0.80= fair; 0.60-0.70= poor; 0.50-0.60= fail.Referring to Figure 9, our model produced flood vulnerability area with the AUC 95%.This means that our model could be considered excellent at separating flooded and non-flooded areas.The AUC can be calculated by Equation ( 10) where x is the rate of POD for scenario i; y is the rate of POFD for the scenario i; and n is the number of suitability group.

Urban Flood-Risk Area
The assessment of flood-risk area was accomplished by using land cover type.We evaluated the risk of flooding based on land cover type by using cluster analysis [83].Approximately 10,612.07Ha was vulnerable to flood in the lowland area of Okazaki City.The result stated that agriculture and residential areas comprised 49.5% and 29.6%, respectively, of the total flood-vulnerable area, while the lowest percentage of the vulnerable area was located in the open area (17.5%) and the vegetation, e.g., forest (3.4%).It occurred due to land cover influence on filtration rate.
Regarding the flood simulation with the building data from GSI (all permanent buildings; offices, schools, hospitals, houses, etc.), flooding affected approximately 1136.66 Ha total of the building area as shown in Figure 10 with gray color indicating the building area.According to the building distribution, more than half of the population in the lowland area of Okazaki City lived in ROC curves represent excellent, good, and worthless for an accuracy assessment.The accuracy is measured by the area under the ROC curve which was called AUC.An area of 1 represents a perfect test; an area of 0.5 represents a worthless test.The accuracy classification of AUC was explained as follows: 0.9-1 = excellent (A); 0.80-0.90= good; 0.70-0.80= fair; 0.60-0.70= poor; 0.50-0.60= fail.Referring to Figure 9, our model produced flood vulnerability area with the AUC 95%.This means that our model could be considered excellent at separating flooded and non-flooded areas.The AUC can be calculated by Equation ( 11) where x is the rate of POD for scenario i; y is the rate of POFD for the scenario i; and n is the number of suitability group.

Urban Flood-Risk Area
The assessment of flood-risk area was accomplished by using land cover type.We evaluated the risk of flooding based on land cover type by using cluster analysis [83].Approximately 10,612.07Ha was vulnerable to flood in the lowland area of Okazaki City.The result stated that agriculture and residential areas comprised 49.5% and 29.6%, respectively, of the total flood-vulnerable area, while the lowest percentage of the vulnerable area was located in the open area (17.5%) and the vegetation, e.g., forest (3.4%).It occurred due to land cover influence on filtration rate.
Regarding the flood simulation with the building data from GSI (all permanent buildings; offices, schools, hospitals, houses, etc.), flooding affected approximately 1136.66 Ha total of the building area as shown in Figure 10 with gray color indicating the building area.According to the building distribution, more than half of the population in the lowland area of Okazaki City lived in flood-prone areas.Moreover, Okazaki City was also covered by agricultural land which consisted of paddy (1400 Ha), wheat (560 Ha), soy (505 Ha), and vegetable and fruits (i.e., eggplant, strawberry, grape (48.9 Ha)) [84].Most of the agricultural sector was located around Yahagi River which was also categorized as a flood-prone area.This condition caused major economic losses, in particular as a result of infrastructure damage, agriculture loss and suspension of business activity.Flood inundation caused not only economic loss but also caused various diseases such as waterborne disease, vector-borne disease, etc. paddy (1400 Ha), wheat (560 Ha), soy (505 Ha), and vegetable and fruits (i.e., eggplant, strawberry, grape (48.9 Ha)) [84].Most of the agricultural sector was located around Yahagi River which was also categorized as a flood-prone area.This condition caused major economic losses, in particular as a result of infrastructure damage, agriculture loss and suspension of business activity.Flood inundation caused not only economic loss but also caused various diseases such as waterborne disease, vector-borne disease, etc. Urban sustainability can be influenced by economic development, socioeconomic policy, population growth, physical environment, and natural hazards [28,85].However, in the interim of planning, development, and management of an urban area, only the economic and social variables are usually considered.As a result, in vulnerable sites, such as areas with steep slopes, natural hazards often occur, especially floods which can cause vast destruction of the social and economic network and lead to the loss of human lives and property.In order to avoid the aforementioned outcome, during the planning and growth of an urban area, it is necessary for the decision makers, engineers, planners, and managers to consider the physical variables of the urban area, in particular its susceptibility to natural hazards.
Multi-criteria analysis such as AHP is an effective decision support tool for dealing with complex decision constellations where technological, economical, ecological, and social aspects have to be included.The AHP method was applied and combined with GIS and Remote Sensing for assessing the level of flood vulnerability of areas.Furthermore, this method was relatively inexpensive, easy to use and, more importantly, allows the combination of many parameters, statistical analyses and physical properties which can be derived from satellite or survey data.However, AHP has its problems and challenges.For instance, AHP relies on input from the experts, and it is sometimes difficult to find experts.Urban sustainability can be influenced by economic development, socioeconomic policy, population growth, physical environment, and natural hazards [28,85].However, in the interim of planning, development, and management of an urban area, only the economic and social variables are usually considered.As a result, in vulnerable sites, such as areas with steep slopes, natural hazards often occur, especially floods which can cause vast destruction of the social and economic network and lead to the loss of human lives and property.In order to avoid the aforementioned outcome, during the planning and growth of an urban area, it is necessary for the decision makers, engineers, planners, and managers to consider the physical variables of the urban area, in particular its susceptibility to natural hazards.
Multi-criteria analysis such as AHP is an effective decision support tool for dealing with complex decision constellations where technological, economical, ecological, and social aspects have to be included.The AHP method was applied and combined with GIS and Remote Sensing for assessing the level of flood vulnerability of areas.Furthermore, this method was relatively inexpensive, easy to use and, more importantly, allows the combination of many parameters, statistical analyses and physical properties which can be derived from satellite or survey data.However, AHP has its problems and challenges.For instance, AHP relies on input from the experts, and it is sometimes difficult to find experts.

Conclusions
It is difficult to reduce the occurrence of natural disasters, including floods.What we can do is minimize their impact.Assessing areas vulnerable to flooding disasters is one of the parameters in creating a flood-risk map for disaster mitigation and urban planning.This study tried to assess the area that was vulnerable to flooding using integrated approaches of remote sensing, GIS, and spatial multi-criteria evaluation through the Analytical Hierarchy Process (AHP) approach.The parameters of slope, drainage density, rainfall intensity, infiltration rate, and land cover were applied to predict affected area of flooding.The AHP calculation shows that slope was the highest weight (43%) in determining vulnerability to flooding through the spatial-weighted overlay, followed by drainage density (20%), rainfall intensity (17%), infiltration rate (10%) and land cover (10%).This calculation resulted in a 0.6% consistency ratio.The slope parameter was the most important parameter because the slope influences the flow direction, runoff and soil infiltration.
There are several measurement methods for urban planning in Japan, which are applied to each area by the local government depending on local circumstances under the City Planning Law [86].According to the structure of City Planning System from the Ministry of Land, Infrastructure, Transportation and Tourism (MLIT), there are three components, i.e., Land Use Regulation, Urban Facilities and Urban Development Projects; one part of the Urban Development Projects is promotion for reconstruction of the disaster-stricken urban area.We proposed the result of this study as a consideration for urban planning in Okazaki City.The result of the flood-vulnerable areas shows excellent accuracy; Probability of Detection (POD-0.88),Probability of False Detection (POFD-0.28),Critical Success Index (CSI-0.44),Bias (1.9) and Area under Curve (AUC-0.95) from the Relative Operating Characteristic (ROC) graph.This method can be utilized to complement the hazard map of MLIT.
The total area of Okazaki City is approximately 95,679.2Ha, and 10,612.07Ha or 11.1% of the total area is highly vulnerable to flooding.From the total flood-vulnerable area in the lowland area, the land cover with the highest risk of flood was a residential area (29.6%) and agricultural area (49.5%).Those areas were close to the big river and had a slow infiltration rate, in addition to having a level to near level slope.These conditions influence the economic situation.By analysis of the building distribution and the flood-risk area, it is clear that approximately 1366.66 Ha total of construction area (i.e., offices, schools, hospitals, houses, etc.) is in the flood-prone area.
This study contributes an important approach for the effectiveness of disaster mitigation and urban planning.This method can be applied in other geographical areas, even if they have different topographical characteristics than this study area.This study aimed to evaluate the physical parameter of flood vulnerability.The use of other parameters, including river proximity, in addition to a comparison study using other sources of elevation data, will be analyzed for future work, and we will also consider the social, economic, ecological, cultural and institutional variables.

Figure 1 .
Figure 1.(a) Location of Aichi Prefecture in Japan; (b) Location of Okazaki City in Aichi Prefecture; (c) Research area in the lowland area of Okazaki City.

Figure 1 .
Figure 1.(a) Location of Aichi Prefecture in Japan; (b) Location of Okazaki City in Aichi Prefecture; (c) Research area in the lowland area of Okazaki City.

Figure 3
Figure 3 was generated from JMA data.It was interpolated by the Kriging Method [44] from 20 points observation in Aichi Prefecture during extreme rainfall (i.e., the rainfall intensity was 146 mm/h or 263.5 mm in two hours on 29 August 2008).Flooding could occur when the amount of rainfall was 199 mm to 435 mm for five hours or 406 mm to 608 mm for one week [43].Urban Sci.2017, 1, 7 6 of 22

Figure 5
Figure5shows the distribution of slope in Okazaki City.It was generated from the elevation map extracted from GSI DEM data.

Urban Sci. 2017, 1 , 7 9 of 22 Figure 6
Figure 6 displays the distribution of soil groups in Okazaki City.It was generated by manual digitalizing through ArcGIS 10.2.2.This soil map was produced by the National Land Agency (a.k.a MLIT) on 7 July 1975.

Figure 6
Figure 6 displays the distribution of soil groups in Okazaki City.It was generated by manual digitalizing through ArcGIS 10.2.2.This soil map was produced by the National Land Agency (a.k.a MLIT) on 7 July 1975.Soil water infiltration is used as a flood parameter.It influences the runoff, soil loss, aquifer recharge, irrigation management and plant water availability.The rate of water infiltration depends on external factors and soil properties, which vary based on the type of soil[66].Thus, a literature review was done to investigate the characteristic of the soil type[63,[66][67][68][69][70].Soil water infiltration is a parameter that needs to be known to explain many processes such as runoff, soil loss, and aquifer recharge, as well as for irrigation management and plant water availability[66].

Figure 8 .
Figure 8. Flood-vulnerable area in the lowland of Okazaki City, the field survey of flood inundation in August 2008, and the sampling area for accuracy assessment.

Figure 8 .
Figure 8. Flood-vulnerable area in the lowland of Okazaki City, the field survey of flood inundation in August 2008, and the sampling area for accuracy assessment.

Figure 9 .
Figure 9. Relative Operating Characteristic curve for flood vulnerability in Okazaki City by using AHP procedure.

Figure 9 .
Figure 9. Relative Operating Characteristic curve for flood vulnerability in Okazaki City by using AHP procedure.

Figure 10 .
Figure 10.Buildings in areas vulnerable to flooding in Okazaki City.

Figure 10 .
Figure 10.Buildings in areas vulnerable to flooding in Okazaki City.
12 a 13 a 14 a 15 a 21 a 22 a 23 a 24 a 25 a 31 a 33 a 34 a 35 a 35 a 41 a 42 a 43 a 44 a 45 a 51 a 52 a 53 a 54 a 55

Table 10 .
Consistency of pair comparison.

Table 11 .
The contingency table of AHP validation.