Flooding constitutes a major problem for the inhabitants of Douala City in general and those of the Tongo Bassa watershed (TBW) in particular. Faced with this situation, public authorities need to put in place measures to mitigate the vulnerability of populations to these disasters. This article aims to map flooding risk areas in the TBW using the geographic information system, field data (historical flood points), remote sensing data (Sentinel II image) and the frequency ratio model. The map produced shows that 1.41, 8.88, 28.51, 33.86 and 27.33% of the basin area are respectively delimited into very low, low, medium, high and very high flood vulnerability classes. High and very high flooding risk areas (those where flooding is most likely to occur) occupy more than half of the basin (61.19%). These areas are characterized by significant imperviousness, low altitudes, weak slopes, significant proximity to watercourses and clayey soils. Most of the houses in the basin (66.92%) are located in areas affected by these two levels of exposure (high and very high). With respective success and prediction accuracy rates of 89 and 96.78%, a certain confidence deserves to be placed on the map of flooding risk areas produced.

  • Flood susceptibility mapping is addressed through the geographic information system, remote sensing and the frequency ratio model.

  • A flooding risk areas map with five levels of exposure is proposed.

  • The number of houses included in each flood exposure level is summarized.

Water resources management and related risks are increasingly becoming an important concern of contemporary society (Chomba et al. 2022; Ebodé 2022a; Ebodé et al. 2022; Nsangou et al. 2022). Among the wide range of existing hydrological risks, flooding appears to be one of the most devastating, considering the enormous damage it often causes (Samanta et al. 2018; Ekwueme 2022; Krisnayanti et al. 2022; Manzoor et al. 2022). Between the 1990s and 2010s, floods caused the deaths of more than 158,000 people around the world (Centre for Research on the Epidemiology of Disasters 2018). Economic losses of around $23 billion are also recorded due to floods each year (UN 2018). If we stick to climate forecasts and changes in land use and land cover (LULC) patterns, we will observe an increase in the number and territories exposed to these disasters (Vidhee & Amit 2020; Asinya & Alam 2021; Chen et al. 2023).

In the European Union, flood phenomena were responsible for one-third of the economic losses caused by natural disasters between 1980 and 2016 (Costache et al. 2020). In terms of the loss of human lives and destruction of property, floods are among the most widespread and severe natural hazards in this region (Costache et al. 2020). The amount of damage due to the California floods in 1997 was $2,000 million. That of Mississippi in 1995 was $7,499 million. Environmentally, Nsangou et al. (2022) reported the Indian Himalaya in 2013 witnessed the terrible event of the Kedarnath flash flood, which was very disastrous and tragic, resulting in a serious stage modification of some geomorphological units in the watershed. Finally, for the African continent alone, the costs could amount to several billions of euros for insurers (Nsangou et al. 2022). Thus, the human desire to build in flooding areas risk appears in flagrant contradiction with the current phenomenon of flooding risk increase, which threatens many human lives and infrastructure (socio-economic, heritage, etc.).

Compared to other parts of the world, sub-Saharan Africa seems more vulnerable to these disasters due to poverty, poor governance and low technological level (Ahouangan et al. 2010). In the case of Cameroon, a study published by Centre for Research on the Epidemiology of Disasters (2016) states that 367,276 people were affected by floods between 2007 and 2015. Douala City is the most affected area due to its flat terrain, its exponential demographic growth (which leads to an anarchic occupation of spaces, including those reserved for the water circulation) and its proximity to the Atlantic Ocean, which is the origin of tidal phenomena. Between 2000 and 2010, floods caused more than 100 deaths and significant material damage in Douala City.

The fight against flooding begins with detailed and precise mapping of risk areas (Tehrany et al. 2015; Ebodé 2022b). Geographic information system (GIS) and remote sensing (RS) techniques and data have largely contributed to the analysis of natural hazards (Jaafari et al. 2014; Moel et al. 2014). Many studies relating to flooding risk area mapping have been carried out using GIS (Strobl et al. 2012; Pradhan et al. 2014). Among the most popular approaches in natural hazard modelling, we have frequency ratio (FR) (Tehrany et al. 2015; Samanta et al. 2018), analytical hierarchy process (Stefanidis & Stathis 2013), fuzzy logic, logistic regression, artificial neural networks (Kia et al. 2012; Lohani et al. 2012) and weights of evidence (Dahal et al. 2008). Among all these approaches, the FR could be considered one of the simplest and most effective in flooding risk area mapping (Liao & Carin 2009). It is a relatively new tool widely used for risk areas mapping several other complex natural disasters, such as landslides (Rahmati et al. 2016). These different reasons led us to adopt this approach in this study.

The Tongo Bassa watershed (TBW) is the largest and most populated in Douala City. Even though it is one of the basins most affected by flooding, the Tongo Bassa has so far only been the subject of a very small number of relevant works on this issue (Elong et al. 2022; Sone et al. 2023). In these works, the FR model has never been used despite its effectiveness and simplicity. Also, the mapping of homes located in areas corresponding to each level of exposure to flooding (very low, low, medium, high and very high) has never been addressed in the few existing studies carried out in this basin, despite the importance it has for decision-making during development and for prioritizing interventions in the event of a hazard.

This study therefore aims to (1) validate the FR model in the TBW and the region; (2) produce a reliable map of flooding risk areas in the TBW with this model and (3) map homes included in each level of exposure (very low, low, medium, high and very strong). The third objective was not easily achieved due to the difficulty of accessing key information, allowing us to go further in the analysis than the few existing studies (distribution of houses in the basin). These data make it possible to propose additional, more concrete tools for development (maps of homes included in the flooding risk area and whose occupants should be relocated to appropriate sites). These data were extracted from Google Earth.

Study area

The TBW is the largest and one of the most active on economic plans in Douala City (the economic capital of Cameroun). This cosmopolitan city of around 3 million inhabitants concentrates nearly 20% of the country's urban population and is the most populous city in Central Africa. The port of Douala is the main gateway to Cameroon and indeed the Central African sub-region, and serves several neighbouring countries. Delimited at its outlet located at the confluence with the Wouri (Figure 1), the TBW has an area of approximately 44.12 km2. Oriented East-West, this town (Douala) is located between 4°2′0″ and 4°5′30″ North latitude and between 9°43′0″ and 9°47′20″ East longitude (Figure 1). Its hydrographic network is dendritic. The climate there is hot and humid equatorial, with temperatures between 21 and 36°C, and abundant annual precipitation (3,593 mm on average) (Ebodé 2022c). This precipitation falls during a single season, which extends from March to November. Geologically, the TBW is dominated by Mio-Pliocene sandy formations, which are favourable to infiltration and rapid waterlogging of soil (Boum-Nkot et al. 2015). These formations are covered by ferralitic soils, predominantly yellowish and sometimes variegated sandy-clayey. On these soils, vegetation grows, mainly consisting of mangrove forests. Relief presents a relatively monotonous appearance in this basin.
Figure 1

Location map of the TBW.

Figure 1

Location map of the TBW.

Close modal

Data sources

The data used in this study include a Sentinel II satellite image, a digital terrain model (light detection and ranging (LIDAR) data), the Food Agriculture Organization (FAO) soil map and historical flooding points (Table 1 and Figure 2). These data made it possible to obtain six independent variables (LULC patterns, altitudes, slopes, distance from watercourses, drainage densities and soil types) (Figure 2), which were subsequently crossed to generate a map of flooding risk areas of the investigated basin. These different variables are considered by some authors (Costache et al. 2020; Nsangou et al. 2022) to be the main factors in the occurrence of floods in a given region since they influence runoff and water infiltration.
Table 1

Data used for the mapping of flooding risk areas in the TBW

DataUseScaleSources
Sentinel II satellite image LULC map 10 m Earth Explorer 
LIDAR data Maps of elevations, slopes, stream distances and drainage densities 10 m Urban community of Douala 
FAO soil map Soil map 1/500,000 FAO 
Historical flood points Realization and validation of the flooding risk areas map — Urban community of Douala 
DataUseScaleSources
Sentinel II satellite image LULC map 10 m Earth Explorer 
LIDAR data Maps of elevations, slopes, stream distances and drainage densities 10 m Urban community of Douala 
FAO soil map Soil map 1/500,000 FAO 
Historical flood points Realization and validation of the flooding risk areas map — Urban community of Douala 
Figure 2

Study workflow.

Data analysis

Supervised classification by maximum likelihood made it possible to produce a map of the main LULC patterns of the basin using ENVI 5.2 software. This operation was preceded by preprocessing and object recognition operations in the field using photography and global positioning system (GPS). Preprocessing of satellite images refers to all the processes applied to raw data to correct geometric and radiometric errors that characterize some satellite images (Ebodé 2023). These errors are generally due to technical problems with satellites and interactions between outgoing electromagnetic radiation and atmospheric aerosols, also called ‘atmospheric noise’. The downloaded Sentinel II image being orthorectified, the preprocessing concerned the atmospheric correction of this image and its reprojection into the local system (WGS_84_UTM_Zone_32N). For this, neo-channels are created to increase the readability of the data by enhancing less obvious properties in the original image, thus showing the scene elements more clearly. Three indices are therefore created, namely normalized difference vegetation index (NDVI), brightness index (BI) and normalized difference water index (NDWI). We chose these indices because they respectively highlight vegetated surfaces and sterile (non-chlorophyll) elements such as urban areas and water bodies. This allowed us to make much more precise supervised classifications (close to reality). Their formulas are as follows:
formula
(1)
formula
(2)
formula
(3)

Where NIR is the surface ground reflectance in the near-infrared channel; R is the ground reflectance of the surface in the red channel and MWIR is the ground reflectance of the surface in the mid-infrared channel. The use of Google Earth, as well as the areas sampled from GPS, made it possible to identify with certainty impervious areas (built-up areas, savannahs, bare soils and crops), water bodies (large rivers, lakes and ponds) and forests. Before classification operation, the separability of the spectral signatures of the sampled objects to avoid interclass confusion was evaluated, by calculating the ‘transformed divergence’ index. The value of this index is between 0 and 2. A value > 1.8 indicates good separability between two given classes. The different classes used in this study show good separability between them, with indices >1.9.

Maps of altitudes, slopes, drainage densities and distances of watercourses were automatically generated using ArcGIS 10.5 software. The soil map of the basin was extracted from the FAO world soil map. Mapping historical flood points is also essential for determining flooding risk areas. In total, 231 historical flood points were used for the calibration and validation of the FR model selected for this study. This model establishes the relationship between the occurrence of flooding and various variables recognized as causing flooding. The values of the FR indices are calculated according to the formula:
formula
(4)
where and are, respectively, the flood susceptibility index and the frequency ratio for each factor/variable.
The is the ratio of the flooded area to the total study area, which demarcates the flooded area from the non-flooded area (Samanta et al. 2018). It is calculated according to the formula:
formula
(5)
where E is the number of floods for each factor/variable; F is the total number of floods; M is the area of each factor and L is the total area of the study area.

The FR values indicate the types of correlation between different factors and flooding. A FR value greater than 1 indicates a strong correlation with flooding; on the other hand, a value less than 1 indicates a weak correlation with flooding. Table 2 shows the value of the FR of each of the classes of different variables.

Table 2

Variables used for determining flooding risk areas from the FR model

Independent variablesClassesAreas (km2)Basin occupancy rate (%)Number of floodsFR
LULC Built and road 36.4 82.5 173 1.04 
Bare soil and crop 6.6 14.95 23 0.76 
Forest 2.28 0.6 
Water 0.12 0.27 1.83 
Altitudes (m) −1 to13 9.22 20.89 188 4.49 
14–25 12.4 28.1 12 0.21 
26–37 10.5 23.82 
38–57 12 27.19 
Slopes (%) 0–3 24 54.39 107 1.01 
4–7 13 29.46 51 0.86 
8–14 5.7 12.94 27 1.04 
15–42 1.42 3.21 15 2.33 
Drainage density (km/km244–53 15.86 18 0.56 
54–63 28.8 65.29 147 1.12 
64–72 7.7 17.45 22 0.63 
73–82 0.62 1.4 13 4.62 
Distance from rivers (m) 0–142 23.7 53.71 184 1.71 
143–283 14.7 33.31 16 0.24 
284–425 11.35 0.04 
426–567 0.72 1.63 
Soils Fx1-1a-583 (Sandy-loamy) 19.92 45.14 
J4-a-674 (Clayey) 24.2 54.86 200 1.82 
Independent variablesClassesAreas (km2)Basin occupancy rate (%)Number of floodsFR
LULC Built and road 36.4 82.5 173 1.04 
Bare soil and crop 6.6 14.95 23 0.76 
Forest 2.28 0.6 
Water 0.12 0.27 1.83 
Altitudes (m) −1 to13 9.22 20.89 188 4.49 
14–25 12.4 28.1 12 0.21 
26–37 10.5 23.82 
38–57 12 27.19 
Slopes (%) 0–3 24 54.39 107 1.01 
4–7 13 29.46 51 0.86 
8–14 5.7 12.94 27 1.04 
15–42 1.42 3.21 15 2.33 
Drainage density (km/km244–53 15.86 18 0.56 
54–63 28.8 65.29 147 1.12 
64–72 7.7 17.45 22 0.63 
73–82 0.62 1.4 13 4.62 
Distance from rivers (m) 0–142 23.7 53.71 184 1.71 
143–283 14.7 33.31 16 0.24 
284–425 11.35 0.04 
426–567 0.72 1.63 
Soils Fx1-1a-583 (Sandy-loamy) 19.92 45.14 
J4-a-674 (Clayey) 24.2 54.86 200 1.82 

To obtain the flooding risk area map, all variables are converted to raster format. The spatial resolution of each of the rasters was defined on a cell size of 10 × 10 m, and it is integrated into the ArcGIS database. This integrated database has been reclassified into five classes of flood sensitivity, namely very low, low, medium, high and very high.

Links between independent variables and flood occurrence

The six variables retained for flooding risk areas mapping in this study each include several classes (Table 2 and Figures 38). Four classes appear on the LULC map, notably buildings and roads, bare soil and crops, forests and water bodies (Figure 3). Among these classes, only water bodies (0.27% of the basin) and buildings (82.5% of the basin) are well correlated with flooding. Their FRs are 1.83 and 1.04, respectively (Table 2). The FRs of the other two classes are less than 1.
Figure 3

Spatial distribution of the main LULC patterns in the TBW.

Figure 3

Spatial distribution of the main LULC patterns in the TBW.

Close modal
Figure 4

Elevations map of the TBW.

Figure 4

Elevations map of the TBW.

Close modal
Figure 5

Slopes map of the TBW.

Figure 5

Slopes map of the TBW.

Close modal
Figure 6

Drainage densities map of the TBW.

Figure 6

Drainage densities map of the TBW.

Close modal
Figure 7

Distance from rivers map of the TBW.

Figure 7

Distance from rivers map of the TBW.

Close modal
Figure 8

Soils map of the TBW.

Figure 8

Soils map of the TBW.

Close modal

As in the case of LULC patterns, the altitudes of the basin have been reclassified into four categories (–1 to 13 m; 14–25 m; 26–37 m and 38–57 m) (Figure 4). Only the –1 to 13 m class (20.89% of the total basin surface) is well correlated with flooding, with a significant FR of 4.49 (Table 2).

The slopes of the basin were also divided into four classes (0–3%; 4–7%; 8–14% and 15–42%) (Figure 5). Only the 4–7% slope class is poorly correlated with flooding. The other three classes are well correlated with flooding. Their FRs are greater than 1 (Table 2). The most important FR is that of the class of slopes 15–47%, which occupy 3.21% of the total area of the basin (Table 2).

The drainage densities of the basin are divided into four classes (44–53 km/km2; 54–63 km/km2; 64–72 km/km2 and 73–82 km/km2; Figure 6). Two of them (54–63 km/km2 and 73–82 km/km2) appear to be well correlated with flooding, with respective FRs of 1.12 and 4.62, and respective basin occupancy rates of 65.29 and 1.4% (Table 2).

Four classes (0–142 m; 143–283 m; 284–425 m and 426–567 m) also appear on the map of distances to watercourses (Figure 7). The 0–142 m class, whose basin occupancy rate is 53.71%, is the only one for which the FR is greater than 1 (Table 2).

The soils of the investigated basin are divided into only two classes (sandy-loamy and clayey) (Figure 8). Unlike sandy-loamy soils, clayey soils (54.86% of the basin) are well correlated with flooding, with a FR of 1.82 (Table 2).

Flooding risk areas mapping

The FR values calculated for each parameter vary between 0 and 4.62 in the study area (Table 2). A FR value less than 1 indicates a weak correlation with flooding and the opposite otherwise (Lee et al. 2012). Subsequently, a flood susceptibility database was developed using the FR model equation (Equation (4)). The database created was reclassified into five flood sensitivity categories, namely very low, low, medium, high and very high. Figure 9 shows the flood sensitivity classes. Flood sensitivity analysis indicates that 1.41, 8.88, 28.51, 33.86 and 27.33% of the basin area are, respectively, demarcated into very low, low, medium, high and very high flood vulnerability classes (Table 3).
Table 3

Statistics relating to the spatial distribution and degree of precision of flooding risk areas in the TBW

Flood risk areasAreas (km2)Basin occupancy rate (%)Flood points used for modelling (200)
Flood points used for validation (31)
Number%Number%
Very low 0.62 1.41 0.00 0.00 
Low 3.92 8.88 1.50 0.00 
Medium 12.58 28.51 19 9.50 3.22 
High 14.94 33.86 50 25.00 16.13 
Very high 12.06 27.33 128 64.00 25 80.65 
Total 44.12 100.00 200 100.00 31 100.00 
Total precision (%) — — — 89 — 96.78 
Flood risk areasAreas (km2)Basin occupancy rate (%)Flood points used for modelling (200)
Flood points used for validation (31)
Number%Number%
Very low 0.62 1.41 0.00 0.00 
Low 3.92 8.88 1.50 0.00 
Medium 12.58 28.51 19 9.50 3.22 
High 14.94 33.86 50 25.00 16.13 
Very high 12.06 27.33 128 64.00 25 80.65 
Total 44.12 100.00 200 100.00 31 100.00 
Total precision (%) — — — 89 — 96.78 

Note: The calculation of the total degree of accuracy only includes the areas with high and very high flooding risk.

Figure 9

Map of flooding risk areas in the TBW.

Figure 9

Map of flooding risk areas in the TBW.

Close modal
Figure 10

Spatial distribution of houses included in the studied basin in 2022 and in the different flood risk areas (B: very low; C: low; D: medium; E: high and F: very high).

Figure 10

Spatial distribution of houses included in the studied basin in 2022 and in the different flood risk areas (B: very low; C: low; D: medium; E: high and F: very high).

Close modal

High and very high flooding risk areas are where flooding is most likely to occur. They are characterized by significant imperviousness, low altitudes, weak slopes, significant proximity to watercourses and clayey soils.

It has already been shown in other studies around the world that low altitudes and slopes, proximity to watercourses and imperviousness present the most important links with flooding (Esteves 2013; Colmet-Daage et al. 2017; Sone et al. 2023).

FR model validation

To validate the FR model, it is important to calculate the success rate and accuracy of the predictions. The success rate was calculated using 200 historical flood points. The prediction accuracy was calculated using 31 historical flood points. Future flooding is most likely to occur in areas with high and very high flood risk (Samanta et al. 2018). The success and accuracy rates of the predictions are 89 and 96.78% (Table 3). Such prediction accuracy validates the FR model in the studied watershed and proves at the same time that it is suitable for flooding risk areas mapping in the studied region.

As is the case in this study, the FR model has already been used to reliably map flooding risk areas in other basins around the world (Liao & Carin 2009; Pradhan & Youssef 2011; Lee et al. 2012; Samanta et al. 2018).

Quantity of houses included in each flood exposure level

The exposure levels which include the greatest number of houses are in the following order: high (25,712 houses or 38.5%), medium (20,509 houses or 30.72%), very high (16,268 houses or 24.42%), low (2,835 houses or 4.24%) and very low (1,418 or 2.12%) (Table 4). Most of the houses included in the flooding zone are located to the South and Southwest of the basin, regardless of the exposure level. More than half of the houses in the basin are located in high and very high-risk zones (41,980 houses or 62.92%), which are the exposure levels with the greatest impacts for which measures must be taken.

Table 4

Number of houses included in each exposure level

Flood risk areasNumber of houses%
Very low 1,418 2.12 
Low 2,835 4.24 
Medium 20,509 30.72 
High 25,712 38.5 
Very high 16,268 24.42 
Total 66,742 100 
Flood risk areasNumber of houses%
Very low 1,418 2.12 
Low 2,835 4.24 
Medium 20,509 30.72 
High 25,712 38.5 
Very high 16,268 24.42 
Total 66,742 100 

Most authors who have dealt with flooding in their work generally limit themselves to producing a map of risk areas using various methods (Chen et al. 2011; Lohani et al. 2012; Zou et al. 2013; Haghizadeh et al. 2017). The rate of houses included in each exposure level (very low, low, medium, high and very high) is a dimension of analysis that is missing in this work. However, this information can help to get an idea of the impact level of flooding on the populations of a given basin and the prioritization of interventions during different hazards. It can indeed happen that the zone at high risk of flooding has a large surface area in a basin, but that the population is mainly settled in the zones at medium and low risk. This reflects the low vulnerability of the population to flooding. On the other hand, if the majority of the population is settled in areas with a high and very high flooding risk, as is the case in this study, this implies high vulnerability and therefore, urgent decision-making. It is crucial to always cross information relating to the different levels of exposure with that of houses to get a clear idea of the current degree of vulnerability of the different parts of a given area and thus be able to prioritize interventions in the event of a hazard.

This study aimed to map flooding risk areas in the TBW using the GIS technique, field data (historical flood points), RS (Landsat image) data and the FR model. For this, six independent variables influencing floods were retained (LULC patterns, altitudes, slopes, drainage densities, distances from watercourses and soil types). At its end, it appears that areas with high and very high risk of flooding (those where flooding is most likely to occur) occupy more than half of the basin. Their respective basin occupancy rates are 33.86 and 27.33%. These areas are characterized by significant imperviousness, low altitudes, weak slopes, significant proximity to watercourses and clayey soils. Most of the houses in the basin (66.92%) are located in areas affected by these two levels of exposure (high and very high) (Figure 10). With respective success and prediction accuracy rates of 89 and 96.78%, the map of flood risk zones produced for the investigated basin deserves a certain amount of confidence to be placed in it. This tool can be validly used by the public authorities to fight against flooding in this basin. Although the results of this study are satisfactory overall, the inclusion of some key parameters, such as geology, would have made it possible to have significantly better results. Field studies are necessary to have a complete database on this basin and the region, which would make it possible to further refine the analyses in such studies.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

Asinya
A. E.
&
Alam
M. J.
2021
Flood risk in rivers: Climate driven or morphological adjustment
.
Earth Systems and Environment
.
doi:10.1007/s41748-021-00257-y
.
Boum-Nkot
N.
,
Ketchemen-Tandia
B.
,
Ndje
Y.
,
Emvouttou
H.
,
Ebonji
C. R.
&
Huneau
F.
2015
Origin of mineralization of groundwater in the Tongo Bassa Watershed (Douala-Cameroon)
.
Research Journal of Environmental and Earth Sciences
7
(
2
),
29
41
.
Chen
J.
,
Shi
X.
,
Gu
L.
,
Wu
G.
,
Su
T.
,
Wang
H.
,
Kim
J.
,
Zhang
L.
&
Xiong
L.
2023
Impacts of climate warming on global floods and their implication to current flood defense standards
.
Journal of Hydrology
618
,
129236
.
doi:10.1016/j.jhydrol.2023.129236
.
Chomba
I.
,
Banda
K.
,
Winsemius
M.
,
Sichingabula
H.
&
Nyambe
I.
2022
Integrated hydrologic-hydrodynamic inundation modeling in a groundwater dependent tropical floodplain
.
Journal of Human, Earth, and Future
3
,
2
.
doi:10.28991/HEF-2022-03-02-09
.
Colmet-Daage
A.
,
Cécile
L.
,
Borrell-Estupina
V.
,
Servat
E.
,
Gomez
E. S.
&
Ricci
S.
2017
Climate change impacts on extreme rainfalls, discharges and floods in Mediterranean catchments. In IAHS 2017 Scientific Assembly, July 2017, Port Elizabeth, South Africa. ⟨hal-02104194⟩
.
Costache
R.
,
Țîncu
R.
,
Elkhrachy
I.
,
Pham
Q.
,
Popa
C.
,
Diaconu
D.
,
Avand
M.
,
Costache
I.
,
Arabameri
I.
&
Bui
T.
2020
New neural fuzzy-based machine learning ensemble for enhancing the prediction accuracy of flood susceptibility mapping
.
Hydrological Sciences Journal
65
(
16
),
2816
2837
.
doi:10.1080/02626667.2020.1842412
.
Centre for Research on the Epidemiology of Disasters
2016
Poverty & Death: Disaster Mortality, 1996–2015
.
Centre for Research on the Epidemiology of Disasters
,
Brussels, Belgium
.
Centre for Research on the Epidemiology of Disasters
2018
Natural Disasters in 2018: Lower Mortality, Higher Cost
.
CRED crunch, N° 29: 2018
, p.
6
.
Ebodé
V. B.
,
Dzana
J. G.
,
Nkiaka
E.
,
Nka
N. B.
,
Braun
J. J.
&
Riotte
J.
2022
Effects of climate and anthropogenic changes on current and future variability in flows in the So'o River Basin (south of Cameroon)
.
Hydrology Research
53
(
9
),
1203
1220
.
doi:10.2166/nh.2022.047
.
Ekwueme
B. N.
2022
Machine learning based prediction of urban flood susceptibility from selected rivers in a tropical catchment area
.
Civil Engineering Journal
8
,
9
.
doi:10.28991/CEJ-2022-08-09-08
.
Elong
A. J.
,
Zhou
L.
,
Karney
B.
,
Fang
H.
,
Cao
Y.
&
Assam
S. L. Z.
2022
Flood prediction with two-dimensional shallow water equations: A case study of Tongo-Bassa watershed in Cameroon
.
Applied Science
12
,
11622
.
doi:10.3390/app122211622
.
Haghizadeh
A.
,
Siahkamari
S.
,
Haghiabi
A. H.
&
Rahmati
O.
2017
Forecasting flood-prone areas using Shannon's entropy model
.
Journal of Earth System Science
126
,
39
.
doi:10.1007/s12040-017-0819-x
.
Jaafari
A.
,
Najafi
A.
,
Pourghasemi
H.
,
Rezaeian
J.
&
Sattarian
A.
2014
Rapport de fréquence basé sur SIG et indice des modèles d'entropie pour l'évaluation de la sensibilité aux glissements de terrain dans la forêt caspienne, nord de l'Iran
.
International Journal of Environmental Science and Technology
11
,
909
926
.
Kia
M.
,
Pirasteh
S.
,
Pradhan
B.
,
Rodzi
M.
,
Sulaiman
W.
&
Moradi
A.
2012
An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia
. Environmental Earth Sciences
67
,
251
264
.
Krisnayanti
D.
,
Rozari
P.
,
Garu
V.
,
Damayanti
A.
,
Legono
D.
&
Nurdi
H.
2022
Analysis of flood discharge due to impact of tropical cyclone
.
Civil Engineering Journal
8
,
9
.
doi:10.28991/CEJ-2022-08-09-01
.
Lee
M. J.
,
Kang
J. E.
&
Jeon
S.
2012
Application of frequency ratio model and validation for predictive flooded area susceptibility mapping using GIS. In Proceedings of Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International. Munich, pp. 895–898
.
Liao
X.
&
Carin
L.
2009
Migratory logistic regression for learning concept drift between two data sets with application to UXO sensing
.
IEEE Transactions on Geoscience and Remote Sensing
47
,
1454
1466
.
Manzoor
Z.
,
Ehsan
M.
,
Khan
M.
,
Manzoor
A.
,
Akhter
M.
,
Sohail
M.
,
Hussein
A.
&
Shafi
A.
2022
Floods and flood management and its socio-economic impact on Pakistan: A review of the empirical literature
.
Frontiers in Environmental Science
10
.
doi:10.3389/fenvs.2022.1021862
.
Moel
H. D.
,
Vliet
M.
&
Aerts
J.
2014
Évaluation de l'effet des mesures de réduction des dommages causés par les inondations: Une étude de cas de la zone non remblayée de Rotterdam, aux Pays-Bas
.
Modification de l'Environnement de Réglementation
14
,
895
908
.
Nsangou
D.
,
Kpoumié
A.
,
Mfonka
Z.
,
Bateni
S.
,
Ngouh
A.
&
Ndam Ngoupayou
J.
2022
The Mfoundi watershed at Yaounde in the humid tropical zone of Cameroon: A case study of urban flood susceptibility mapping
.
Earth Systems and Environment
6
,
99
120
.
doi:10.1007/s41748-021-00276-9
.
Pradhan
B.
&
Youssef
A.
2011
Cartographie de la susceptibilité aux crues maximales sur 100 ans à l'aide de modèles hydrologiques et hydrodynamiques intégrés : Couloir de la rivière Kelantan, Malaisie
.
Gestion des Risques d'Inondation
4
,
189
202
.
Pradhan
B.
,
Hagemann
U.
,
Tehrany
M.
&
Prechtel
N.
2014
An easy to use ArcMap based texture analysis program for extraction of flooded areas from TerraSAR-X satellite image
.
Comput. Geosci.
63
,
34
43
.
Rahmati
O.
,
Pourghasemi
H.
&
Zeinivand
H.
2016
Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran
.
Geocarto International.
doi:10.1080/10106049.2015.1041559
.
Samanta
S.
,
Pal
D. K.
&
Palsamanta
B.
2018
Flood susceptibility analysis through remote sensing GIS and frequency ratio model
.
Applied Water Science
8
,
66
.
Sone
E. W.
,
Onguéné
R.
,
Ndongo
B.
,
Nshagali
G.
,
Colmet-Daage
A.
,
Marie
G.
,
Iroumé
J.
,
Stieglitz
T.
,
Besck
F.
,
Efole Ewoukem
T.
,
Tomedi Eyango
M.
,
Etamé
J.
&
Braun
J. J.
2023
Using GIS and multicriteria analysis to map flood risk areas of the Tongo Bassa River Basin (Douala, Cameroon)
.
Journal of Coastal Research
39
(
3
),
531
543
.
doi:10.2112/JCOASTRES-D-22-00019.1
.
Strobl
R. O.
,
Forte
F.
&
Lonigro
T.
2012
Comparaison de la faisabilité de trois techniques de délimitation de l'étendue des risques d'inondation à l'aide du système d'information géographique: Étude de cas à Tavoliere delle Puglie, Italie
.
Gestion des Risques d'Inondation
5
,
245
257
.
Tehrany
M. S.
,
Pradhan
B.
&
Jebur
M. N.
2015
Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method
.
Stochastic Environmental Research and Risk Assessment
29
,
1149
1165
.
doi:10.1007/s00477-015-1021-9
.
UN
.
2018
World Urbanization Prospects 2018, New York: United Nations Department of Economic and Social Affairs (UN-DESA), Population Division. May (16) Retrieved From United Nations Department of Economic and Social Affairs.
Vidhee
A.
&
Amit
G.
2020
Implications of land use transitions and climate change on local flooding in urban areas: An assessment of 42 Indian cities
.
Land Use Policy
95
,
104571
.
doi:10.1016/jlandusepol.2020.104571
.
Zou
Q.
,
Zhou
J.
,
Zhou
C.
,
Song
L.
&
Guo
J.
2013
Comprehensive flood risk assessment based on set pair analysis-variable fuzzy sets model and fuzzy AHP
.
Stochastic Environmental Research and Risk Assessment
27
(
2
),
525
546
.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).