# Flash Flood Susceptibility Mapping in Sinai, Egypt Using Hydromorphic Data, Principal Component Analysis and Logistic Regression

^{1}

^{2}

^{3}

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^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

^{2}in size and its largest dimensions are about 385 km from north to south and 210 km from west to east. Geographically, Sinai can be divided into three parts. The northern part consists of broad coastal plains with fossil beaches and extensive sand dunes, some of which are more than 100 m high. The main part is the wadi El-Arish basin which descends from an altitude of more than 900 m to the Mediterranean Sea and forms the largest valley of the Sinai Peninsula. The center is highland mainly composed of two limestone plateaus, El-Tih in the south and El-Egma in the north, where the sources of the wadi Al-Arish arise. The southern part consists of high and rugged mountain ranges of igneous rock, reaching more than 2400 m, with Mount Catherine at 2642 m above sea level being the highest point in Egypt.

#### 2.2. Hydro-Morphometric Parameters

^{2}for the upslope drainage area as the starting point of first order streams, which corresponds 5000 grid cells, a standard recommended by ArcGIS spatial analysis tools (Available online: https://pro.arcgis.com; accessed on 22 January 2022). Sub-basins are delineated based on stream orders and hydro-morphometric parameters are derived for each sub-basin using standard spatial analyses methods and equations as listed in Table 1.

- Area (A): surface of a drainage basin, which is a prime determinant of the total discharge [31]; large catchments receive more precipitation and have a higher peak discharge compared to smaller catchments.
- Perimeter (P): circumference of a drainage basin; there are no clear indications of direct significance for the hydrological regime, but it is used in the determination of other parameters.
- Basin length (L
_{b}): maximum distance from the catchment boundary to the outlet; a good indicator of the concentration time of a flood wave [11]. - Form factor (F
_{f}): ratio of the width to the length of a catchment and indicative of the flood regime [11]; large form factors lead to shorter lag times and a higher peak discharge. - Elongation ratio (R
_{e}): ratio of the diameter of a circle with the same area as the catchment area to the maximum catchment length [16]; low values mean less circular shape and longer flood concentration time.

- Stream order (S
_{u}): highest stream order in a basin according to the method designed by [14]; it is an indicative parameter of the basin dimensions, channel size and stream discharge. - Stream number (N
_{u}): total number of stream segments of all orders [15]; a high stream number is expected to imply faster peak flow. - Bifurcation ratio (R
_{b}): average ratio between the number of streams of one order and those of the next higher order [12]; indicative of the complexity of a catchment, but according to [14] less so for the flow regime, although [12] considers flooding more likely in catchments with a higher bifurcation ratio. - Drainage density (D
_{d}): length of streams per unit area; an indicator of infiltration and permeability of a drainage [11]. - Length of overland flow (L
_{o}): the average length of overland flow is equal to the reciprocal of twice the drainage density [16]; low values indicate shorter flow paths, making the basin more prone to flash flooding. - Texture ratio (R
_{t}): total number of first order streams per basin circumference; indicates coarse, medium, or fine textured topography [40].

- Basin relief (R
_{f}): height difference of the lowest and highest points of a basin and an essential indicator of surface runoff [16]. - Relief ratio (R
_{r}): ratio of the basin relief to the basin length and a key element for understanding erosion and drainage [16]. - Ruggedness number (R
_{n}): product of drainage density and basin relief; regions prone to flash flooding have higher ruggedness numbers, indicating high drainage density combined with steep slopes [38]. - Mean basin slope (S): major factor controlling infiltration and surface runoff and the resulting runoff rate and concentration time.

#### 2.3. Principal Component Analysis

#### 2.4. Logistic Regression

## 3. Results

#### 3.1. Drainage Catchments and Hydro-Morphometric Data

#### 3.2. Principal Component Analysis

_{b}) which are directly related to the size of a watershed, and with stream order (S

_{u}), stream number (N

_{u}), stream length (L

_{u}) and texture ratio (R

_{t}) which are also indirectly related to size. Thus, the first and most important principal component represents the effect of basin size and accounts for 37% of the variation in the data. The second component is strongly correlated with all relief parameters: basin relief (R

_{f}), relief ratio (R

_{r}), ruggedness number (R

_{n}) and mean basin slope (S); this component accounts for 19% of the variation in the data. The third component, which accounts for 15% of the total variance, is strongly correlated with drainage density (D

_{d}) and length of overland flow (L

_{o}). This component thus represents the drainage capacity of a river basin. The fourth component accounts for 14% of the total variance and is strongly correlated with the form factor (F

_{f}), compactness coefficient (C

_{c}) and elongation ratio (R

_{e}), so this component expresses the influence of the basin shape. The fifth component accounts for only 5% of the variance but is rather special in that it is only significantly correlated with the bifurcation ratio (R

_{b}). Thus, this component represents the effect of stream bifurcation, which is apparently a unique basin property unrelated to any other hydro-morphometric parameter. Note that the stream frequency (F

_{s}) is not significantly correlated with any of the PCs and thus contributes little to the information contained in the data. Principal component values for all basins are given Table S3 in the Supplementary Materials.

#### 3.3. Logistic Regression

_{1}and PC

_{4}and not for PC

_{3}and PC

_{5}, while PC

_{2}is very close to the threshold. However, the assumption of a normal distribution is not reliable if the sample size is small, as in this case. The next two columns in the table provide the deviation and AIC values, which indicate how well the model with the selected predictors fits the observations. The values corresponding to the intercept are for the null model, which is a logistic model with only an intercept and no predictors that is used as a reference to compare with other models. The values corresponding to the predictors are for excluding that predictor from the full model and the values on the last line are for the total full model. Both the deviance and AIC should be as small as possible. Comparison of the deviance and AIC obtained for the total model and for the null model shows a large difference, indicating that the predictors allow significant improvement in goodness of fit. Comparison of the deviance and AIC when one of the predictors is removed from the full model shows that all predictors are relevant and should not be removed from the model. The increase in deviance when one of the predictors is removed compared to the full model also indicates the importance of that predictor in the model. It follows that the order of importance of the predictors is: PC

_{1}, PC

_{4}, PC

_{2}, PC

_{5}and PC

_{3}, as given in the last column of Table 6.

_{0}= −4.74 (Table 6). Note that all basin where floods have been observed are on the right side of this line, while all basins predicted by the model on the left of this line have a near zero predicted flooding probability. Basins predicted to the right of the red line are thus prone to flooding with a probability that increases the further they are from this line. The blue dotted line in the graph shows the average outcome of the observations, logit(p) = ln(9/103) = −2.44. All basins plotted to the right of this line have a higher probability of flooding than is observed on average, and vice versa. Seven of the basins where flash floods have been observed are to the right of this line. Additionally, to the right of this line are 15 basins where no flooding has been observed, so these basins have features that indicate a higher probability of flash flooding than observed. The black dotted line represents logit(p) equal to zero (p = 0.5). There are only six basins with a predicted logit(p) greater than zero (p > 0.5) and thus very sensitive to flash flooding; five of these, wadis Kid, El-Aawag, Feiran upstream, Dahab and Watir, are basins where flooding has been observed and one, wadi Feiran downstream, where no flooding has been assumed but has similar characteristics to the other five.

## 4. Discussion

^{2}, or 15% of the total area of Sinai. The high sensitivity zone is mainly in the center of the Sinai Peninsula and some scattered areas further north. It encompasses 16 river basins, covering a total area of approximately 15,000 km

^{2}or 28% of the Sinai area, including some upstream sub-basins of wadi El-Arish, sub-basins of wadi Dahab and Watir, and the basins of wadis Sedri, Garf and Werdan which drain into the Gulf of Suez. The basins in the north are wadi El-Beada in Bir Al-Abd, which drains to the Mediterranean, and two sub-basins of wadi El-Harish in El-Hasana and Quasisma, respectively, which may be the source of the flash floods that have been observed in this wadi. The moderate sensitivity zone is mainly located in the north of the Sinai Peninsula and includes 36 watersheds with a total area of about 21,000 km

^{2}or 39% of the Sinai area. These are usually smaller to medium sized basins located in flatter areas. The low-sensitive zone comprises 45 catchments with a total area of approximately 10,000 km

^{2}, or 18% of the Sinai area. These are usually very small basins on flat terrains with short drainage paths and few branches. Most are located in the north and center along the periphery of the Sinai Peninsula; some are also found in the southern part of Sinai. The resulting flash flood sensitivity map is largely consistent with flash flood observations that occurred in different regions of the Sinai and with the findings or predictions of other studies [6,9,10,31,34].

## 5. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- WMO. World Meteorological Organization: Flash Flood Guidance System (FFGS) with Global Coverage. 2016. Available online: https://community.wmo.int/hydrology-and-water-resources/flash-flood-guidance-system-ffgs-global-coverage (accessed on 1 May 2022).
- El Gohary, R. Environmental Flash Flood Management in Egypt. In Flash Floods in Egypt; Negm, E.E., Ed.; Advances in Science, Technology & Innovation; Springer: New York, NY, USA, 2020; pp. 85–105. [Google Scholar]
- Elnazer, A.A.; Salman, S.A.; Asmoay, A.S. Flash flood hazard affected Ras Gharib city, Red Sea, Egypt: A proposed flash flood channel. Nat. Haz.
**2017**, 89, 1389–1400. [Google Scholar] [CrossRef] - Kamel, M.; Arfa, M. Integration of remotely sensed and seismicity data for geo-natural hazard assessment along the Red Sea Coast, Egypt. Arab. J. Geosci.
**2020**, 13, 1195. [Google Scholar] [CrossRef] - Abdel-Fattah, M.; Kantoush, S.; Sumi, T. Integrated Management of Flash Flood in Wadi System of Egypt: Disaster Preventing and Water Harvesting. Annu. Disas. Prev. Res. Inst. Kyoto Univ.
**2015**, 58, 485–496. [Google Scholar] - Omran, E.-S.E. Egypt’s Sinai Desert Cries: Flash Flood Hazard, Vulnerability, and Mitigation. In Flash Floods in Egypt; Negm, E.E., Ed.; Advances in Science, Technology & Innovation; Springer: New York, NY, USA, 2020; pp. 215–236. [Google Scholar]
- Moawad, M.B. Analysis of the flash flood occurred on 18 January 2010 in wadi El Arish, Egypt (a case study). Geomat. Nat. Haz. Risk.
**2013**, 4, 254–274. [Google Scholar] [CrossRef] [Green Version] - Cools, J.; Vanderkimpen, P.; El-Afandi, G.; Abdelkhalek, A.; Fockedey, S.; El-Sammany, M.; Abdallah, G.; El-Bihery, M.; Bauwens, W.; Huygens, M. An early warning system for flash floods in hyper-arid Egypt. Nat. Haz. Earth Syst. Sci.
**2012**, 12, 443–457. [Google Scholar] [CrossRef] - Prama, M.; Omran, A.; Schröder, D.; Abouelmagd, A. Vulnerability assessment of flash floods in Wadi Dahab Basin, Egypt. Environ. Earth Sci.
**2020**, 79, 114. [Google Scholar] [CrossRef] [Green Version] - El-Fakharany, M.A.; Mansour, N.M. Morphometric analysis and flash floods hazards assessment for Wadi Al Aawag drainage Basins, southwest Sinai, Egypt. Environ. Earth Sci.
**2021**, 80, 168. [Google Scholar] [CrossRef] - Horton, R.E. Drainage-basin characteristics. Eos Trans. Am. Geophys. Union
**1932**, 13, 350–361. [Google Scholar] [CrossRef] - Horton, R.E. Erosional development of streams and their drainage basins; hydrophysical approach to quantitative morphology. Geol. Soc. Am. Bull.
**1945**, 56, 275–370. [Google Scholar] [CrossRef] [Green Version] - Smith, K.G. Standards for grading texture of erosional topography. Am. J. Sci.
**1950**, 248, 655–668. [Google Scholar] [CrossRef] - Strahler, A.N. Quantitative analysis of watershed geomorphology. Trans. Am. Geoph. Union
**1957**, 38, 913–920. [Google Scholar] [CrossRef] [Green Version] - Strahler, A.N. Quantitative Geomorphology of Drainage Basins and Channel Networks. In Handbook of Applied Hydrology; McGraw Hill Book Company: New York, NY, USA, 1964; p. 411. [Google Scholar]
- Schumm, S.A. Evolution of drainage systems and slopes in badlands at Perth Amboy, New Jersey. Bull. Geol. Soc. Am.
**1956**, 67, 597–646. [Google Scholar] [CrossRef] - Diaconu, D.C.; Costache, R.; Popa, M.C. An overview of flood risk analysis methods. Water
**2021**, 13, 474. [Google Scholar] [CrossRef] - Ali, K.; Bajracharyar, R.M.; Raut, N. Advances and challenges in flash flood risk assessment: A review. J. Geogr. Nat. Disast.
**2017**, 7, 195. [Google Scholar] [CrossRef] [Green Version] - Farhan, Y.; Anaba, O.; Salim, A. Morphometric analysis and flash floods assessment for drainage basins of the Ras En Naqb area, south Jordan using GIS. J. Geosci. Environ. Protect.
**2016**, 4, 9–33. [Google Scholar] [CrossRef] [Green Version] - Mahmood, S.; Rahman, A. Flash flood susceptibility modeling using geo-morphometric and hydrological approaches in Panjkora Basin, Eastern Hindu Kush, Pakistan. Environ. Earth Sci.
**2019**, 78, 43. [Google Scholar] [CrossRef] - Mahmood, S.; Rahman, A. Flash flood susceptibility modelling using geomorphometric approach in the Ushairy Basin, eastern Hindu Kush. J. Earth Syst. Sci.
**2019**, 128, 97. [Google Scholar] [CrossRef] [Green Version] - Bhat, M.S.; Alam, A.; Ahmad, S.; Farooq, H.; Ahmad, B. Flood hazard assessment of upper Jhelum basin using morphometric parameters. Environ. Earth Sci.
**2019**, 78, 54. [Google Scholar] [CrossRef] - Obeidat, M.; Awawdeh, M.; Al-Hatouli, F. Morphometric analysis and prioritisation of watersheds for flood risk management in Wadi Easal Basin (WEB), Jordan, using geospatial technologies. Flood Risk Manag.
**2021**, 14, e12711. [Google Scholar] [CrossRef] - Pangali Sharma, T.P.; Zhang, J.; Khanal, N.R.; Prodhan, F.A.; Nanzad, L.; Zhang, D.; Nepal, P. A geomorphic approach for identifying flash flood potential areas in the East Rapti River Basin of Nepal. ISPRS Int. J. Geo-Inf.
**2021**, 10, 247. [Google Scholar] [CrossRef] - Arnous, M.O.; Aboulela, H.; Green, D. Geo-environmental hazards assessment of the north western Gulf of Suez, Egypt. J. Coast. Conserv.
**2011**, 15, 37–50. [Google Scholar] [CrossRef] - Youssef, A.M.; Pradhan, B.; Hassan, A.M. Flash flood risk estimation along the St. Katherine road, southern Sinai, Egypt using GIS based morphometry and satellite imagery. Environ. Earth Sci.
**2011**, 62, 611–623. [Google Scholar] [CrossRef] - Abdel-Lattif, A.; Sherief, Y. Morphometric analysis and flash floods of Wadi Sudr and Wadi Wardan, Gulf of Suez, Egypt: Using digital elevation model. Arab. J. Geosci.
**2012**, 5, 181–195. [Google Scholar] [CrossRef] - Elewa, H.H.; Ramadan, E.M.; El-Feel, A.A.; Abu El-Ella, E.A.; Nosair, A.M. Runoff water harvesting optimization by using RS, GIS and watershed modelling in Wadi El-Arish, Sinai. Int. J. Eng. Res. Technol.
**2013**, 2, 1635–1648. [Google Scholar] - Abdalla, F.; El-Shamy, I.; Bamousa, A.; Mansour, A.; Mohamed, A.; Tahoon, M. Flash floods and groundwater recharge potentials in arid land alluvial basins, southern Red Sea coast, Egypt. Inter. J. Geosci.
**2014**, 5, 971–982. [Google Scholar] [CrossRef] [Green Version] - Abdel Ghaffar, M.K.; Abdellatif, A.D.; Azzam, M.A.; Riad, M.H. Watershed characteristic and potentiality of wadi El-Arish, Sinai, Egypt. Int. J. Adv. Remote Sens. GIS
**2015**, 4, 1070–1091. [Google Scholar] - Abuzied, S.; Yuan, M.; Ibrahim, S.; Kaiser, M.; Saleem, T. Geospatial risk assessment of flash floods in Nuweiba area, Egypt. J. Arid Environ.
**2016**, 133, 54–72. [Google Scholar] [CrossRef] - Abdelkareem, M. Targeting flash flood potential areas using remotely sensed data and GIS techniques. Nat. Haz.
**2017**, 85, 19–37. [Google Scholar] [CrossRef] - Elsadek, W.M.; Ibrahim, M.G.; Mahmod, W.E. Flash flood risk estimation of Wadi Qena Watershed, Egypt using GIS based morphometric analysis. Appl. Environ. Res.
**2018**, 40, 36–45. [Google Scholar] [CrossRef] [Green Version] - Abuzied, S.M.; Mansour, B.M.H. Geospatial hazard modeling for the delineation of flash flood-prone zones in Wadi Dahab basin, Egypt. J. Hydroinform.
**2019**, 21, 180–206. [Google Scholar] [CrossRef] [Green Version] - Elsadek, W.M.; Ibrahim, M.G.; Mahmod, W.E.; Kanae, S. Developing an overall assessment map for flood hazard on large area watershed using multi-method approach: Case study of Wadi Qena watershed, Egypt. Nat. Haz.
**2019**, 95, 739–767. [Google Scholar] [CrossRef] - Davis, J. Statics and Data Analysis in Geology; Wiley: New York. NY, USA, 1975. [Google Scholar]
- Abrams, M.; Crippen, R.; Fujisada, H. ASTER Global Digital Elevation Model (GDEM) and ASTER Global Water Body Dataset (ASTWBD). Remote Sens.
**2020**, 12, 1156. [Google Scholar] [CrossRef] [Green Version] - Melton, M. An Analysis of the Relations among Elements of Climate, Surface Properties and Geomorphology; Department of Geology, Columbia University, Technical Report, 11, Project NR 389-042; Office of Navy Research: New York, NY, USA, 1957. [Google Scholar] [CrossRef]
- Strahler, A.N. Hypsometric (area-altitude) analysis of erosional topography. Geol. Soc. Am. Bull.
**1952**, 63, 1117–1142. [Google Scholar] [CrossRef] - Smith, K.G. Erosional processes and landforms in badlands national monument, South Dakota. Bull. Geol. Soc. Am.
**1958**, 69, 975–1008. [Google Scholar] [CrossRef] - Jolliffe, I.T. Principal Component Analysis, 2nd ed.; Springer: New York, NY, USA, 2002. [Google Scholar]
- R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2017; Available online: http://www.R-project.org/ (accessed on 15 November 2021).
- Akaike, H. A new look at the statistical model identification. IEEE Trans. Autom. Control.
**1974**, 19, 716–723. [Google Scholar] [CrossRef]

**Figure 3.**Sub-basins with ID number and drainage channels with stream order, excluding first order, derived from the digital elevation model.

**Figure 4.**Predicted and observed flash flood probability against logit(p): dots are observed flash flood probabilities, the solid black line represents the probability predicted by the logistic model, the red dotted line corresponds to the mean of the model predictions, logit(p) = −4.74, the blue dotted line to the mean of the observations, logit(p) = −2.44, and the black dotted line with logit (p = 0.5) = 0.

**Figure 5.**Susceptibility to flash flooding in the Sinai predicted with the probabilistic model (Equation (2)) using the principal components of the hydro-morphometric parameters as predictors.

Parameter | Description | Determination | Reference |
---|---|---|---|

Basin geometry | |||

A [L^{2}] | Area | Spatial analysis | - |

P [L] | Perimeter | Spatial analysis | - |

L_{b} [L] | Basin length | Spatial analysis | [11] |

F_{f} [-] | Form factor | ${F}_{f}=A/{L}_{b}^{2}$ | [11] |

C_{c} [-] | Compactness coefficient | ${C}_{c}=P/2\sqrt{\pi A}$ | [11,12] |

R_{e} [-] | Elongation ratio | ${R}_{e}=2\sqrt{A/\pi}/{L}_{b}$ | [16] |

Drainage network | |||

S_{u} [-] | Stream order | Spatial analysis | [12] |

N_{u} [-] | Stream number | ${N}_{u}={\displaystyle \sum}_{1}^{n}{N}_{i}$ | [15] |

R_{b} [-] | Bifurcation ratio | ${R}_{b}=({\displaystyle \sum}_{1}^{n}{N}_{i}/{N}_{i+1})/\left(n-1\right)$ | [14] |

L_{u} [L] | Stream length | ${L}_{u}={\displaystyle \sum}_{1}^{n}{L}_{i}$ | [14] |

D_{d} [L^{−1}] | Drainage density | ${D}_{d}={L}_{u}/A$ | [11] |

F_{s} [L^{−2}] | Stream frequency | ${F}_{s}={N}_{u}/A$ | [11,12] |

L_{o} [L] | Length of overland flow | ${L}_{o}=1/2{D}_{d}$ | [11] |

R_{t} [L^{−1}] | Texture ratio | ${R}_{t}={N}_{1}/P$ | [13] |

Relief | |||

R_{f} [L] | Basin relief | Spatial analysis | [16] |

R_{r} [-] | Relief ratio | ${R}_{r}={R}_{f}/{L}_{b}$ | [16] |

R_{n} [-] | Ruggedness number | ${R}_{n}={R}_{f}{D}_{d}$ | [38] |

S [°] | Mean slope | Spatial analysis | - |

_{i}is the number of stream segments of order i, and L

_{i}is the length of stream segments of order i.

**Table 2.**Basins where flash floods have been reported in the literature, used for calibration of the logistic model.

Wadi | Basin No. |
---|---|

Sedri | 3 |

Werdan | 9 |

Ras Sudr | 12 |

Watir | 54 |

Dahab | 56 |

Feiran | 58 |

El-Aawag | 84 |

Gharandal | 89 |

Kid | 98 |

**Table 3.**Range of the hydro-morphometric parameter, showing minimum, maximum, mean and standard deviation (SD).

Parameter | Minimum | Maximum | Mean | SD |
---|---|---|---|---|

A (km^{2}) | 19 | 2254 | 492 | 542 |

P (km) | 31 | 386 | 139 | 82 |

L_{b} (km) | 10.9 | 94.5 | 38.0 | 20.2 |

F_{f} | 0.07 | 0.52 | 0.26 | 0.10 |

C_{c} | 1.50 | 2.88 | 2.07 | 0.34 |

R_{e} | 0.30 | 0.81 | 0.56 | 0.11 |

S_{u} | 2 | 6 | 3.7 | 1.2 |

N_{u} | 3 | 194 | 42.1 | 45.1 |

R_{b} | 1.75 | 6 | 3.4 | 0.9 |

L_{u} (km) | 11 | 1122 | 229 | 250 |

D_{d} (km^{−1}) | 0.31 | 0.86 | 0.49 | 0.10 |

F_{s} (km^{−1}) | 0.04 | 0.16 | 0.09 | 0.02 |

L_{o} (km) | 0.58 | 1.60 | 1.06 | 0.19 |

R_{t} (km^{−1}) | 0.02 | 0.65 | 0.18 | 0.12 |

R_{f} (m) | 65 | 2595 | 806 | 585 |

R_{r} | 0.004 | 0.082 | 0.025 | 0.020 |

R_{n} | 0.03 | 1.31 | 0.39 | 0.29 |

S (°) | 1.50 | 21.40 | 6.20 | 4.69 |

**Table 4.**Eigenvalue, variance (%) accounted for and cumulative variance (%) from a principal component analysis (significant values are indicated in bold).

PC Number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
---|---|---|---|---|---|---|---|---|

Eigenvalue | 7.13 | 3.36 | 3.04 | 1.76 | 0.96 | 0.66 | 0.39 | 0.30 |

Variance (%) | 0.40 | 0.19 | 0.17 | 0.10 | 0.05 | 0.04 | 0.02 | 0.02 |

Cumulative (%) | 0.40 | 0.58 | 0.75 | 0.85 | 0.90 | 0.94 | 0.96 | 0.98 |

**Table 5.**Correlation coefficients between the first five principal components and the hydro-morphometric parameters after Varimax rotation (significant values are indicated in bold).

Parameter | Principal Component | ||||
---|---|---|---|---|---|

PC_{1} | PC_{2} | PC_{3} | PC_{4} | PC_{5} | |

A | 0.94 | 0.04 | 0.21 | −0.05 | 0.15 |

P | 0.95 | −0.01 | −0.12 | −0.16 | 0.11 |

L_{b} | 0.95 | 0.06 | −0.18 | −0.11 | 0.12 |

F_{f} | 0.29 | −0.04 | 0.87 | −0.26 | 0.00 |

C_{c} | 0.04 | −0.16 | −0.88 | 0.02 | 0.07 |

R_{e} | 0.29 | −0.07 | 0.86 | −0.30 | −0.03 |

S_{u} | 0.80 | −0.11 | 0.13 | −0.12 | −0.31 |

N_{u} | 0.93 | 0.05 | 0.26 | −0.01 | 0.15 |

R_{b} | 0.22 | 0.08 | −0.04 | −0.02 | 0.93 |

L_{u} | 0.94 | 0.01 | 0.21 | 0.03 | 0.15 |

D_{d} | −0.11 | 0.03 | −0.23 | 0.94 | −0.01 |

F_{s} | −0.39 | −0.03 | 0.28 | 0.41 | −0.33 |

L_{o} | 0.03 | 0.07 | 0.24 | −0.94 | −0.01 |

R_{t} | 0.83 | 0.08 | 0.47 | 0.00 | 0.17 |

R_{f} | 0.26 | 0.94 | −0.02 | −0.05 | 0.10 |

R_{r} | −0.37 | 0.87 | 0.13 | 0.05 | −0.03 |

R_{n} | 0.18 | 0.91 | −0.10 | 0.27 | 0.12 |

S | −0.06 | 0.88 | 0.11 | −0.35 | −0.05 |

**Table 6.**Regression results of the logistic model: estimated model coefficients, standard error, z-value and probability, deviance (Dev.), Akaike information criterion (AIC) and rank.

Predictor | Estimate | Std. Err. | z-Value | Pr (>|z|) | Dev. | AIC | Rank |
---|---|---|---|---|---|---|---|

Intercept | −4.74 | 1.09 | −4.36 | 1.33 × 10^{−5} | 62.6 | 64.6 | |

PC_{1} | 1.56 | 0.58 | 2.68 | 0.01 | 43.1 | 53.1 | 1 |

PC_{2} | 0.94 | 0.54 | 1.75 | 0.08 | 34.8 | 44.8 | 3 |

PC_{3} | 0.71 | 0.50 | 1.43 | 0.15 | 33.8 | 43.8 | 5 |

PC_{4} | −1.99 | 0.90 | −2.22 | 0.03 | 40.6 | 50.6 | 2 |

PC_{5} | 0.90 | 0.60 | 1.51 | 0.13 | 33.9 | 43.9 | 4 |

Total | 31.5 | 43.5 |

**Table 7.**Probability for flooding estimated with the logistic model and after cross-validation of the model.

Basin | Probability | |
---|---|---|

No. | Model | Cross Val. |

3 | 0.422 | 0.248 |

9 | 0.030 | 0.014 |

12 | 0.019 | 0.005 |

54 | 0.759 | 0.673 |

56 | 0.761 | 0.695 |

58 | 0.835 | 0.786 |

84 | 0.866 | 0.729 |

89 | 0.238 | 0.196 |

98 | 0.872 | 0.848 |

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**MDPI and ACS Style**

El-Rawy, M.; Elsadek, W.M.; De Smedt, F.
Flash Flood Susceptibility Mapping in Sinai, Egypt Using Hydromorphic Data, Principal Component Analysis and Logistic Regression. *Water* **2022**, *14*, 2434.
https://doi.org/10.3390/w14152434

**AMA Style**

El-Rawy M, Elsadek WM, De Smedt F.
Flash Flood Susceptibility Mapping in Sinai, Egypt Using Hydromorphic Data, Principal Component Analysis and Logistic Regression. *Water*. 2022; 14(15):2434.
https://doi.org/10.3390/w14152434

**Chicago/Turabian Style**

El-Rawy, Mustafa, Wael M. Elsadek, and Florimond De Smedt.
2022. "Flash Flood Susceptibility Mapping in Sinai, Egypt Using Hydromorphic Data, Principal Component Analysis and Logistic Regression" *Water* 14, no. 15: 2434.
https://doi.org/10.3390/w14152434