# Flood Susceptibility Mapping Using Remote Sensing and Integration of Decision Table Classifier and Metaheuristic Algorithms

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## Abstract

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## 1. Introduction

## 2. Methodology

## 3. Material and Methods

#### 3.1. Materials

#### 3.1.1. Study Area

#### 3.1.2. Flood Detection and Inventory

#### 3.1.3. Conditioning Factors

- Altitude

- Slope

- Slope aspect

- Topographic wetness index (TWI)

- Stream power index (SPI)

- Plan curvature

- Rainfall

- Normalized difference vegetation index (NDVI)

- Distance from the river

- Land cover

- Geology

#### 3.2. Methods

#### 3.2.1. Certainty Factor (CF) Method

#### 3.2.2. Pairwise Consistency Method

#### 3.2.3. Decision Table (DTB) Classifier

#### 3.2.4. Genetic Algorithm (GA)

#### 3.2.5. Particle Swarm Optimization (PSO) Algorithm

_{1}and C

_{2}are the velocity constants, rand () is a random function ([0, 1]), ${\mathrm{x}}_{\mathrm{t}}$ is the particle’s current location, pbest is the ideal site for a particle, and gbest is the best position found by the entire particle.

#### 3.2.6. Harmony Search (HS) Algorithm

#### 3.2.7. Hybrid Algorithms

#### 3.2.8. Validation Methods

## 4. Results

#### 4.1. Results of Pairwise Consistency

#### 4.2. Results of CF Method

#### 4.3. Results of Hybrid Modeling

#### 4.4. Flood Susceptibility Mapping (FSM) and Validation

## 5. Discussion

#### 5.1. Examining the Role of Factors in Flood Prediction

#### 5.2. Evaluation and Comparison of Algorithms

#### 5.3. Strengths of the Research

## 6. Conclusions and Remarks

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 4.**Influential flood factors: (

**a**) altitude, (

**b**) slope, (

**c**) slope aspect, (

**d**) TWI, (

**e**) SPI, (

**f**) plan curvature, (

**g**) rainfall, (

**h**) NDVI, (

**i**) distance to river, (

**j**) land cover, and (

**k**) geology.

Sensors | Sensor Mode | Polarization | Path | Dates |
---|---|---|---|---|

Sentinel-1A | Interferometry wide swath (IW) | VV, VH | Ascending | 27 December 2018 27 January 2019 |

Class | No. of Pixels in Domain | No. of Floods | CF | Class | No. of Pixels in Domain | No. of Floods | CF |
---|---|---|---|---|---|---|---|

Altitude (m) | Distance to river (m) | ||||||

0–645 | 6,462,394 | 37 | −0.002 | 0–100 | 1,089,056 | 9 | 0.305 |

645–966 | 6,334,216 | 49 | 0.25 | 100–200 | 927,573 | 8 | 0.334 |

966–1315 | 3,744,146 | 11 | −0.48 | 200–300 | 982,420 | 6 | 0.06 |

1315–1757 | 224,136 | 11 | −0.14 | 300–400 | 821,497 | 5 | 0.057 |

>1757 | 733,452 | 4 | −0.049 | >400 | 1,569,502 | 84 | −0.06 |

Slope | SPI | ||||||

0–6 | 8,391,442 | 64 | 0.24 | 0–100 | 3,408,492 | 30 | 0.34 |

6–14 | 4,852,114 | 20 | −0.281 | 100–150 | 1,163,948 | 8 | 0.16 |

14–23 | 3,413,191 | 14 | −0.285 | 150–200 | 940,593 | 10 | 0.46 |

23–35 | 214,618 | 12 | −0.026 | 200–400 | 2,584,906 | 14 | −0.05 |

>35 | 712,641 | 2 | −0.51 | >400 | 1,141,762 | 50 | −0.23 |

TWI | NDVI | ||||||

−0.17–3.66 | 4,749,037 | 25 | −0.082 | −0.62–−0.08 | 237,367 | 0 | −1 |

3.66–4.63 | 5,674,969 | 23 | −0.293 | −0.08–0.13 | 5,926,676 | 45 | 0.24 |

4.63–5.65 | 4,988,295 | 26 | −0.091 | 0.13–0.22 | 6,876,000 | 43 | 0.08 |

5.65–7.9 | 385,582 | 36 | 0.38 | 0.22–0.34 | 498,872 | 17 | −0.4 |

>7.9 | 247,441 | 2 | 0.28 | >0.34 | 148,036 | 7 | −0.17 |

Land cover | Rainfall (mm) | ||||||

Water body | 347,922 | 4 | 0.5 | 247–296 | 1,203,193 | 1 | −0.85 |

Forest | 94,861 | 0 | −1 | 296–322 | 3,444,360 | 13 | −0.34 |

Agriculture | 4,167,971 | 33 | 0.27 | 322–344 | 4,516,856 | 41 | 0.36 |

Pasture | 1,3496,784 | 67 | −0.13 | 344–366 | 874,886 | 48 | −0.044 |

Residential areas | 731,632 | 3 | −0.28 | >366 | 160,229 | 9 | −0.021 |

Bare land | 671,524 | 5 | 0.22 | ||||

Slope aspect | Geology | ||||||

F | 174,452 | 0 | −1 | Tertiary | 1,0294,396 | 47 | −0.2 |

N | 2,101,154 | 16 | 0.24 | Cretaceous | 5,260,569 | 20 | −0.33 |

NE | 2,342,310 | 10 | −0.25 | Mesozoic | 1,292,709 | 14 | 0.47 |

E | 2,369,979 | 11 | −0.19 | Quaternary | 1,890,736 | 30 | 0.63 |

SE | 2,086,778 | 11 | −0.081 | Jurassic | 245,395 | 0 | −1 |

S | 2,712,349 | 19 | 0.18 | Triassic | 39,804 | 0 | −1 |

SW | 3,103,277 | 17 | −0.045 | Cenozoic | 266,473 | 1 | −0.34 |

W | 274,472 | 17 | 0.073 | Cretaceous volcanoes | 225,608 | 0 | −1 |

NW | 188,054 | 11 | 0.018 | ||||

Plan curvature | |||||||

<−0.001 | 5,462,084 | 25 | −0.2 | ||||

−0.001–0.001 | 8,406,182 | 47 | −0.02 | ||||

>0.001 | 5,647,302 | 40 | 0.19 |

Algorithm | Parameters |
---|---|

GA | Iteration = 30 Population size = 100 Crossover rate = 0.7 Mutation rate = 0.5 |

PSO | Iteration = 30 Population size = 100 Inertia weight = 1 Personal learning coefficient = 1 Global learning coefficient = 2 |

HS | Iteration = 30 Harmony memory size = 20 Number of new harmonies = 20 Harmony memory consideration rate = 0.5 Pitch adjustment rate = 0.1 |

Algorithm | Number of Features | Features | Objective Function Value |
---|---|---|---|

GA | 7 | Slope, rainfall, geology, distance to river, ndvi, altitude, land cover | 0.138 |

PSO | 6 | SPI, NDVI, rainfall, geology, land cover, plan curvature | 0.158 |

HS | 4 | Geology, land cover, rainfall, altitude | 0.183 |

Algorithm | Training | Validation |
---|---|---|

DTB-GA | 0.2029 | 0.4524 |

DTB-PSO | 0.2507 | 0.4571 |

DTB-HS | 0.3232 | 0.459 |

Models | AUC | SE | 95% CI |
---|---|---|---|

DTB-GA | 0.889 | 0.0322 | 0.807 to 0.944 |

DTB-PSO | 0.844 | 0.0399 | 0.755 to 0.911 |

DTB-HS | 0.812 | 0.0456 | 0.718 to 0.885 |

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## Share and Cite

**MDPI and ACS Style**

Askar, S.; Zeraat Peyma, S.; Yousef, M.M.; Prodanova, N.A.; Muda, I.; Elsahabi, M.; Hatamiafkoueieh, J.
Flood Susceptibility Mapping Using Remote Sensing and Integration of Decision Table Classifier and Metaheuristic Algorithms. *Water* **2022**, *14*, 3062.
https://doi.org/10.3390/w14193062

**AMA Style**

Askar S, Zeraat Peyma S, Yousef MM, Prodanova NA, Muda I, Elsahabi M, Hatamiafkoueieh J.
Flood Susceptibility Mapping Using Remote Sensing and Integration of Decision Table Classifier and Metaheuristic Algorithms. *Water*. 2022; 14(19):3062.
https://doi.org/10.3390/w14193062

**Chicago/Turabian Style**

Askar, Shavan, Sajjad Zeraat Peyma, Mohanad Mohsen Yousef, Natalia Alekseevna Prodanova, Iskandar Muda, Mohamed Elsahabi, and Javad Hatamiafkoueieh.
2022. "Flood Susceptibility Mapping Using Remote Sensing and Integration of Decision Table Classifier and Metaheuristic Algorithms" *Water* 14, no. 19: 3062.
https://doi.org/10.3390/w14193062