# GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### Description of the Research Area

^{2}, between 34°31′ N and 35°5′ N, 49°30′ E to 50°9′ E (Figure 1). Topography of the Tafrash watershed area is hilly with elevation ranging from 1296 to 3101 m. This area experiences cold winters and relatively moderate summers. The average temperature is 19.2 °C in summer and 6.4 °C in winter. Average annual rainfall in this region is 254.3 mm. Major water supply sources in the Tafresh watershed include springs, the perennial GharehChay River, the Ab Kamar seasonal river, and semi-deep wells. The GharehChay river with discharge 3000 ls

^{−1}is one of the most important rivers in the area, which provides water for irrigation in Tafresh area, but due to droughts in recent years, discharge has reduced below 2000 ls

^{−1}. However, several severe flash floods occur in the Tafrash watershed during winter every year, due to sudden heavy rainfall within a short period.

## 3. Data Collection and Preparation

#### 3.1. Flash Flood Inventory

#### 3.2. Flash Flood Conditioning Factors

## 4. Methods Used

#### 4.1. Frequency Ratio

#### 4.2. Correlation Based Feature Selection

#### 4.3. AdaBoostM1

#### 4.4. Bagging

_{i}(x) denotes an indicator function.

#### 4.5. Dagging

#### 4.6. MultiBoostAB

#### 4.7. Credal Decision Tree

_{zj}express the sample size and the event frequency (Z = z

_{j}), respectively; and r is called the hyperparameter, which has values of 1 or 2, as stated by Walley [80].

#### 4.8. Validation of the Models

#### 4.8.1. Receiver Operating Characteristic (ROC) Curve

#### 4.8.2. Statistical Measures

_{a}and P

_{est}are the measured and expected agreements, respectively.

_{model}and X

_{act}denote the model simulated and actual (i.e., measured) value, respectively; L stands for the summation of samples.

## 5. Methodology

#### 5.1. Data Collection and Preparation

#### 5.2. Generating Training and Testing Datasets

#### 5.3. Building the Flash Flood Models

#### 5.4. Validation of the Models

#### 5.5. Generation of Flash Flood Susceptibility Maps

## 6. Results and Discussion

#### 6.1. Impact Weight of each Class of Variables Affecting Flash Flood Susceptibility by FR Method

#### 6.2. Importance of Factors Using Correlation-Based Feature Selection

#### 6.3. Validation of Different Models

#### 6.4. Development of Flash Flood Susceptibility Maps

## 7. Concluding Remarks

## Author Contributions

## Funding

## Conflicts of Interest

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**Figure 3.**Maps of flash flood conditioning factors: (

**a**) distance to rivers, (

**b**) aspect, (

**c**) elevation, (

**d**) slope, (

**e**) rainfall, (

**f**) distance from faults, (

**g**) land use, (

**h**) soil, and (

**i**) lithology.

**Figure 6.**Analysis of Receiver Operating Characteristic (ROC) of the models: (

**a**) training dataset and (

**b**) validating dataset.

**Figure 8.**Analysis of accuracy of the models using: (

**a**) training dataset and (

**b**) validating dataset.

**Figure 10.**Flash flood susceptibility maps of the models: (

**a**) ABM-CDT, (

**b**) Bag-CDT, (

**c**) Dag-CDT, (

**d**) MBAB-CDT, and (

**e**) CDT.

Row | Primary Input Data | Original Format Sources | Spatial Resolution | Source of Data | Derived Map |
---|---|---|---|---|---|

1 | ALOS-PALSER DEM | Raster | 12.5 m | https://search.asf.alaska.edu/ | Slope, Aspect, Curvature, Elevation, Distance from river |

2 | Landsat 8 OLI | Raster | 30 m | Department of Natural Resources of Markazi Province | Land use map |

3 | Meteorological data | Point | - | Markazi County Meteorological Bureau | Rainfall map |

4 | Geological map | Vector | 1:100000 | Geological survey and Mineral Exploration of Iran | Lithology and Distance from fault |

5 | Soil map | Vector | 1:100000 | Department of Natural Resources of Markazi Province | Soil map |

Group No | Geo-Units | Description | Permeability |
---|---|---|---|

1 | Ea.bvt | Andesitic to basaltic volcanic tuff | Low |

2 | OMc | Basal conglomerate and sandstone | Moderate |

3 | Ed.avs | Dacitic to andesitic volcanosediment | Moderate |

4 | TRJs | Dark grey shale and sandstone (SHEMSHAK FM.) | Moderate |

5 | EKgy | Gypsum | High |

6 | K2I1 | Hyporite bearing limestone (Senonian) | Moderate |

7 | OMq | Limestone, marl, gypsiferous marl. Sandymarl and sandstone (QOM FM) | Low |

8 | Qft2 | Low level piedment fan and valley terrace deposit | High |

9 | Plc | Polymictic conglomerate and sandstone | Moderate |

10 | Mur | Red marl, gypsiferous marl, sandstone and conglomerate (upper red Fm.) | High |

11 | TRn | Sandstone, quartze arenite, shale and fossiliferous limestone (NAIBAND for) | Moderate |

12 | K2shm | Sale calcareous shale and sandstone with intercalations of limestone | Moderate |

13 | Ktzl | Thick bedded to massive, white to pinkish orbitolina bearing limestone (TIZKUh FM) | Moderate |

14 | Judi | Upper Jurassic diorite | Low |

15 | EK | Well bedded green tuff and tuffaceousshle (KARAJ FM) | Moderate |

Statistical Measures | Formula |
---|---|

PPV (%) | $PPV=\frac{A}{A+B}$ |

NPV (%) | $NPV=\frac{C}{C+D}$ |

ACC (%) | $ACC=\frac{A+C}{A+C+B+D}$ |

SST (%) | SST = $\frac{A}{A+D}$ |

SPF (%) | SPF = $\frac{C}{C+B}$ |

k | $k=\frac{{P}_{a}-{P}_{est}}{1-{P}_{est}}$ P _{a} = (A + C)P _{est} = (A + D) × (A + D) + (B + C) × (D + C) |

Ranked | Class | Average Merit (AM) |
---|---|---|

1 | Distance from rivers | 0.608 |

2 | Slope | 0.484 |

3 | Elevation | 0.337 |

4 | Lithology | 0.125 |

5 | Soil | 0.099 |

6 | Rainfall | 0.049 |

7 | Land use | 0.024 |

8 | Aspect | 0.022 |

9 | Distance from faults | 0.007 |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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**MDPI and ACS Style**

Pham, B.T.; Avand, M.; Janizadeh, S.; Phong, T.V.; Al-Ansari, N.; Ho, L.S.; Das, S.; Le, H.V.; Amini, A.; Bozchaloei, S.K.;
et al. GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment. *Water* **2020**, *12*, 683.
https://doi.org/10.3390/w12030683

**AMA Style**

Pham BT, Avand M, Janizadeh S, Phong TV, Al-Ansari N, Ho LS, Das S, Le HV, Amini A, Bozchaloei SK,
et al. GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment. *Water*. 2020; 12(3):683.
https://doi.org/10.3390/w12030683

**Chicago/Turabian Style**

Pham, Binh Thai, Mohammadtaghi Avand, Saeid Janizadeh, Tran Van Phong, Nadhir Al-Ansari, Lanh Si Ho, Sumit Das, Hiep Van Le, Ata Amini, Saeid Khosrobeigi Bozchaloei,
and et al. 2020. "GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment" *Water* 12, no. 3: 683.
https://doi.org/10.3390/w12030683