# Deep Neural Network Utilizing Remote Sensing Datasets for Flood Hazard Susceptibility Mapping in Brisbane, Australia

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## Abstract

**:**

## 1. Introduction

#### Related Studies

## 2. Study Area and Materials

#### 2.1. Study Area

#### 2.2. Data Description

#### 2.2.1. Flood Inventories

#### 2.2.2. Explanatory Factors

#### The Topographic Factors

^{2}/m) and $\beta $ measures the topographic gradient or local slope gradient in degree [35]. The higher value indicates higher accumulation and runoff flow and thus more sensitivity to the flood occurrence [3,7].

#### The Water-Related Factors

#### Geological Factors

#### Land Use

## 3. Methodology

#### 3.1. Overview

#### 3.2. Multicollinearity Analysis

#### 3.3. Modeling with ML Methods

#### 3.3.1. Artificial Neural Networks (ANN)

#### 3.3.2. Deep Learning Neural Networks (DLNN)

#### 3.3.3. Optimized DLNN via PSO

#### 3.4. Evaluation Methods

## 4. Results

## 5. Discussion

^{2}) were mainly within “high” and “very high” classes, showing a promising agreement between the susceptibility map and hazard map. The flooded region and simulated spatial assessment of flood susceptibility using PSO-DLNN are presented in Figure 12.

## 6. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**General location of the study area: (

**a**) Australia map; (

**b**) location map of study area; (

**c**) the study area including flood and non-flood inventory points.

**Figure 2.**Thirteen explanatory factors for model development in this study: (

**a**) altitude, (

**b**) slope, (

**c**) aspect, (

**d**) curvature, (

**e**) distance to river, (

**f**) distance to road, (

**g**) SPI, (

**h**) TWI, (

**i**) STI, (

**j**) total annual rainfall, (

**k**) soil, (

**l**) lithology, and (

**m**) land use.

**Figure 6.**Flood susceptibility maps simulated using different models: (

**a**) ANN, (

**b**) DLNN, and (

**c**) PSO-DLNN.

**Figure 8.**Evaluation of the flood susceptibility maps based on the AUC test (

**a**) ANN, (

**b**) DLNN, and (

**c**) PSO-DLNNO.

**Figure 9.**Agreement and disagreement flood susceptibility for the "very high" class simulated by ANN, DLNN, PSO-DLNN.

**Figure 11.**Comparison of flooded by hazard map and by flood susceptibility (PSO-DLNN) ranked by each class based on (

**a**) number of pixels and (

**b**) area (m

^{2}).

**Figure 12.**Comparison of flooded region by hazard map and simulated spatial assessment of flood susceptibility ranked by each class using PSO-DLNN.

Code | Label | Name |
---|---|---|

1 | Bellthorpe andesite, Brookfield volcanics, Gilla volcanics, unnamed volcanic units | |

2 | Bundamba Group (i.e., Marburg Subgroup and Woogaroo Subgroup) and Landsborough sandstone | |

3 | Ipswich coal measures | |

4 | Middle to Late Triassic volcanic units, southeast Queensland | |

5 | Neranleigh-Fernvale beds, Bunya phyllite | |

6 | Paleocene–oligocene sediments | |

7 | Quaternary alluvium and lacustrine deposits | |

8 | Td-QLD | |

9 | Triassic intrusives in south-eastern and central Queensland |

Class | Description |
---|---|

Cd3 | Sands (Uc2.12) and siliceous sands (Uc1.21 and Uc1.22) on sandstones, grey cracking clays (Ug5.23) on shales, and shallow red clays (Uf6.12) on basalt |

Fu2 | Shallow and stony leached loams (Um2.12) and also (Um5.2) loams. |

Fu3 | Shallow and stony leached loams (Um2.1), and also (Um5.2) loams. |

Kb28 | Moderate and shallow forms of dark cracking clays on the slopes. |

MM9 | Brown and grey cracking clays (Ug5.34), (Ug5.39), and (Ug5.2), which occur on the third terrace with (Gn3.21), (Dy3.41), and (Dy3.13) soils. |

Mw30 | Red earths (Gn2.14) with associated areas of red friable earths (Gn3.11). |

Pl1 | Hard acidic red and yellow soils (Dr3.41), (Dr2.41), and (Dy3.41) with some areas of (Dy3.43) and (Dr3.43) soils. |

Sj12 | Hard acidic yellow and yellow mottled soils (Dy2.41) and (Dy3.41) with (Dd1.41) on the flat areas, together with leached sands (Uc2.33 and Uc2.32) on low broad sandy banks. |

Tb64 | Hard acidic yellow (Dy3.41) and red (Dr3.41) mottled soils. |

Tb65 | Hard acidic and neutral yellow and red soils (Dy3.41), (Dy3.42), (Dr3.41), and (Dr2.12) on sandstones. |

No | Parameter | Model | ||
---|---|---|---|---|

ANN | DLNN | PSO-DLNN | ||

1 | Input nodes | 13 | 13 | 13 |

2 | Output nodes | 2 | 2 | 2 |

3 | Activation | - | ‘relu’ | ‘relu’ |

4 | Function | - | ‘Sigmoid’ | ‘Sigmoid’ |

5 | reluLeak | - | 0.01 | 0.01 |

6 | Eta | - | 0.8 | 0.8 |

7 | Hidden layer unit | 1 | 3 | 3 |

8 | Iteration | 500 | 500 | |

10 | Phi | - | - | 4.1 |

11 | phi1 | - | - | 2.05 |

12 | Phi2 | - | - | 2.05 |

13 | W | - | - | 0.73 |

14 | C1 | - | - | 1.49 |

15 | C2 | - | - | 1.49 |

Variables | VIF | Tolerance |
---|---|---|

Altitude | 4.52 | 0.22 |

Slope | 4.1 | 0.24 |

Aspect | 1.03 | 0.97 |

Curvature | 1.31 | 0.76 |

Distance from river | 2.39 | 0.42 |

Distance from road | 2.13 | 0.47 |

Rainfall | 2.07 | 0.48 |

Land use | 1.59 | 0.63 |

Lithology | 1.38 | 0.72 |

Soil | 1.99 | 0.50 |

SPI | 1.15 | 0.87 |

TWI | 1.69 | 0.59 |

STI | 4.04 | 0.25 |

Models | Area | Susceptibility Class | ||||
---|---|---|---|---|---|---|

Very low | Low | Moderate | High | Very high | ||

ANN | Km^{2} | 440.2872 | 144.0198 | 2.1537 | 2.1726 | 193.0005 |

% | 56.33 | 18.43 | 0.28 | 0.28 | 24.69 | |

DLNN | Km^{2} | 528.4881 | 48.6306 | 24.6753 | 29.5146 | 150.3252 |

% | 67.61 | 6.22 | 3.16 | 3.78 | 19.23 | |

PSO-DLNN | Km^{2} | 484.5816 | 74.4777 | 61.4268 | 73.0179 | 88.1298 |

% | 61.99 | 9.53 | 7.86 | 9.34 | 11.28 |

Models | Stage | Evaluation Tests | |||
---|---|---|---|---|---|

Sensitivity | Specificity | TSS | AUC | ||

ANN | Train | 0.98 | 0.96 | 0.94 | 0.98 |

Validation | 0.94 | 0.85 | 0.79 | 0.93 | |

DLNN | Train | 0.99 | 0.87 | 0.86 | 0.98 |

Validation | 0.86 | 0.85 | 0.71 | 0.96 | |

PSO-DLNN | Train | 0.99 | 0.89 | 0.88 | 0.99 |

Validation | 0.92 | 0.98 | 0.90 | 0.98 |

Variables | Importance |
---|---|

Altitude | 100 |

Slope | 33.05 |

Aspect | 1.32 |

Curvature | 16.55 |

Distance from river | 55.44 |

Distance from road | 29.21 |

Rainfall | 9.31 |

Land use | 22.63 |

Lithology | 11.29 |

Soil | 1.74 |

SPI | 0 |

TWI | 18.77 |

STI | 39.69 |

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## Share and Cite

**MDPI and ACS Style**

Kalantar, B.; Ueda, N.; Saeidi, V.; Janizadeh, S.; Shabani, F.; Ahmadi, K.; Shabani, F.
Deep Neural Network Utilizing Remote Sensing Datasets for Flood Hazard Susceptibility Mapping in Brisbane, Australia. *Remote Sens.* **2021**, *13*, 2638.
https://doi.org/10.3390/rs13132638

**AMA Style**

Kalantar B, Ueda N, Saeidi V, Janizadeh S, Shabani F, Ahmadi K, Shabani F.
Deep Neural Network Utilizing Remote Sensing Datasets for Flood Hazard Susceptibility Mapping in Brisbane, Australia. *Remote Sensing*. 2021; 13(13):2638.
https://doi.org/10.3390/rs13132638

**Chicago/Turabian Style**

Kalantar, Bahareh, Naonori Ueda, Vahideh Saeidi, Saeid Janizadeh, Fariborz Shabani, Kourosh Ahmadi, and Farzin Shabani.
2021. "Deep Neural Network Utilizing Remote Sensing Datasets for Flood Hazard Susceptibility Mapping in Brisbane, Australia" *Remote Sensing* 13, no. 13: 2638.
https://doi.org/10.3390/rs13132638