Deep Neural Network Utilizing Remote Sensing Datasets for Flood Hazard Susceptibility Mapping in Brisbane, Australia
"> Figure 1
<p>General location of the study area: (<b>a</b>) Australia map; (<b>b</b>) location map of study area; (<b>c</b>) the study area including flood and non-flood inventory points.</p> "> Figure 2
<p>Thirteen explanatory factors for model development in this study: (<b>a</b>) altitude, (<b>b</b>) slope, (<b>c</b>) aspect, (<b>d</b>) curvature, (<b>e</b>) distance to river, (<b>f</b>) distance to road, (<b>g</b>) SPI, (<b>h</b>) TWI, (<b>i</b>) STI, (<b>j</b>) total annual rainfall, (<b>k</b>) soil, (<b>l</b>) lithology, and (<b>m</b>) land use.</p> "> Figure 2 Cont.
<p>Thirteen explanatory factors for model development in this study: (<b>a</b>) altitude, (<b>b</b>) slope, (<b>c</b>) aspect, (<b>d</b>) curvature, (<b>e</b>) distance to river, (<b>f</b>) distance to road, (<b>g</b>) SPI, (<b>h</b>) TWI, (<b>i</b>) STI, (<b>j</b>) total annual rainfall, (<b>k</b>) soil, (<b>l</b>) lithology, and (<b>m</b>) land use.</p> "> Figure 2 Cont.
<p>Thirteen explanatory factors for model development in this study: (<b>a</b>) altitude, (<b>b</b>) slope, (<b>c</b>) aspect, (<b>d</b>) curvature, (<b>e</b>) distance to river, (<b>f</b>) distance to road, (<b>g</b>) SPI, (<b>h</b>) TWI, (<b>i</b>) STI, (<b>j</b>) total annual rainfall, (<b>k</b>) soil, (<b>l</b>) lithology, and (<b>m</b>) land use.</p> "> Figure 3
<p>Procedures for flood susceptibility mapping in this study.</p> "> Figure 4
<p>Simplified representation of ANN.</p> "> Figure 5
<p>Simplified representation of DLNN.</p> "> Figure 6
<p>Flood susceptibility maps simulated using different models: (<b>a</b>) ANN, (<b>b</b>) DLNN, and (<b>c</b>) PSO-DLNN.</p> "> Figure 7
<p>Flood density graph of ANN, DLNN, and PSO-DLNN.</p> "> Figure 8
<p>Evaluation of the flood susceptibility maps based on the AUC test (<b>a</b>) ANN, (<b>b</b>) DLNN, and (<b>c</b>) PSO-DLNNO.</p> "> Figure 9
<p>Agreement and disagreement flood susceptibility for the "very high" class simulated by ANN, DLNN, PSO-DLNN.</p> "> Figure 10
<p>Comparison of flooded area predicted by PSO_DLNN method and hazard map.</p> "> Figure 11
<p>Comparison of flooded by hazard map and by flood susceptibility (PSO-DLNN) ranked by each class based on (<b>a</b>) number of pixels and (<b>b</b>) area (m<sup>2</sup>).</p> "> Figure 11 Cont.
<p>Comparison of flooded by hazard map and by flood susceptibility (PSO-DLNN) ranked by each class based on (<b>a</b>) number of pixels and (<b>b</b>) area (m<sup>2</sup>).</p> "> Figure 12
<p>Comparison of flooded region by hazard map and simulated spatial assessment of flood susceptibility ranked by each class using PSO-DLNN.</p> ">
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
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
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
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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 | Km2 | 440.2872 | 144.0198 | 2.1537 | 2.1726 | 193.0005 |
% | 56.33 | 18.43 | 0.28 | 0.28 | 24.69 | |
DLNN | Km2 | 528.4881 | 48.6306 | 24.6753 | 29.5146 | 150.3252 |
% | 67.61 | 6.22 | 3.16 | 3.78 | 19.23 | |
PSO-DLNN | Km2 | 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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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
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 StyleKalantar, 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