Google Earth Engine and Machine Learning for Flash Flood Exposure Mapping—Case Study: Tetouan, Morocco
<p>Study area: (<b>1</b>) North Africa, Morocco; (<b>2</b>) Tanger<tt>–</tt>Tetouan<tt>–</tt>Al Hoceima region; (<b>3</b>) Tetouan Province; (<b>4</b>) Tetouan city.</p> "> Figure 2
<p>The database used: (<b>A</b>) an overview of the study area’s satellite imagery, (<b>B</b>) the elevation distribution based on the digital elevation model (DEM) of the study area, (<b>C</b>) the population density repartition in the study area, (<b>D</b>) the flooded (red points) and non-flooded (green points) locations in the study area.</p> "> Figure 3
<p>The proposed methodology flowchart: (<b>1</b>) Mapping LULC; (<b>2</b>) FF susceptibility mapping and forecasting; (<b>3</b>) FF extent mapping; (<b>4</b>) FF exposure.</p> "> Figure 4
<p>Flash flood conditioning factors in the study area: (<b>A</b>) Geographical distribution of flooded and non-flooded locations from the field survey, (<b>B</b>) LU/LC, (<b>C</b>) SPI, (<b>D</b>) Plan curvature.</p> "> Figure 5
<p>Flash flood conditioning factors in the study area: (<b>A</b>) Profile curvature, (<b>B</b>) TPI, (<b>C</b>) TWI, (<b>D</b>) Elevation, (<b>E</b>) Slope, and (<b>F</b>) Aspect.</p> "> Figure 6
<p>Synthetic Aperture Radar (SAR) images showing the study area before (<b>left</b>) and during (<b>right</b>) the flash flood.</p> "> Figure 7
<p>Land use and land cover classification of the study area (<b>1</b>–<b>15</b>) for each composite image ((<b>A</b>) Max; (<b>B</b>) Mean; (<b>C</b>) Median; (<b>D</b>) Min; (<b>E</b>) Mode), generated using machine learning algorithms ((<b>I</b>) SVM, (<b>II</b>) RF, (<b>III</b>) CART).</p> "> Figure 8
<p>The influence of the choice of dataset on classification performance: Evaluation of the impact of different datasets (max, mean, median, min, and mode) on LU/LC classification performance using overall accuracy and the Kappa index for each machine learning algorithm used (SVM, RF, and CART).</p> "> Figure 9
<p>Flash Flood susceptibility maps generated using machine learning algorithms: Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), and Multilayer Perceptron (MLP). Each map categorizes the study area into five levels of flood susceptibility: highest, high, moderate, low, and lowest.</p> "> Figure 10
<p>Comparison of the performance of the machine learning algorithms used (SVM, LR, RF, NB, and MLP) for flash flood susceptibility using precision, recall, F1 score, and Kappa.</p> "> Figure 11
<p>Performance comparison of the machine learning algorithms used (SVM, LR, RF, NB, and MLP) using Receiver operating characteristic (ROC) curves.</p> "> Figure 12
<p>Pairwise correlation matrix of variables used in the study: LU/LC, aspect, TWI, TPI, slope, profile curvature, plan curvature, X, Y, SPI, flood, and non-flood.</p> "> Figure 13
<p>Comparison of the relative importance of the different factors used for flash food susceptibility mapping, as identified by Random Forest (RF) (<b>A</b>) and Multilayer Perceptron (MLP) (<b>B</b>).</p> "> Figure 14
<p>Flash flooding extent of the study area using Synthetic Aperture Radar (SAR) data. Flash flood areas are shown in blue.</p> "> Figure 15
<p>Superposition of high flash flood susceptibility and SAR flash flood data. Areas of high flash flood susceptibility are shown in yellow. Areas detected as inundated using Synthetic Aperture Radar (SAR) data are shown in blue.</p> "> Figure 16
<p>Overview of the Google Earth Engine application showing exposure to flash floods in the study area. Affected forest areas are shown in dark green, affected vegetation in light green, affected urban areas in red, and affected barren areas in yellow.</p> "> Figure 17
<p>Distribution of the population potentially exposed to flash flooding in the study area based on the population density.</p> "> Figure 18
<p>Flash flood exposure in the study areas: Colors represent affected areas: dark green (forest), light green (vegetation), red (urban), and yellow (barren land).</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.2.1. Satellite Imagery
2.2.2. Digital Elevation Model
2.2.3. Flash Flood and Non-Flash-Flood Areas
2.2.4. Population Density
2.3. Methods
2.3.1. Mapping LULC
2.3.2. Mapping and Forecasting of Flash Flood Susceptibility
Land Use Land Cover (LULC)
Stream Power Index (SPI)
Plan Curvature
Profile Curvature
Topographic Position Index
Topographic Wetness Index
Elevation
Slope
Aspect
2.3.3. Flash Flood Extent Mapping
2.3.4. Damage Assessment—Flash Flood Exposure
2.4. Machine Learning Algorithms
2.4.1. Support Vector Machine (SVM)
2.4.2. Logistic Regression (LR)
2.4.3. Random Forest (RF)
2.4.4. Naïve Bayes (NB)
2.4.5. Multilayer Perceptron (MLP)
2.4.6. Classification and Regression Trees (CART)
2.4.7. Hyperparameters of Machine Learning Models
2.5. Accuracy Assessment
2.5.1. Binary Classification’s Accuracy Assessment
2.5.2. Multiclass Classification’s Accuracy Assessment
3. Results
3.1. Land Use Land Cover (LULC)
3.2. Flash Flood Susceptibility
3.3. Flash-Flooded Areas
3.4. Flash Flood Exposure
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Name | Pixel Size | Wavelength | |
---|---|---|---|
B1: Aerosols | 60 m | S2A: 443.9 nm | (S2B): 442.3 nm |
B2: Blue | 10 m | S2A: 496.6 nm | (S2B): 492.1 nm |
B3: Green | 10 m | S2A: 560 nm | (S2B): 559 nm |
B4: Red | 10 m | S2A: 664.5 nm | (S2B): 665 nm |
B5: Red Edge 1 | 20 m | S2A: 703.9 nm | (S2B): 703.8 nm |
B6: Red Edge 2 | 20 m | S2A: 740.2 nm | (S2B): 739.1 nm |
B7: Red Edge 2 | 20 m | S2A: 782.5 nm | (S2B): 779.7 nm |
B8: NIR | 10 m | S2A: 835.1 nm | (S2B): 833 nm |
B8A: Red Edge 3 | 20 m | S2A: 864.8 nm | (S2B): 864 nm |
B9: Water vapor | 60 m | S2A: 945 nm | (S2B): 943.2 nm |
B10: Cirrus | 60 m | S2A: 1373.5 nm | (S2B): 1376.9 nm |
B11: SWIR 1 | 20 m | S2A: 1613.7 nm | (S2B): 1610.4 nm |
B12: SWIR 2 | 20 m | S2A: 2202.4 nm | (S2B): 2185.7 nm |
Reducer | Band | Resolution |
---|---|---|
ee.Reducer.max() | Blue (B2), Green (B3), Red (B4), NIR (B8) | 10 m |
ee.Reducer.min() | Blue (B2), Green (B3), Red (B4), NIR (B8) | 10 m |
ee.Reducer.median() | Blue (B2), Green (B3), Red (B4), NIR (B8) | 10 m |
ee.Reducer.min() | Blue (B2), Green (B3), Red (B4), NIR (B8) | 10 m |
ee.Reducer.mode() | Blue (B2), Green (B3), Red (B4), NIR (B8) | 10 m |
ee.Reducer.max() | NDVI, NDWI, NDBI, BSI | |
ee.Reducer.min() | ||
ee.Reducer.median() | 10 m | |
ee.Reducer.min() | ||
ee.Reducer.mode() |
LULC | No. of Polygons | No. of Pixels | Training Pixels | Testing Pixels |
---|---|---|---|---|
Water | 108 | 1852 | 1297 | 556 |
Bare Land | 97 | 641 | 447 | 192 |
Green land | 114 | 1174 | 822 | 353 |
Forest | 105 | 1492 | 1045 | 448 |
Urban | 116 | 988 | 693 | 296 |
SUM | 540 | 6147 | 4304 | 1845 |
Phase | Algorithm | Best Parameters |
---|---|---|
LULC Mapping | RF | n_estimators = 14 |
SVM | kernel = “linear”, Cost = 0.1 | |
CART | maxNodes: (50) | |
FF Susceptibility | SVM | kernel = “rbf”,C = 20 |
RF | n_estimators = 200, | |
LR | The same accuracy for all solvers: ‘newton-cg’, ‘lbfgs’, ‘liblinear’, ‘sag’, ‘saga’ | |
NB | var_smoothing: 1.000 × 10−9 | |
MLP | Hidden_layer_sizes = (15, 15), learning_rate_init = 0.01, mlp.out_activation_ = ‘sigmoid’ |
Actual | |||
---|---|---|---|
Positive | Negative | ||
Predicted | Positive | True Positive | False Positive |
Negative | False Negative | True Negative |
Actual | |||||||
---|---|---|---|---|---|---|---|
A | B | C | D | Sum | UA | ||
predicted | A | 107 | 2 | 7 | 10 | 126 | 0.8492 |
B | 1 | 80 | 5 | 3 | 89 | 0.8989 | |
C | 4 | 11 | 100 | 6 | 121 | 0.8264 | |
D | 3 | 2 | 7 | 98 | 110 | 0.8909 | |
Sum | 115 | 95 | 119 | 117 | |||
PA | 0.9304 | 0.8421 | 0.8403 | 0.8376 |
Water | Barre Land | Greenland | Forest | Urban | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SVM | RF | CART | SVM | RF | CART | SVM | RF | CART | SVM | RF | CART | SVM | RF | CART | ||
Max | UA | 0.97 | 0.99 | 0.96 | 1 | 0.96 | 0.66 | 0.64 | 0.77 | 0.72 | 0.80 | 0.80 | 0.73 | 0.94 | 0.92 | 0.87 |
PA | 0.91 | 0.94 | 0.89 | 0.89 | 0.8 | 0.75 | 0.66 | 0.84 | 0.66 | 0.94 | 0.74 | 0.74 | 0.98 | 0.92 | 0.81 | |
Mean | UA | 0.99 | 0.96 | 0.96 | 0.79 | 0.92 | 0.8 | 0.95 | 0.87 | 0.89 | 0.93 | 0.8 | 0.76 | 1 | 0.99 | 0.98 |
PA | 1 | 0.96 | 0.92 | 1 | 0.91 | 0.91 | 0.87 | 0.86 | 0.82 | 0.9 | 0.83 | 0.87 | 0.98 | 0.95 | 0.95 | |
Median | UA | 0.95 | 0.98 | 0.96 | 0.97 | 0.87 | 0.75 | 0.89 | 0.76 | 0.9 | 0.69 | 0.81 | 0.79 | 1 | 0.96 | 0.93 |
PA | 0.96 | 0.93 | 0.9 | 1 | 0.85 | 0.74 | 0.72 | 0.8 | 0.87 | 0.8 | 0.95 | 0.87 | 0.97 | 0.95 | 0.93 | |
Min | UA | 0.98 | 0.9 | 0.91 | 0.63 | 0.89 | 0.57 | 0.62 | 0.72 | 0.73 | 0.77 | 0.88 | 0.86 | 0.98 | 0.93 | 0.89 |
PA | 1 | 0.96 | 0.98 | 0.8 | 0.41 | 0.68 | 0.69 | 0.85 | 0.65 | 0.68 | 0.79 | 0.83 | 1 | 0.99 | 0.78 | |
Mode | UA | 0.99 | 0.94 | 0.96 | 0.27 | 0.52 | 0.28 | 0.49 | 0.72 | 0.63 | 0.89 | 0.77 | 0.68 | 0.93 | 0.82 | 0.83 |
PA | 0.99 | 0.94 | 0.92 | 0.58 | 0.37 | 0.4 | 0.62 | 0.72 | 0.53 | 0.68 | 0.87 | 0.75 | 0.7 | 0.87 | 0.78 | |
Avg UA | 0.98 | 0.95 | 0.95 | 0.73 | 0.83 | 0.61 | 0.72 | 0.77 | 0.77 | 0.82 | 0.81 | 0.77 | 0.97 | 0.92 | 0.90 | |
Avg PA | 0.97 | 0.95 | 0.92 | 0.85 | 0.67 | 0.70 | 0.71 | 0.81 | 0.71 | 0.80 | 0.84 | 0.81 | 0.93 | 0.94 | 0.85 |
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SELLAMI, E.M.; Rhinane, H. Google Earth Engine and Machine Learning for Flash Flood Exposure Mapping—Case Study: Tetouan, Morocco. Geosciences 2024, 14, 152. https://doi.org/10.3390/geosciences14060152
SELLAMI EM, Rhinane H. Google Earth Engine and Machine Learning for Flash Flood Exposure Mapping—Case Study: Tetouan, Morocco. Geosciences. 2024; 14(6):152. https://doi.org/10.3390/geosciences14060152
Chicago/Turabian StyleSELLAMI, EL Mehdi, and Hassan Rhinane. 2024. "Google Earth Engine and Machine Learning for Flash Flood Exposure Mapping—Case Study: Tetouan, Morocco" Geosciences 14, no. 6: 152. https://doi.org/10.3390/geosciences14060152