Develop an Approach for Mapping an Accurate and Appropriate Flood Susceptibility for Quang Binh Province, Vietnam, Using Machine Learning Algorithms and Remote Sensing

Hung Tran Dang; Tinh Tran Van; Bang Nguyen Thanh; Nga Pham Thi Thanh; Huyen Bui Thanh; Hiep Quang Pham

doi:10.2478/cee-2026-0089

Develop an Approach for Mapping an Accurate and Appropriate Flood Susceptibility for Quang Binh Province, Vietnam, Using Machine Learning Algorithms and Remote Sensing

Civil and Environmental Engineering

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By: Hung Tran Dang, Tinh Tran Van, Bang Nguyen Thanh, Nga Pham Thi Thanh, Huyen Bui Thanh and Hiep Quang Pham

Open Access

|Mar 2026

Figures & Tables

Table 1:

Mathematical foundations and roles of applied machine learning algorithms

No.	Machine learning algorithm	Mathematical basis	Application / Reference
1	ANN	$\begin{matrix} h^{(l)} = f^{l} (W^{(l)} h^{(l - 1)} + b^{(l)}), l = 1, \dots ., L \\ \hat{y} = h^{(L)} \end{matrix}$	Multi-layer network for nonlinear pattern learning (Rumelhart et al., 1986).
2	KNN	$D_{(x, y)} = (\sum_{i = 1}^{n} {x_{i} - y_{i}\|}^{p})$	Instance-based learning using Minkowski distance (Cover & Hart, 1967).
3	SVM	$min_{w, b} \frac{1}{2} {w‖}^{2} subject to y_{i} (w^{T} x_{i} + b) \geq 1, \forall i$	Maximum-margin hyperplane classifier (Cortes & Vapnik, 1995).
4	CART	$arg min_{j, t} \frac{N_{L}}{N} \cdot ℶ (L) + \frac{N_{R}}{N} \cdot ℶ (R)]$	Recursive binary splitting to minimize impurity (Breiman & Ihaka, 1984).
5	RF	$m_{M, n} (x) \sum_{j = 1}^{M} m_{n} (x; Θ j; D_{n})$	Ensemble of decision trees with bootstrap aggregation (Ho, 1995; Breiman, 2001).
6	Naive Bayes	$\hat{y} = \underset{c \in C}{arg max} P (c) \prod_{i = 1}^{n} P (x_{i} \| c)$	Probabilistic classifier with conditional independence (Mitchell, 1997).
7	GBM	$F_{M} (x) = \sum_{m = 1}^{M} γ_{m} h_{m} (x)$	Sequential additive model minimizing loss (Friedman, 2001).
8	XGBoost	$ℒ^{(t)} = \sum_{i = 1}^{n} l (y_{i}, {\hat{y}}_{i}^{(t - 1)\}} + f_{t} (x_{i})) + Ω (f_{t})$	Optimized regularized boosting where Ω(ft) is optimized with histogram-based splitting to enhance efficiency on large datasets (Chen & Guestrin, 2016).
9	LightGBM	$ℒ^{(t)} = \sum_{i = 1}^{n} l (y_{i}, {\hat{y}}_{i}^{(t - 1)\}} + f_{t} (x_{i})) + Ω (f_{t})$	Leaf-wise histogram-based gradient boosting where Ω(ft) is optimized with histogram-based splitting to enhance efficiency on large datasets (Ke et al., 2017).

Table 2:

Hyperparameters and grid search of applied machine learning algorithms

No.	Machine learning algorithms	Hyperparameters	Grid search
1	ANN	size, decay	size = {3, 5, 7}; decay = {0.1, 0.01, 0.001}
2	KNN	k	k = {3, 15, 2}
3	SVM	C, σ	tuneLength = 10
4	CART	cp	tuneLength = 10
5	RF	mtry	mtry = {2, 4, 6, 8}; ntree = 500
6	Naive Bayes	laplace	laplace = {0, 0.5, 1, 2}
7	GBM	n.trees, depth, shrinkage, minobsinnode	n.trees = {50–300}; depth = {1, 3, 5}; shrinkage = {0.01, 0.05, 0.1}; minobsinnode = {10, 20}
8	XGBoost	nrounds, max_depth, eta, subsample, colsample_bytree, gamma	nrounds = {100, 200}; max_depth = {3, 6}; eta = {0.05, 0.1}; subsample = 0.8; colsample_bytree = 0.8
9	LightGBM	num_leaves, learning_rate, feature_fraction, bagging_fraction, early_stopping_rounds	num_leaves = 31; learning_rate = 0.1; feature_fraction = 0.8; bagging_fraction = 0.8; early_stopping = 20

Flood controlling factors: (a) Aspect, (b) Curvature, (c) Flow accumulation, (d) Flow direction, (e) River density, (f) Road density, (g) Slope, (h) Soil types, (i) LC, (j) TWI, (k) Distance to river, (l) Flood in Quang Binh based on DEM

Table 3:

Information on applied flood controlling factors

No.	Flood controlling factors	Value (Min-Max)	Data sources and equations
1	Aspect	0 – 360°	Derived from DEM (Aspect Tool/Arcmap)
2	Curvature	−12.74 – 17.50	Derived from DEM (Curvature Tool/Arcmap)
3	Flow accumulation	1 – 268066	Derived from flow direction with D8 flow direction type and represented the total accumulated flow for each cell (Flow Accumulation Tool/Arcmap)
4	Flow direction	1 – 128	Derived from DEM, used D8 flow direction type, and values were based on neighboring flow cells (Flow Direction Tool/Arcmap)
5	River density	0 – 0.69	River density in a given neighbor (km/km²) (Line Density/Arcmap)
6	Road density	0 – 5.67	Road density in a given neighbor (km/km²) (Line Density/Arcmap)
7	Slope	0 – 75.62°	Derived from DEM (Slope Tool/Arcmap)
8	Soil types	1 – 14 (Representative for each soil type)	Downloaded at https://www.fao.org/soils-portal
9	LC	1 – 11 (Representative for each type of LC)	Downloaded at https://livingatlas.arcgis.com/landcover/
10	TWI	7.89 – 30.32	$TWI = ln (\frac{α}{tan (β)})$ α: The amount of water contained in every pixel (flow accumulation), or specific area through a certain point per unit Contour length β: Slope gradient (radians)
11	Distance to river	0 – 23429	Calculated, for each cell, the distance to the closest source (Euclidean Distance/Arcmap)

Table 4:

Downloaded satellite image

Satellite	Name	Mode	Orbit pass	Relative orbit	Time
Sentinel-1	S1A_IW_GRDH_1SDV_20201018T11053 2_20201018T110557_034850_041018_E503	IW	ASCENDING)	128	2020-10-18 11:05:32

Sentinel-1 satellite image in Quang Binh province (18/10/2020)

Pearson correlation among applied flood controlling factors

Table 5:

Optimal hyperparameters of applied machine learning algorithms

No.	Machine learning algorithms	Optimal hyperparameters
1	ANN	size = 3; decay = 0.1
2	KNN	k = 15
3	SVM	sigma = 0.0891; C = 128
4	CART	cp = 0.00071
5	RF	mtry = 2
6	Naive Bayes	laplace = 0
7	GBM	n.trees = 300; depth = 5; shrinkage = 0.05; minobsinnode = 20
8	XGBoost	nrounds = 200; max_depth = 6; eta = 0.05; colsample_bytree = 0.8; subsample = 0.8; min_child_weight = 1
9	LightGBM	num_leaves = 31; learning_rate = 0.1; feature_fraction = 0.8; bagging_fraction = 0.8; bagging_freq = 5

Table 6:

Accuracy assessment of applied machine learning algorithms

No.	Model	Accuracy	AUC	Precision	Recall	F₁
1	ANN	0.9716	0.9847	0.7703	0.3314	0.4634
2	KNN	0.9626	0.8205	0.2500	0.0058	0.0114
3	SVM	0.9755	0.9810	0.8021	0.4477	0.5746
4	CART	0.9811	0.9675	0.7763	0.6860	0.7284
5	RF	0.9821	0.9942	0.8678	0.6105	0.7167
6	Naive Bayes	0.9202	0.9790	0.3126	0.9651	0.4723
7	GBM	0.9834	0.9941	0.8065	0.7267	0.7645
8	XGBoost	0.9841	0.9948	0.8311	0.7151	0.7688
9	LightGBM	0.98450.9845	0.9942	0.8289	0.7326	0.7778

ROC results of applied machine learning algorithms

Flood susceptibility mapping and relevance of factors from machine learning algorithms, and flood interpretation from satellite image

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DOI: https://doi.org/10.2478/cee-2026-0089 | Journal eISSN: 2199-6512 (formerly 1336-5835) | Journal ISSN: 1336-5835

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Language: English

Submitted on: Dec 12, 2025

Accepted on: Jan 23, 2026

Published on: Mar 19, 2026

Published by: University of Žilina

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

Flood susceptibility,

Machine learning,

Historical flood,

Storm-induced water level rise,

Quang Binh province

Related subjects:

Business and economics,

Business management,

Industries,

Environmental management

© 2026 Hung Tran Dang, Tinh Tran Van, Bang Nguyen Thanh, Nga Pham Thi Thanh, Huyen Bui Thanh, Hiep Quang Pham, published by University of Žilina
This work is licensed under the Creative Commons Attribution 4.0 License.

AHEAD OF PRINT

Develop an Approach for Mapping an Accurate and Appropriate Flood Susceptibility for Quang Binh Province, Vietnam, Using Machine Learning Algorithms and Remote Sensing

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