Performance Analysis of Machine Learning Algorithms in Storm Surge
Vai-Kei Ian
1 a
, Rita Tse
1, 2
, Su-Kit Tang
1, 2 b
and Giovanni Pau
1, 3, 4 c
Faculty of Applied Sciences, Macao Polytechnic University, R. de Lu
ıs Gonzaga Gomes, Macao SAR, China
Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence of Ministry of
Education, Macao Polytechnic University, R. de Lu
ıs Gonzaga Gomes, Macao SAR, China
Department of Computer Science and Engineering - DISI, University of Bologna, Via Zamboni, 33, 40126 Bologna, Italy
UCLA Computer Science Department, 404 Westwood Plaza, Los Angeles, CA, U.S.A.
Storm Surge, Machine Learning, Ensemble Machine Learning Algorithm, Natural Disaster.
Storm surge has recently emerged as a major concern. In case it occurs, we suffer from the damages it creates.
To predict its occurrence, machine learning technology can be considered. It can help ease the damages created
by storm surge, by predicting its occurrence, if a good dataset is provided. There are a number of machine
learning algorithms giving promising results in the prediction, but using different dataset. Thus, it is hard
to benchmark them. The goal of this paper is to examine the performance of machine learning algorithms,
either single or ensemble, in predicting storm surge. Simulation result showed that ensemble algorithms can
efficiently provide optimal and satisfactory result. The accuracy of prediction reaches a level, which is better
than that of single machine learning algorithms.
The use of machine learning (ML) involves algo-
rithms and statistical models that can learn through
inference and pattern recognition (Selvam and Babu,
2015), and that can adapt, without being explicitly
programmed, to the changing environments. Recent
advances in machine learning have led to its applica-
tion to many domains, solving various types of prob-
lems (Chan et al., 2021b; Chan et al., 2021a; Lin et al.,
2021; Tse et al., 2020; Cheok et al., 2022).
Ensemble ML algorithms use multiple bases of
ML algorithms to give a common estimate of a re-
sult in order to reduce the generalization error. In the-
ory, the prediction error should be decreased if the
base prediction models are sufficiently independent
and diverse. Stability and accuracy are then increased
by minimizing error caused by factors such as noise,
bias, and volatility (Lessmann et al., 2015). In other
words, it augments and improves overall prediction
accuracy over that of single algorithms, by combining
results from numerous models. The combined result
is usually better in terms of prediction accuracy when
compared to utilizing a single ML algorithm. There-
fore, ensemble ML algorithms are also widely used in
a variety of applications and fields.
Prediction of storm surge in risk assessment has
been one of the difficult problems for years due to the
complex structure of storm surge and variety of influ-
encing factors being developed while it is progress-
ing. Applying machine learning (ML) techniques in
risk assessment continuously evolves due to their abil-
ity to capture the associated relationships efficiently.
Thus, various machine learning algorithms have been
proposed to predict storm surges previously. How-
ever, quality of prediction is hardly compared, as
datasets of different structure and data type were used.
In this paper, single and ensemble ML algorithms
will be evaluated using a storm surge dataset for
prediction of possible storm surge occurrence in the
South China Sea region. The dataset, collected from
Hong Kong (HKO, 2021) and Kaohsiung (CWB,
2021) on storm scenarios from 2017 to 2020, will be
used for training the algorithm models with hyperpa-
rameters tuned using 10-fold cross-validation. Eval-
uation result on testing showed that ensemble ML al-
gorithms have advantages over single ML algorithms,
achieving a satisfactory level of certainty and confi-
Ian, V., Tse, R., Tang, S. and Pau, G.
Performance Analysis of Machine Learning Algorithms in Storm Surge Prediction.
DOI: 10.5220/0011109400003194
In Proceedings of the 7th International Conference on Internet of Things, Big Data and Security (IoTBDS 2022), pages 297-303
ISBN: 978-989-758-564-7; ISSN: 2184-4976
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
The remainder of the paper is organized as fol-
lows. In section 2, a background of ensemble ML al-
gorithms in prediction will be introduced. After that,
we give the evaluation configuration and result in sec-
tion 3. In section 4, a discussion on the limitations of
the simulation will be given. Finally, the conclusion
of the work is provided in section 5.
The application of machine learning (ML) algorithms
in risk assessment for storm surges are continuously
evolving due to their ability to capture the associ-
ated relationships efficiently. Various machine learn-
ing algorithms have been proposed to predict for
storm surges previously (Sarzaeim et al., 2017; Wu
et al., 2019; Kim et al., 2019; Modaresi et al.,
2018; Ni et al., 2020; Sankaranarayanan et al., 2020;
de Oliveira and de Carvalho Carneiro, 2021; Theera-
Umpon et al., 2008; Yu et al., 2006). To improve pre-
diction accuracy, ensemble ML algorithms have been
Max Voting (Arafat et al., 2019), Averaging, and
Weighted Average (Shahhosseini et al., 2022) are
typical basic ensemble techniques, whereas Bagging,
Boosting, Blending, and Stacking (Dou et al., 2020;
Franch et al., 2020) are prominent advanced ensem-
ble approaches. These strategies can improve and en-
hance performance when more patterns are observed
and the final prediction is a consensus from the mod-
els that comprise it. For example, Bagging, or boot-
strap aggregations, is the process of creating models
in parallel, which might be similar or different, and
averaging their related predictions as the final result.
Boosting, on the other hand, refers to the sequential
construction of models by repeatedly evaluating the
success of ancestral models in a hierarchy. The subor-
dinate level of model emphasizes the learning process
for estimate in cases when previous models failed to
perform successfully. AdaBoost, Gradient Boosting,
and XGBoost are three popular approaches (Mahesh,
2020). While Stacking seeks to link models by in-
cluding their outputs as features into the final model,
Blending is quite similar in that it leverages those base
models to deliver predictions as new features, with the
final model being trained on the new features to yield
the ultimate prediction. The sole distinction is that
in Blending, the meta model is trained on a separate
holdout set rather than a complete and folded training
set. In other words, Stacking employs out-of-fold pre-
dictions for the next layer’s training dataset, whereas
Blending uses a validation set instead for the subse-
quent layer’s training.
Both single ML and ensemble ML algorithms can
give promising and useful result in storm surge pre-
diction. However, their performance in prediction is
not fairly comparable as different datasets were used
in their training and testing. In this paper, using one
dataset with various attributes from tropical cyclones,
tide levels, and meteorological conditions, for train-
ing and testing, the performance of single ML and
ensemble ML algorithms are evaluated.
To evaluate the performance of ML algorithms (single
or ensemble), a high-quality dataset with appropriate
attributes is crucial. It will be injected into both single
and ensemble ML algorithms individually for train-
ing, testing, and validation of performance in terms
of accuracy. Single ML algorithms include Decision
tree, Naive Bayes, K-nearest neighbors, XGBoost and
SVM; whereas ensemble ML algorithms include Bag-
ging, Random Forest, AdaBoost, Gradient boost, Vot-
ing and Stacking. Configuration and comparative re-
sults will be discussed further in section 3.2.
3.1 Dataset
In this paper, the storm surge dataset (HKO, 2021;
CWB, 2021) is used in evaluation as it contains var-
ious important attributes from tropical cyclones, tide
levels, and meteorological conditions. In total, there
are 17 columns of features that are used for training
and validating models. Among them, grade, central
pressure, maximum wind, distance, azimuth angle,
mean sea level pressure (MSLP), changes in mean sea
level pressure, wind speed, changes in wind speed,
and abnormal surge level are included as they are the
most significant attributes in determining the possi-
bility of the occurrences of storm surge. A detailed
description is shown below.
Grade: Categories of tropical cyclones, including
1. Low (L), 2. Tropical Depression (TD), 3. Trop-
ical Storm (TS), 4. Severe Tropical Storm (STS),
5. Typhoon (TY)
Central Pressure: Surface pressure at the center of
the tropical cyclone as estimated or measured
Max Wind: Maximum value of the average wind
speed at the surface
Distance: Distance apart calculated by the latitude
and longitude of the tropical cyclone
IoTBDS 2022 - 7th International Conference on Internet of Things, Big Data and Security
Figure 1: Performance comparison on accuracy among different ML algorithms.
Azimuth Angle: The azimuth angle calculated by
the direction towards which the center of the ty-
phoon is moving
MSLP/WS: Measurement of the mean sea level
pressure/wind speed at ground station
MSLP/WS Delta: Changes in mean sea level
pressure/wind speeds measurements over the past
Surge: Difference between the actual water level
and the normal sea water level (astronomical tide)
As the amount of storm surge data in the dataset
is limited, it is divided into several parts for k-fold
cross-validation (CV), which is an efficient approach
in data utilization during model building. The ac-
curacy is ensured and noise influence is minimized
across the development cycle (Lee et al., 2020; Jung,
2018). For generalization of accuracy, it allows unbi-
ased estimates by validating a prediction model with
unseen data.
For ensemble ML algorithms, the input dataset is
divided into k samples. One of the k samples will be
designated as the test set, while the remaining k - 1
samples will be designated as the training set. Every
sample is subjected to a recursive cycle on all classi-
fiers. The performance of the algorithms could then
be summarized across all k trials collectively, demon-
strating the accuracy of prediction on unseen data.
3.2 Configuration
In the evaluation, an intensive simulation was car-
ried out to compare the performance of single and en-
semble ML algorithms. The simulation environment
was setup with a high-end computer running CentOS
Linux with Python and open-sourced ML libraries
eron, 2019; Stan
cin and Jovi
c, 2019; Raschka
et al., 2020). The hardware and software configura-
tion for the simulation environment is listed below.
1. Processing (CPU): 3.20GHz Intel
(4.60GHz boost) 6 cores, 12 threads, with 12MB
Smart Cache
2. Graphics (GPU): NVIDIA Quadro P1000 (4GB
3. RAM: 32GB (2667MHz)
4. Storage: 450GB SSD + 930GB HDD
5. OS: CentOS Linux
6. Language: Python 3.9
7. Libraries: Numpy, Scipy, Pandas, Scikit-learn, Tensor-
Flow, Keras, Matplotlib
3.3 Results
Using the storm surge dataset introduced in section
3.1, experiments on the 5 single ML algorithms and 6
ensemble ML algorithms introduced in section 3 with
data re-sampling using k-fold CV were conducted and
summarized in Figure 1. The results revealed that
ensemble ML algorithms could expectantly give op-
timal prediction outcomes against the single ML al-
gorithms. In addition, some of the algorithms have
been selected for performance evaluation by using
Mean Absolute Error (MAE) and Mean Squared Error
(MSE) (Gaudette and Japkowicz, 2009; Kumar et al.,
2020) along with accuracy obtained respectively. Re-
sults complied with the overall trend for performance
behaviors where satisfactory results could be obtained
from ensemble algorithms, as can be seen in Fig-
ure 2a, 2b & 2c. However, it does not apply to all
problem-solving strategies. In Figure 2d, 2e & 2f,
some single ML algorithms show similar performance
Performance Analysis of Machine Learning Algorithms in Storm Surge Prediction
(a) Better performance in Ensembles. (b) Finer results in MAE.
(c) Finer results in MSE.
(d) Similar performance. (e) Similar results in MAE.
(f) Similar results in MSE.
Figure 2: Performance level acquired from single/ensemble ML methods.
with ensemble algorithms in terms of accuracy, MAE
& MSE.
3.4 Adjustments to Ensemble Methods
For machine learning algorithms, choosing an appro-
priate level of model complexity is a key balancing
act. If a model is too complex, data will be fit into
the model entirely, resulting in poor generalization to
unseen data (overfitting) in testing. If its complex-
ity is too low, it would not capture all information in
the data (underfitting). To improve the stability and
accuracy of the prediction model by reducing errors
caused by variables such as noise, bias, and variability
when ensemble algorithms are adopted becomes criti-
cal and essential. To tackle this issue, hyperparameter
tuning can be applied, which can create a significant
impact on behaviors and accuracy of the model, by
adjusting its associated parameters for the best out-
Ensemble algorithms have gained a lot of atten-
tion and are extensively utilized because they can out-
perform a single ML approach in general. These
approaches used the notion of re-sampled or re-
weighted training data sets from the original data, and
a prediction model was applied to each of them many
times. Boosting, for example, has been designed to
increase the performance of any weak learning algo-
rithm by dynamically modifying the distribution of
the training data set and taking a weighted major-
ity vote on their predictions. The bagging approach,
on the other hand, uses bootstrap samples to produce
classifiers in an ensemble. Each of them is based on
random sampling with the same number of instances
as the original data. The ultimate result is reached by
majority voting. Meanwhile, random forest was de-
veloped as an ensemble approach for combining tree
classifiers in such a way that each level of trees is de-
termined by the values of their features, which are
selected from a set of features sampled individually.
When dividing a node, the chosen split is no longer
the best split across all features during tree building.
Instead, the split is determined by selecting the best
option from a random subset of the characteristics.
Because of this unpredictability, the bias generally in-
creases significantly, but the variance reduces owing
to the averaging effect.
To demonstrate, the ideal size of decision trees
should be viewed in terms of the overall performance
of ensemble techniques during which decision trees
are used as model classifiers. The number of target
variable categories that can explain the nature of the
input dataset is the most common tree size in boost-
ing. It demonstrates that the error rate of a stump
tree, the number of classes, and the depth of a single
tree may be used to determine the ideal tree size for
a given dataset. In general, greater tree sizes produce
more accurate findings. In other words, the accuracy
of ensemble trees increase as tree size increases.
To evaluate the important affects on accuracy of
several ensemble techniques by tree size, its effects
on two types of ensemble methods are distinct. First,
the tree size impact is not uniform in the boosting
type ensemble technique. The ideal tree size might be
modest depending on the dataset. In contrast to boost-
ing, bootstrap-based ensemble approaches (bagging
IoTBDS 2022 - 7th International Conference on Internet of Things, Big Data and Security
(a) (b)
(d) (e) (f)
Figure 3: Parameter adjustments directly affect performance for different ensemble ML algorithms.
and random forest) produce more uniform outcomes.
In most scenarios, a greater number of tree sizes re-
sults in higher accuracies. Changes to the depth of a
tree, or the size and number of trees in the forest, as
shown in Figure 3, would improve the overall perfor-
mance. It was shown than by having a proper adjust-
ment and optimal configuration in base estimator, all
ensemble models would have a satisfactory level of
accuracy, with Random Forest having the best perfor-
mance model at 91.80%. On the other hand, stack-
ing appears to be the least efficient in this compara-
tive experiment, with an accuracy of just 87.5%, even
though its flexibility and adaptability ease the devel-
opment of its prediction model.
Principal benefits to using ML approach in storm
surge prediction would be its excellent temporal ef-
ficiency. The proposed ensemble ML algorithms are
intuitive to use and very time effective, which is a
significant advantage over other current classical pre-
diction systems. It is not necessary to prepare any
documentation in advance for prediction events. As
a result, interpretation of the prediction findings is
mostly attainable without the need for expert assis-
tance. When provided accessible typhoon forecast
information, which could be collected automatically
from the Internet, each prediction could be made in
1–2 minutes, including gathering typhoon informa-
tion, data pre-processing, calculation, and visualiza-
tion, using a widely available PC. In comparison, if
the storm surge forecast was processed in the tradi-
tional method, it would take significantly longer. The
ensemble ML approach’s performance exhibits its ex-
cellent temporal efficiency. Users may also adjust the
predicted location and maximum wind speed in terms
of tropical cyclone intensity forecast uncertainty due
to the speedy processing time. Overall, the sug-
gested ML algorithms, which are effective and effi-
cient in their operational application, respond quickly
to the need for emergency consultation and might give
timely auxiliary decision-making support.
ML algorithms also enable the concept of multi-
scenario prediction. When the anticipated course of
the typhoon position deviates much from the actual
movement pattern, benefits of ensemble ML predic-
tion models are highlighted as the models can incor-
porate all conceivable typhoon positions, taking into
account not only the uncertainty of forward heading
direction but also variance in speed of movement.
Thus, a generalized and objective prediction result on
the possibility for having storm surges could be de-
Aside from the essential relevance and effective-
ness of ensemble ML algorithms in storm surge pre-
dictions, precision in various other elements such as
astronomical tide computation and tropical cyclone
forecasts form a critical basis for the success of the
ensemble ML approaches. The variability of the
weather system is the major source of prediction error.
Specifically, the route and severity of typhoons might
abruptly shift, making precise surge elevation prob-
lematic. As a result, the first and most important goal
is to unravel the mechanism of these fluctuations and
Performance Analysis of Machine Learning Algorithms in Storm Surge Prediction
include them accordingly into the prediction model in
order to improve forecast accuracy and enhance per-
formance. In general, such abnormal tracks may be
traced to the underlying surface condition, interaction
with other weather systems, and the presence of re-
lated atmospheric circulation in the absence of envi-
ronmental guiding flow. When a typhoon passes over
a warm body of water, strength changes are directly
connected with air-sea interaction. When the sea sur-
face temperature rises, the mixing layer thickens, or
the interaction time lengthens, typhoons build quickly
because more heat can be absorbed. The movement
of a typhoon, for example, would be meandering. As
a result, because the locations and intensities of the
typhoons are crucial features in the overall building
of the prediction model, this driving factor affects the
overall performance of the model. Second, consid-
ering the long-term implications of climate change is
essential to the appropriate operation of the predic-
tion model. The effects of climate change on storm
surges are multidimensional. On the one hand, the
frequency and severity of Typhoons have increased in
recent decades. During a storm, a rise in wind in-
tensity might result in more severe tragedies. On the
other hand, sea level is progressively increasing as a
result of receding glaciers and the melting ice in the
Arctic. Storm surge impacts and consequences would
be boosted proportionately. As a result, the success
of the prediction model should be able to reflect and
capture these changes, as well as the retraining of the
model with newly related collected data, in order to
produce and reflect a suitable assessment for the pos-
sibility of this natural catastrophe occurring.
Future research should consider the potential ef-
fects, characteristics and significant parameters of
storm surge more carefully, especially under the in-
fluences of global climate change and sea level rise.
Effects of future climate change could be addressed
by collection of more atmospheric data from IoT de-
vices, such as smart lampposts, spread across the ter-
ritories which enable the prediction models to capture
recent changes in our climate and provides more reli-
able prediction results. This is one of the key compo-
nents in future attempts to mitigate storm surge haz-
ards. To get the most out of these additional efforts,
combine a more effective and efficient sampling strat-
egy during model retraining with sufficient, adequate
and balanced training dataset. We believe that the
proposed ensemble prediction methods could further
be extended and adjusted for specific coastal applica-
tions, such as providing immediate operational surge
forecasts, probabilistic coastal flood hazard assess-
ments, or future surge forecasts during typhoon sea-
sons. Future studies should also address and investi-
gate the shifting patterns of storm surges over time, as
well as the impacts of the wind intensity field, based
on the findings of this paper.
As storm surge datasets vary considerably among
studies, benchmarking machine learning algorithms,
both single and ensemble, using the same dataset can
reveal their performance in terms of prediction accu-
racy. This paper compares the performance of the al-
gorithms and highlights the significance of ensemble
machine learning algorithms in storm surge predic-
tion. In our simulation, we found that the ensemble
machine learning algorithms, Random forest classifi-
cation and Stacking, performs better than single and
other ensemble machine learning algorithms in storm
surge prediction. In case of the occurrence of overfit-
ting and underfitting in training, its prediction result
will contain bias, which can be resolved by hyperpa-
rameter tuning to the algorithm.
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Performance Analysis of Machine Learning Algorithms in Storm Surge Prediction