HAZROAD - MONITORING SYSTEM FOR HAZARD PREVENTION IN
HIGHWAYS - A CASE STUDY IN BRAZILIAN FEDERAL HIGHWAYSFERNANDA OLIVEIRA DE SOUSA
Department of Civil Engineering, Federal University of Rio de Janeiro, Brazil
fernandasousa@poli.ufrj.br

ABSTRACTThis study introduces a combination of Remote Sensing (RS) and Artificial Intelligence (AI)techniques to create a system for mapping and monitoring hazard events on Brazilian
Federal highways. The proposed method is subdivided into four steps: I – Creating a Hazard
inventory, II – Mapping hazards, III – Prediction of new events, and IV – Creating a web
application for data management. The inventory was made using publications gathered fromthe official Twitter accounts of the Brazilian Federal Highway Police (PRF) and the National
Department of Transportation Infrastructure (DNIT), agencies responsible for supervisingand coordinating the Brazilian road system. The collected data were analyzed by Natural
Language Processing (NLP) techniques and classified into two categories, Relevant andIrrelevant. The first is for cases where the text gave information about natural disasters on
highways, and the second one is where the publication referred to other subjects. Threedifferent models were employed for NLP classification, Logistic Regression (LogR), Random
Forest (RF), and Support Vector Machine (SVM). They all provided a reasonable metrics
score of approximately 97% using the Term Frequency-Inverse Document Frequency (TFIDF) algorithm. In the following phases, both mapping and forecasting models were
designed to emphasize geotechnical and flood disasters triggered by rainfall, the mostcommon events in Brazil. To map flood extent and landslide, high-resolution satellite
imagery acquired by the Sentinel Synthetic Aperture Radar (SAR), were used. The
processing of these images allows the automatic calculation of the extent of flooding andlandslide areas. In stage III, the forecast model was created by segmentation of the images
collected in the preceding stage. The Deep Learning (DL) model U-NET was adopted forthis purpose. The U-NET resulted in remarkable precision metrics ranges of 90%-96% forboth cases. Moreover, hazard maps were obtained by a Multi-Criteria. Finally, the web app
and database were built to report new events and store all the data from the previous
phases.
1. INTRODUCTION
Brazil has the fourth most extensive road network in the world. Composed of 1,720,700.00
km of extension among urban and rural roads, of which 75,553.00 km are federal highways,
the object of this study [1,2]. Figure 1 illustrates the Brazilian federal highway network. The
figure shows in detail the Federal Highways in the state of Espírito Santo chosen as thestudy region of this paper. All federal highways in Brazil are given the prefix “BR-” and three
digits, where the first one depends on their direction. They are classified into five categories:
Radial, Longitudinal, Transversal, Diagonal, and Connecting. Radial (BR-0XX), are thehighways that depart from the Federal Capital in the direction of the country’s extremes.
Longitudinal (BR-1XX), are those that cross the country in a north-south direction.
Transversal (BR-2XX) are the ones in the east-west direction. On the other hand, Diagonal(BR-3XX) can have two modes of orientation: Northwest-Southeast or NorthwestSouthwest. And Connection highways (BR-4XX) are in either direction usually connecting
at least one federal highway to international borders [1].

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XXVIIth World Road CongressFigure 1 – Brazilian Federal Highways network
Brazilian transportation system is dependent mainly on road transportation, although it
suffers from problems with infrastructure, safety, and longer travel times over long distances.
In addition, factors such as the length of the network, the diverse hydro and geomorphologic
conditions of the territory, and the insufficient investment in maintenance and repair havemade them extremely vulnerable to weather events.
In several countries, floods, landslides, and rock falls in urban areas have become
increasingly frequent due to climate changes. These events impact lives, the environment,
economics as well the transportation system. In Brazil the overview is not different, to givea clear example, in December 2021, an intense volume of rainfall (4.5 times the historical
average) was recorded, which resulted in landslides, floods, falling trees, and roadway
destruction on several stretches of the BR 101, 174, 158, 358, and 459 highways. The
disasters affected at least 116 municipalities, compromising the mobility of freight andpassengers and consequently the country’s economy. For the repair of the affected
stretches, the Brazilian government has allocated about 39 million dollars through the MP
1086/2021 law proposal [3]. For this reason, the development of a disaster management
plan for highways has become a requirement in Brazil. A disaster management plan consistsof a set of strategies divided into four phases (i.e. Preparedness, Response, Recovery, andMitigation) that are designed to create local networks and structures to cut down on thepotential risks of disaster. In particular, the stage of preparedness social media and hazard
maps play a fundamental role to prevent major damage. Social networks such as Twitter,
Facebook, and Instagram have been fully implemented by stakeholders, public authorities,
and humanitarian organizations to provide information about natural disasters, andbroadcast risk alerts. Moreover, they can be considered an attractive tool to engage citizens
in preparedness activities. Regarding the studies of risk areas, evacuation site destinations,and escape routes, hazard maps are used. They are based on geographic information that
includes the history of previous disasters, as well as topographical and geological featuresthat contribute to the land formation and disaster susceptibility in the area [4,5].
The combination of Remote Sensing and Artificial Intelligence has recently grown in the fieldof natural disaster management. In part, due to the improvement of geospatial technologies
and the possibility to process high-resolution satellite imagery. But also because of theascension of open data platforms such as Google Earth Engine (GEE) that allow access to
geospatial Big data [6]. Cloud platforms facilitate the collection of high-resolution images[2]

XXVIIth World Road Congressfrom pre-and post-natural disasters. The analysis of texture information within these images
is essential for change detection [7]. Liu et al., supports that data-driven models, based on
a Machine Learning (ML) approach have achieved good performances in landslideprediction. Such models have also been used to understand the relationship between theoccurrence itself and its predictors [8]. In addition, Deep Learning (DL) techniques havebeen widely used in flood management, as in many cases they overcome the accuracy
limitations of numerical models traditionally employed [9,10]. In recent years, several studies
have been developing to identify the potential factors of risk and the possible damage
caused by natural disasters. For the same purpose, this study was created to complement
the existing references, highlighting the Brazilian case.
2. MATERIALS AND METHODSIn the HazRoad model, the inventory is developed by a combination of scraping data frominformation posted on Twitter and pre-processing natural language text. This allows for rapid
database creation as well as near-real-time data input. Once past events have been known,
the next step is to estimate affected areas by analyzing pre-and post-event images. Then,the hazard maps are designed. Subsequently, by employing deep learning techniques onthese mapped regions, future events can be predicted. Finally, a rule-based approach isused to estimate the possibility of a hazard occurring throughout the region of interest.
Specifically, the proposed framework consists of 14 successive steps. As illustrated in thefollowing figure (Figure 2).

Figure 2 – Flowchart of HazRoad Model
The overall data acquisition and processing approaches, likewise the machine learning andremote sensing methods adopted to prepare this research will be described in the next
sections.
2.1.

Inventory of Historical Events

In Brazil, both disaster management and monitoring are coordinated by the National Civil
Defense System of the municipalities in cooperation with the National Center for Monitoring[3]

XXVIIth World Road Congressand Alerting Natural Disasters (CEMADEN) [11]. Nevertheless, there is not a specific sector
that monitors natural disasters on highways or a survey of this data. Incidents such as
inundations, landslides, or rock falls that occur on roads are usually published on the Twitter
accounts of DNIT, and also of the Federal Highway Police of each state. Therefore, obtaining
information on these events is technically challenging due to the lack of an official source.
In fact, there are several possibilities to gather information from the Internet. Web Scraping
is one of the most widely used to retrieve web data, for this reason, it was adopted in thisstudy.
Natural language Processing was used before machine learning classification to filter andextract valuable information from tweets. NLP is defined as a subfield of Artificial
Intelligence. Its main objective is the implementation of mechanisms, theories, elements,and systems that enable human-machine interaction using its own natural language [12].
HazRoad NLP pipeline consists of three main parts: pre-processing text, classification, andfeature extraction. The pre-processing text means to clean data by removing noise, such as
punctuation marks, additional spaces, and unnecessary character encodings (e.g.
emoticons or emojis) [13]. In the classification phase, to establish whether the tweet reportsa hazard or not, three different ML classification methods were applied, Support Vector
Machines, Logistic Regression, and Random Forest. Finally, for feature extraction was
employed the Term Frequency-Inverse Document Frequency approach. The process offeature extraction essentially consists of capturing important phrases and words containedin a text. TF-IDF is a method of feature extraction by performing a clustering process basedon calculating the frequency of each word in the dataset used. [14]. The last stage aims tocreate a database with key information such as location, road name, and type of hazard.
2.2.

Machine and Deep Learning Models

Both Deep Learning and Machine Learning are fields of Artificial Intelligence. Machinelearning is the branch of computer science that provides computers the capability to learn
without being explicitly programmed [15]. The ML mathematical models make predictions intagged data through statistics theories [16]. Hence, it is generally necessary for some preprocessing to organize the data. On the other hand, Deep Learning excludes a significant
amount of pre-processing steps required by machine learning. These algorithms can
assimilate and process unstructured data, such as text and images, and they can also
automate feature extraction, reducing reliance on human experts. DL is best suited forcomplex problems such as image recognition, speech recognition, and natural language
processing [17]. AI models may be divided into three main classes: Supervised,
Unsupervised and Reinforced Learning. The whole models used to develop this project areclassified as supervised. This implies that labeled data sets were used to train thealgorithms. For all models, the fitted data was split into training, test, and validation in a ratioof 70%, 20%, and 10%, respectively. The following is a brief description of the techniques
employed in this study:● Logistic Regression: Logistic regression is a linear model, analogous to linear
regression. It finds a linear relationship between the variable target (dependent) andthe characteristics of the variables (independent). In terms of the linear function, thelogistic regression model predicts the probability p(x) that the dependent variablebelongs to the class of interest [18].
● Random Forest: RF is a supervised learning algorithm that is assumed to beversatile owing to its capacity to perform classification and regression tasks. Whenused, the algorithm produces a combination of decision trees to achieve a more
precise prediction. This algorithm is used commonly because it is simple,[4]

XXVIIth World Road Congressunderstandable, and presents good results in different types of machine learning
analysis [19].
● Support Vector Machine: SVM Classifier is a supervised machine learningtechnique that employs hyperplanes to transform the nonlinear features into linear
ones to classify features. A hyperplane is a decision plane that attempts to divide a
set of objects and classify them [20].
● U-NET: U-Net network is a deep convolutional network that has widely been used in
medical image segmentation [21]. Its major advantage is that it can be trained with asmall amount of data. U-Net is a two-stage deep learning model. Its architecture
includes an encoder model followed by a decoder model as depicted in figure 3.

Figure 3 – U-Net Architecture
The encoding path is analogous to a typical CNN structure and consists of sequential
convolution blocks. Every convolutional block contains two layers with a 3 × 3 kernel size
and a 2 x 2 max-pooling layer. Each convolutional layer is triggered by the rectified linear
unit (ReLU) activation function. In order to do non-linear down-sampling, a 2 x 2 max-poolinglayer is added to the end of the convolutional block in the encoder path. In contrast, a 2 x 2
up-sampling layer is added to the decoder path. Following a 3x3 convolutional layer comesthe up-sampling layer (see figure 2). This combination is what we refer to as learnable up
convolution. At the final, a 1x1 layer constructs a binary mask. This model uses a set of input
landslides and flooding images and their corresponding binary masks. During training andbased on the binary mask as the target output, the model learns how to classify each pixelof the images into different object labels [21,22,23].

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XXVIIth World Road Congress2.3.

Performance Metrics

To evaluate the performance of all the models were used four metrics: Accuracy, F1 score,
Precision, and Recall. Statistical metrics were computed based on true positive (TP), true
negative (TN), false positive (FP), and false negative (FN) variables. All metrics andvariables are briefly explained in table 1.
Table 1 – Performance Metrics
Metrics

Formula

FP
FN
TP
TN

False Positive
False Negative
True Positive
True Negative

Accuracy

𝑇𝑃 + 𝑇𝑁𝑇𝑃 + 𝑇𝑁 + 𝐹𝑃 + 𝐹𝑁

The ratio of samples that are correctly classified. This shows
how well the model works.

F1 score

2 𝑥 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 𝑥 𝑅𝑒𝑐𝑎𝑙𝑙
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 + 𝑅𝑒𝑐𝑎𝑙𝑙

It is a measure to evaluate classification systems and is a way
to combine the precision and recall results

Precision

𝑇𝑃
𝑇𝑃 + 𝐹𝑃The ratio of positive samples that are correctly classified. This
indicates the good predictability of the model.

Recall𝑇𝑃
𝑇𝑃 + 𝐹𝑁

The ratio of true positive samples that are correctly classified.
This indicates the good predictability of the model.

2.4.

Description
Number of actual negative samples classified as positive
Number of actual positive samples classified as negative
Number of actual positive samples correctly classified.
Number of actual negative samples correctly classified.

Satellite Imagery Analysis

For each case of hazard detection, pre-and post-event images were used. In both cases,
the time series scenes were acquired from the Google Earth Engine platform. For floods,
Sentinel – 1 GRD-IW products are used. The Sentinel-1 mission consists of two polarorbiting satellites (i.e. Sentinel-1A and Sentinel-1B) that work efficiently day and night,
operating with a C-band synthetic aperture radar instrument that allows imagery acquisition
regardless of weather or lighting conditions. It collects Synthetic Aperture Radar (SAR) datain single or dual polarization over 6 days. In order to simplify the handling of the images
employed in this project, it was adopted the following pre-processing procedures [24]. The
key aspects of pre-processing can be listed as follows:
1. Apply Orbit File: This operation enables the automatic download and update of orbit
state vectors in product metadata for each SAR picture, providing more accurate
satellite location and velocity information.
2. Thermal Noise Removal: Reduces noise effects in the inter-sub-swath texture,
particularly by normalizing the backscatter signal across all Sentinel-1 imagery,decreasing the number of discontinuities among both sub-swaths for scenes acquiredin multi-swath modes.
3. Calibration: Calibration is the method by which digital pixel values are converted toradiometrically calibrated SAR backscatter. The Sentinel-1 GRD product includes theinformation needed to apply the calibration equation.
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XXVIIth World Road Congress4. Speckle Noise removal: Speckle filtering is a technique for improving image quality
by minimizing speckles. It appears as a result of the interference of waves reflectedfrom a large number of elementary scatterers.
5. Terrain Correction: The pixels are projected onto a map system and re-sampled toa spatial resolution of 10 m. Topographic corrections are also accomplished using a
Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM). It
corrects the distortions in the terrain’s areas.
6. Conversion Linear into dB: A logarithmic transformation is used to convert theunitless backscatter coefficient to dB.
Landslide identification differs from flood identification in terms of data acquisition and preprocessing. First, Sentinel-2 scenes were utilized in landslide cases. Furthermore, the preprocessing techniques used for Sentinel-2 products are less complex than the ones
employed for flood detection (i.e. Sentinel – 1 scene). Its steps consist of filtering clouds andcompositing all images selected. Both techniques are described below:
1. Filter Clouds: This implies that the less cloudy Sentinel-2 granules are selected byfiltering the collection of images obtained during the pre-and post-event timeline. The
process is based on the proportion of cloud cover. The cloud threshold of 30% wasused in this study [25].
2. Composite Images: Pixel-based image compositing is a technique used in remotesensing to overcome some of the limitations such as data availability, cloud coverage,
image archive discontinuity, atmospheric interference, and radiometricinconsistencies caused by seasonal differences or changes in sun angles [26,27].
The HazRoad algorithm calculates the normalized difference vegetation index (NDVI)
to determine the composition of landslide areas. Scheip et al., proposed thiscompositing method in 2021, which is distinguished by the use of Google EarthEngine (GEE) to generate and perform calculations on a greenest-pixel composite.
The greenest pixel composite is defined as the highest NDVI value calculated for theentire compilation of images, it means pre- and post-event scenes [28].
2.5.

Hazard Mapping

According to a definition provided by The United Nations Office for Disaster Risk Reduction(UNDRR), a hazard is a process, phenomenon, or human activity that can result in death,injury, or other health consequences, property damage, economic and social interruption,or environmental degradation. This definition also subdivided hazard into three categories:
Anthropogenic, Natural, and Socio-natural [29]. In this paper, the term ‘hazard’ will be usedsolely to refer to the natural category. Natural hazards are those associated mainly withnatural processes and weather events. They include floods, volcanoes, earthquakes,
tsunamis, and landslides.
Specifically, for the HazRoad model, a slightly different proposal was adopted. Natural
disasters were grouped into three classes: Geohazards, Structural Collapses and Floods.
The Geo-hazard category covers landslides, erosion, and rock falls. Solheim et al. [30],define geohazards as incidents generated by geological, geomorphological, or climaticconditions or processes that pose serious threats to human life, property, and the naturaland built environment. Regarding, Structural Collapse the category includes road and bridge
collapses, and potholes in the road. And flooding refers to instances of flash flooding along[7]

XXVIIth World Road Congressthe highway. This classification system is useful because it helps distinguish common
occurrences on highways.
Overall, several conditioning factors can be considered for mapping hazards. For example,
the Global Facility for Disaster Reduction and Recovery (GFDRR) [31] recommends that for
mapping road disasters factors such as terrain, rainfall, road alignments, population data,
land cover, and land use should be included. Antzoulatos et al. [32], support that the most
relevant factors for flood mapping are elevation, slope, and aspect, all topographic
parameters, which are extracted from digital elevation models (DEM) of the study area. The
same conditioning factors were considered to design the HazRoad mapping models. For
both, the elevation models were obtained using the Shuttle Radar Topography Mission(SRTM). The SRTM V3 (SRTM Plus) product provided by NASA JPL has 1 arc-second(approximately 30m) resolution [33,34].
2.6.

Study Area

Due to the large extension of the Brazilian road network, and the availability of satellite
images, it was chosen as the study area for the Remote Sensing and Deep Learning models(steps 2 and 3) only the regions with a higher frequency of floods and mass movements inthe last three years. In particular, this work focuses on fourteen stretches of highway
between the municipalities namely Serra, Cariacica, Viana, and Domingos Martins, alllocated in the state of Espírito Santo, as shown in Figure 4.

Figure 4 – Study area
Espírito Santo is located in the southeast region of Brazil. Covering an area of 46,095,583
km², it is the fourth smallest Brazilian state. Nevertheless, its geographical location turns it
into an important link between the country's regions. It borders the states of Bahia (to thenorth), Minas Gerais (to the west), Rio de Janeiro (to the south), and the Atlantic Ocean (tothe east). Its road network comprises ten Federal highways (see Figure 1), among them BR101 and BR-262, which are the targets of this study. BR-101 is a longitudinal highway thatcrosses almost the entire Brazilian coast. BR-262 is a transverse highway that connects thecoast to Minas Gerais and Mato Grosso do Sul. It serves as an important exit corridor for
cattle, agriculture, and mining products.
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XXVIIth World Road Congress

The study domain is located in the central zone of Espírito Santo, which has a great variationin altitude. Regarding topography, the study area is characterized by the presence of low
elevations in the municipality of Serra, where kilometers 294, 299, 300, and 302 of the BR101 highway are located, and steep slopes in the sections of the BR-262 between Cariacica
and Domingos Martins. From the geomorphological point of view, this area is divided into
two morpho-structural groups, the Sedimentary Deposits, near the Serra, and theRemobilized Fold Bands in the other three municipalities. The sedimentary deposits aredefined by the occurrence of sandy and sandy-clay sediments with gravel levels, basicallyfrom the Barrier Formation group and coastal environments, deposited during the Cenozoic
period. While the remobilized fold belts are distinguished by evidence of crustal movements,
with fault marks, block displacements, and transverse faulting, imposing clear structuralcontrol over the present morphology [35]. Regarding the hydrological aspects, the study
area is comprised of two hydrographic basins, Santa Maria da Vitória River Basin (Serraand Cariacica), and Jucu River Basin (Domingos Martins, Cariacica, and Viana). According
to data from the National Water Agency (ANA), the respective basins present highvulnerability and frequency to flood events. It is worth noting that the studied stretches in the
Serra region are located in a fluvial accumulation zone, and therefore are subject to periodic
flooding [36].
2.7.

Mapping Areas Affected by Landslides and Floods

This step aims at outlining areas affected by mass movements or floods. For this, Sentinel
satellite images were collected at different time intervals, before and after each event. Boththe acquisition and processing of these images were performed on the Google Earth Engine
platform. The mapping phase is crucial for the development of the project because thecreation of the forecast model database depends on the images generated by it.
On November 14, 2019, an intense rainfall struck the mountainous region of the state of
Espírito Santo, specifically between the municipalities of Viana and Domingos Martins. As
a result, at least 6 points of landslides were registered on BR-262 that needed to be blocked.
According to information from the transportation department, at least 77 points of landslides
were reported on roads in this area between November 14 and 19 [37]. To map these
events, 53 pre-and post-disaster satellite images from SAR-2 were collected. The definitionof affected areas was achieved through a time series analysis of the Normalized Difference
Vegetation Index (NDVI). In recent studies, the analysis of vegetation changes detectedfrom abrupt NDVI declines in a short period has been employed for the rapid identificationof landslides [38]. The methodology consists of calculating the difference between the NDVI
from post-event of pre-event. The index is calculated by Equation 1. Then, it is defined as adata-driven threshold. In this study was applied the model proposed by Zhu and Woodcock[39] which measures the difference between observations and model predictions for eachrange/index, and the value found is normalized by three times the Root Mean Squared Error
RMSE (Equation 2).
𝑁𝐼𝑅−𝑅𝑒𝑑

𝐵8 − 𝐵4𝑁𝐷𝑉𝐼 = 𝑁𝐼𝑅+𝑅𝑒𝑑 = 𝐵8 +𝐵4(1)

Where:NIR = light reflected in the near-infrared spectrum
Red = light reflected in the red range of the spectrum
B8 and B4 = Bands from Sentinel SAR-2, 10m resolution
1
𝑘

∑𝑘𝑖=1

̂ (𝑖,𝑥)𝑂𝐿𝑆 ||𝜌(𝑖,𝑥)−𝜌
3𝑥𝑅𝑀𝑆𝐸𝑖

>1
[9]

(2)
XXVIIth World Road CongressWhere:x = Julian datei = the i-th satellite band𝜌(𝑖, 𝑥) = observed value for the i-th satellite Band at Julian date x𝜌̂(𝑖, 𝑥)= predicted value for the i-th satellite Band at Julian date x based on OLS fitting
Finally, landslide areas are defined as those have NDVI values greater than the thresholdand slope value over 10%. Figure 5 illustrates the NDVI images from the study area preand post-event (Landslide, November 14, 2019).
a)Pre-landslideb)

Post-landslidec)Landslide

Figure 5 – NDVI values and Landslide areas
NDVI index ranges from -1.0 to 1.0, representing the variation of photosynthetic activity of
the analyzed area. Negative values are formed mainly by water or snow, and values close
to zero usually indicate bare rocks and soil. Values smaller than 0.1 correspond to bareareas of rocks, sand, or snow. Moderate values (from 0.2 to 0.3) represent shrubs andgrasslands, while high values (from 0.6 to 0.8) indicate a higher density of green vegetation.
[40,41].
For flood detection, 32 Sentinel-1 images were used. The SAR collection is Ground Range
Detected High Resolution (GRDH) products with a resolution of 10 × 10 m. All data wereacquired from the GEE platform in Interferometric Wide Swath (IW) mode in VH polarization.
The image acquisition period ranges from December 1, 2019, to January 31, 2020.
The flooding event occurred on January 2 and 3, 2020, with a total rainfall accumulation of
491.83 mm in 48h in the municipalities of Cariacica, Viana, and Serra [42]. In Viana, theheavy rain flooded four sections of BR-262, near the access to the Marcílio de Noronhaneighborhood. Visual representation of the SAR images is shown in Figure 6. The image
processing steps were described in Section 2.4. The initial threshold calculation is performedby the difference between the images converted into dB after and before the event (Equation
3).
𝑇ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑 = 𝑙𝑜𝑔 (𝑃𝑜𝑠𝑡𝑒𝑣𝑒𝑛𝑡 − 𝑃𝑟𝑒𝑒𝑣𝑒𝑛𝑡 )

[10]

(3)

XXVIIth World Road Congressa) Pre-floodb)

Post-flood

c)

Flood

Figure 6 – Backscatter images pre-and post-event and Flooded areas

In order to increase the probability of estimating a correct threshold, it was decided to split
the study area into 50 polygons and calculate the threshold for each of them. The final
average threshold is the arithmetic mean of the obtained values. However, before delimiting
the flooded areas, it is necessary to define the permanent water bodies such as rivers,
streams, and lakes (water seasonality 10 months of the year). Then, a mask is created todifferentiate the permanent water pixels from the flood pixels. Flooded areas are thosewhere the set of pixels does not belong to the water bodies, has a slope less than or equal
to 5%, and the difference between pre-and post-image is greater than the average threshold.
2.8.

Hazard Assessment

In this study, hazard assessment is performed using Analytic Hierarchy Process (AHP). AHP
is a semi-quantitative method Multi-Criteria Analysis (MCA) approach developed by Saaty
in 1980 [43]. Its methodology consists of, splitting a complex problem into simple criteria,
where the decisions are taken considering the weight of each criterion calculated in apairwise comparison matrix [44]. This matrix is drawn based on the element comparison
scale. The scale ranges from 1 to 9 where 1 is equal to “Equally important values” (Twofactors contribute equally to the objective) and 9 equals extremely important values. On theother hand, the even numbers 2,4,6, and 8 are intermediate values (intermediate preference
between adjacent scales). Each pair within each criterion is assigned a score. This scoreindicates how well option "A" satisfies criterion "B". The scores are then normalized andaveraged. Ten criteria are applied to "judge" the relative importance of one indicator
compared to another. The pairwise comparison tables were filled in for nine risk factors (e.g.
slope, TWI, etc.) in the area of natural disasters. Their results were normalized andexamined with the Consistency Ratio (CR) test represented in the equation below [45]. CR
depends on the Randomness Index (RI) that is shown in Table 2.
𝐶𝐼

1

−𝑁
𝐶𝑅 = 𝑅𝐼 = 𝑅𝐼 ( 𝜆𝑁−1)

(4)

Where:
CI = Consistency Index
RI = Random Index
𝜆 = Major Eigen value
N = Criteria number[11]

XXVIIth World Road CongressTable 2 – Random Index (RI) by Saaty (1980) [44]
N
RI

1
0.0

2
0.0

3
0.58

4
0.9

5
1.12

6
1.24

7
1.32

8
1.41

9
1.45

10
1.49

11
1.51

12
1.48

13
1.56

14
1.57

15
1.58

In HazRoad model, complex problems are defined as flood and landslide events. In addition,
the criteria are defined based on causative factors to hazards. Several researches haveconsidered slope, slope aspect, soil, elevation, land cover and use, drainage, distance fromroads, distance from rivers, Topographic Wetness Index (TWI), and precipitation of theregion as main causative factors of landslides and floods [46].
3. RESULTS AND DISCUSSION
Table 3 shows the summary of the performance metrics for the Natural Language
Processing model.
Table 3 – Performance metrics for NLP models
Supervised Classifier

Accuracy Precision Recall

F1-Score

Logistic Regression

0.97

0.97

0.97

0.97

Support Vector Machine

0.97

0.97

0.97

0.97

Random Forest

0.97

0.96

0.97

0.96

The performance metrics for the HazRoad NLP framework present significant outcomes, allclose to 1.0. Interestingly, these values for the three classifiers used were relatively equal.
To distinguish which model is the best among the possibilities, the feature importance metric
was adopted. Feature importance refers to techniques that assign a score to input features
based on their usefulness in predicting a target variable. Their scores play an important rolein a predictive modeling project, including providing information about the data. The classifierthat presented the best results was the Logistic Regression, which was effective in
identifying the following keywords as the most frequent terms: flood, hillside, landslide,erosion, collapse, road sinking, bridge collapse, and pothole.
Figure 7 provides the results obtained from the analysis of the tweets released by DNIT and
the Federal Highway Police.

Figure 7 – Hazards recorded from tweets from 2009 to 2022

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The graph shows the evolution of the number of disasters in the period between 2009 and2022. From the chart, it is possible to observe a significant increase in events recorded from2018. A possible explanation for this may be the increased accumulation of rainfall in thesouthern, southeastern, and northeastern regions of Brazil. These regions concentrate
93.88% of the cases of highway disasters. Another possible explanation for this is the spreadof the social network Twitter as a means of communication for transportation regulatory
agencies. 20,611 tweets were published between September 2009 and October 2022, of
which 669 were disaster records. Hence, the inventory consists of 303 landslides, 15 rockfalls, 76 erosion, 165 floods, 64 road collapses, 16 bridge collapses, and 30 pothole events.
The three highways which had the highest number of incidences were BR-101, BR-262, andBR-381 with 111, 79, and 62 disaster events recorded, respectively. Among the states, thehighest numbers were recorded in Minas Gerais, with 190 events, Espírito Santo, 114, andParaná, with 103 incidents. Viana and Serra are highlighted among the flood cases,
registering 11 and 8 cases respectively.
The experimental results for HazRoad’s automatic landslide and flood detection arepresented in Table 4.
Table 4 – Performance metrics for DL models
HazardBatch Size

Learning Rate

Filters

Precision Recall

F1-Score

Landslide

160.0005

4

0.963

0.957

0.960

Flood

8

0.0001

4

0.900

0.923

0.911

Overall, both models show a consistently high level of performance. The F1-score evaluation
results for each case demonstrate the applicability of the HazRoad training dataset fordisaster detection results. The F1-score for the test dataset on the models ranges from 91%
to 96%, which is a good result. Moreover, the same was observed in Precision and Recall
models, which range between 90.00%-93.30% and 92.30%-95.70%, respectively. In termsof computational architecture, the landslide detection model proved to need 16 training
examples used for each iteration, while the model trained on flood data proved efficient with
only 8 iterations. This implies that more computational effort was required to identify mass
movements. The results of the hazard prediction by image segmentation are set out in the
figure below. On the left are the black and white satellite images. The white polygons
represent landslide and flood areas respectively. A closer inspection of Figure 8 shows that
despite the high precision values, both models have certain limitations when identifying
small areas.
Landslide
Flood

Figure 8 – Hazards predictions
The results of the hazard assessment analysis are set out in Figure 9. Additionally, theweighting of the individual causal hazard factors obtained by the AHP matrix is shown in
Table 5.
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It can be seen from the maps in the figure below that all sections of the highway in the study
area have high vulnerability to hydro-geological disasters. One of the reasons is thegeomorphology of the terrain, which presents high altitude, between the region between
Viana and Domingos Martins (BR-262) and low altitude mainly in the region of theMunicipality of Serra (km's 294,299,300 and 302 of BR-101), an area that tends to flood.

Figure 9 – Hazards maps
It is observed that the instability on slopes increases as the gradient increases, near the
Serra, do Caparaó (mountain range west of Km 36 of BR-262). Whereas, areas where theslope angles are below 15º are less susceptible to landslides, in the case of the Serra
Municipality. On the other hand, the probability of flooding events rises in the upstream
direction of BR-101, in the stretches between Cariacica and Serra.
Table 5 – Hazard Factor Weighting
HazardTWI

Elev.

Precip.

Slope

LULC

NDVI

Landslide
Flood

0.202
0.217

0.132
0.200

0.069
0.087

0.230
0.081

0.040
0.056

0.051
0.043

Dist.
river
0.047
0.107

Dist.
road
0.093
0.047

Drainage
Density
0.068
0.081

Soil
Type
0.068
0.081

Topography wetness index had the highest impact on the occurrence of floods andlandslides. The results suggest a significant accumulation of water across the study area,
primarily attributed to the presence of clay soil, which is prevalent in the region. In addition,
the hydrological characteristics of the area also contribute to the prevalence of high and very
high flood hazard zones. The sections of the highway closest to the Jacu and Vitória rivers
experience the most severe impacts in this regard. Concerning the landslide, the areas near
the Serra do Caparaó have the higher susceptibility. Because, the region's steep terrain
significantly increases the potential for landslides. Furthermore, the geomorphologicalformation of this area renders it more vulnerable to such events.
4. CONCLUSION
This work contributes to existing knowledge about highway disasters by providing acomprehensive methodology for creating a management system. Web scraping and NLP
techniques allow the collection and organization of data for the creation of an inventory. The
remote sensing mapping step helps in the rapid delineation of affected areas. Through theuse of deep learning image segmentation, new disaster spots can be automatically detected,
as the algorithm recognizes impacted areas due to variations in the pixel pattern of theimage. The hazard assessment identifies the most disaster-prone areas. Finally, all results
are stored in a web portal. It is also interesting to note that the methods used to develop
HazRoad can be applied to other roads in other parts of the world.
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