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Early flood detection and rescue using
bioinformatic devices, internet of things (IOT)
and Android application
Rijwan Khan
ABES Institute of Technology, Ghaziabad, India
Mohammad Shabaz
Department of Computer Science Engineering, Chitkara University, Mohali, India, and
Sarfaraj Hussain, Faraz Ahmad and Pranav Mishra
ABES Institute of Technology, Ghaziabad, India
Abstract
Purpose –The impact of natural disasters on human life, the environment and the flora and fauna can be contained to large extent by intelligent
human intervention. This study introduces the human capabilities which can be extended considerably with technology. Internet of things have
always provided opportunities for predicting and managing manmade/natural disasters. The extreme reason for causing soil erosions, landslides,
cloud bursts, floods, etc., are due to excessive rainfall. However, the flood is one of the most happening natural disasters, following Bihar to be the
most affected region due to floods. Lots of lives and properties were lost and damaged.
Design/methodology/approach –This implemented researchers to introduce an advanced solution for such calamities. Expectations were
developed that it would signalize authority as early as possible so that advanced measures are taken before the effect. The lack of sensing or
alarming technology in India pushed researchers to develop a model using the Android app that basically detected the upcoming flood and other
calamities.
Findings –Most importantly the entire model was programmed with IoT and its techniques so that the response is quicker and more accurate.
Originality/value –This research study is original.
Keywords RFID, Sensors, Flood, Microcontroller, Android application DHT11
Paper type Research paper
1. Introduction
The internet of things (IoT) have impacted a lot of daily lives
based on sufficient connectivity in the form of extended
internet. This technique is mainly associated with certain
proliferating terms such as “ambient intelligent,”“ubiquitous
network”and “cyber-physical system.”It is a fundamental
globalized network connected to the real world through
important microelectronic devices and information and
communication technologies (Gubbi et al.,2013). The system
here is based on a particular standardized protocol with
recognized “things,”attributes and personalities whether
physically or in virtuality, seamlessly integrated into the
information network (Noura et al.,2019). The two
foundational technologies of IoT are radio frequency
identification (RFID) and wireless sensor networks (WSNs)
where the former is used for transmitting data from microchips
to the respective readers and the latter trying to interconnect
with the intelligent sensors. Both the fundamentals, however,
act upon helping the general public with effective tracking
measures, retailing, logistics, monitoring applications for
traffic, health care, industry, environment, etc. (Chen, 2012).
They date back to the 1980s but currently, their significance
have been known to gain rapid attractions among various fields
and industries. IoT has made this possible as is made through
smartphones, smart communications, smart technologies and
even the tiniest but smartest objects. IoT is currently
everywhere (Xu et al., 2014). Natural and unnatural disasters
such as earthquakes, floods, accidents array the ever-display
test for launching crisis administrations (Zahra et al.,2018).
Disaster organization attracts research fields related to
information technology, ecology, health, plus businesses. This
attraction creates a massive gathering useful for product
establishment, examination and pre-imperative organization of
its data (Hristidis et al.,2010). There is a need for integrated
communication and data framework in disaster or crisis
administration so that it can provide secure trading. This
specific requirement is proactively informed by the polices and
fire officials and common guards to various organizations. The
data, henceforth, is reported both upstream and downstream
within the associations to prevent the loss of human lives
The current issue and full text archive of this journal is available on Emerald
Insight at: https://www.emerald.com/insight/1708-5284.htm
World Journal of Engineering
© Emerald Publishing Limited [ISSN 1708-5284]
[DOI 10.1108/WJE-05-2021-0269]
Received 7 May 2021
Revised 14 June 2021
Accepted 15 June 2021
caused due to disaster. Reportedly, the plan also works out for
tracing the reason behind the development of the disaster, and
the accurate statistics of lost lives and property. The important
elements that quickly helped the plan go in control were the
progressive and immediate alert, early acknowledgment, cross-
examination toward issues and degrees, public notification and
fitting masters, mobilization of a response, damage
containment, relief and helpful watch over those impacted
because of natural change, among various causes, disastrous
occasions have extended out and out consistently and that is
not simply costing us to the extent assets/system hurt more over
in dynamic adversities of human lives (Hristidis et al.,2010;
Yang et al., 2013). While no one can stop the occasion of basic
disasters, with the help of present-day advancement one can
extra people’s lives more enough. Exchange systems in the
midst of a damaging occasion can be the complexity among life
and downfall for those in the impacted areas.
There are basically three key strategies for detecting
catastrophes such as floods at an early stage. They are as
follows:
1 Detection of flood and other natural factors such as
humidity, temperature, water level and flow level.
2 To detect the humidity and temperature, the authors are
using a DHT11 digital sensor.
3 To detect the flow of water, the authors are using a water
float sensor.
4 To detect the range, authors are using a sonar sensor.
5 Arduino will take the data and process it and then it will
make a decision if the values are greater than threshold
values. Then send it to the server through a wireless
network.
6 Then the user will simply log in to the portal (application)
and can see the updated information.
2. Role of the internet of things in flood detection
There are various implications of the IoT in the investigation
and critical present-day gatherings. The idea of IoT is
primitively derived from the words “Web”(a vision for the use
of the internet) and “things”(A vision inciting the arrangement
of things. Now, the two words when fixed together semantically
defines the arrangement of interdepending variants addressable
to standard corresponding traditions. IoT was introduced long
back during the 1980s for improving edged remote
advancements such as RFID, near field communication,
machine-to-machine communication, vehicular-to-vehicular
communication, sensors, actuators and smartphones, are
carefully presented in business parts for executing front line
thought of IoT (Yang et al.,2013;Perumal et al., 2015). Over
the span of regular framework breakdown, disk-to-disk
correspondence began to confine an uncommonly selected
framework where a segment of the devices will go about as a
hand-off or portal administrator. This hand-off administrator
will interface the impacted domain with the rest of the world at
whatever point they get any live advances, for instance, Wi-Fi,
Satellite or working standard cell coordinate. Using IoT, the
work is arranged in different devices to make sure whether the
device is conferring to another device for conducting quickly
and objectively among the corresponding frameworks. Here,
IoT is an assorted framework for distinctively sorting out the
related devices. Because of the low/affordable price of the
various sensors, a high accuracy rate of the data is collected and
moderately high reliability of hardware is used. IoT
technologies are of great demand specifically during disaster
management programs.
The operation starts from the data collection in real-time from
the various sensors such as the ultrasonic water flow/level sensors,
temperature/humidity sensor and rain gauges. All the data is
passed to a microcontroller. In the back end, IoT performs data
analysis of the real-time data using various software components
either using cloud/fog or edge computing. Various data mining
algorithms and artificial intelligence/machine learning (ML)
techniques help in predicting the possibility of future risks
because of floods by comparing them to the pre-programmed
values. This information can be broadcasted through social
media, other broadcast forms and sending warning messages
such as the short message service (SMS) thereby coordinating
evacuation and relief efforts. Thus, IoT technologies are used in
monitoring, tracking, sensing, controlling and warning
environmental disasters.
3. Literature review
Flood and earthquake are some of the best-known catastrophic
reasons behind the humongous mortality rate of both humans
and fauna. The inclination in the substantial death rate causes
disruption in a nation’s economy. Therefore, it is a necessity
these days for the authoritarians such as the government to
possess an IoT-based accurate flood and earthquake detection
system. The system was proposed by Babu, Venita, et al. to
detect these real-time disasters and inspect the post-affected
areas. Their research paper has highlighted the flood and
earthquake observatory system that acts as a proactive
emergency alert system and produces accurate results within
less cost and time period (Babu and Rajan, 2019).
Satria, Dedi, et al. proposed a system and based their
experimental setup and results according to the real-time flood
detecting system. The project is likely to be Web-based as it
displays accurate data on the height of the water, rain and flood
using ultrasonic oriented sensors such as Arduino Uno, wireless
routers and ethernet shields, for transferring various data to the
hands of the common public via the internet (Satria et al.,
2018). New technologies such as IoT make use of data
analytical tools and artificial intelligence for detecting any
upcoming disaster and informing authorities in advance. Such
technologies have helped human life a lot in saving not only
lives but also buildings, monuments and various types of
infrastructures, finding a lost victim and completing rescue
operations successfully. According to Ray, Partha, et al. IoT is
the best solution for taking part during disaster management
and rescue operating systems because it studies a particular
mishap to the core by understanding past contributions. This
gives a futuristic approach for solving even the extreme leveled
issues faced during disaster management systems (Ray et al.,
2017). Following this competence, the benchmark of IoT data
provides enough time for the authoritarians to plan and rescue
enough lives beforehand. However, it is still unsure whether
these data are still safe within the cloud platforms or not
(Rauniyar et al., 2016). Flood forecasting is that kind of
hydrological process that is modeled using ML besides IoT.
Early flood detection and rescue
Rijwan Khan et al.
World Journal of Engineering
IoT sensors find their requirements usually during cases related
to natural environment and disasters until ML once predicted
the flood inundation depth of the Erren River Basin in South
Taiwan. The study later found how effective ML is in
forecasting floods and other natural disasters at a very advanced
stage. It also clarifies that IoT does possess the capability to
handle every forecasting situation but with the help of ML, the
data seems to generate more accuracy within less time period.
This model, therefore, is noted as an IoT-based ML model. It
reliably aims to produce accurate results for various other
complex regional flood inundation forecasts. It has many
substantial advantages such as online assessment and
adjustment, consistent accuracies and numerical evaluations
(Yang and Chang, 2020). Technologies such as RFID, sensing
and intelligent networks and objects, also work in collaboration
with IoTs producing many distinctive definitions according to
each study or academic community (Yang et al.,2010). IoT
also works as a complicated cyber-physical system and lends a
helping hand during communication, identification process,
industry-based automated monitoring systems including
controlling, managing and maintenance. The sensors and
actuators are vividly replaced with IoT devices in the
manufacturing and infrastructure-based industries because of
their expensiveness. For instance, food industries recently
replaced their initial tracking devices with WSN and RFID-
based IoT techniques for improving their supply chain and
food quality. This way, the industries were able to focus more
on their developing challenges and future opportunities (Puthal
et al., 2016). Flood detection systems use IoTs in real-time for
evacuating people from the spot and minimizing the dramatic
impacts caused due to this disaster. All these purposes are very
well-acknowledged with the fact that time is the key factor here.
This is why the use of these technologies helps in the advanced
detection and reporting of accurate data to the research centers
so that its domain, reason and impact are identified and
examined greatly to prevent any further side-effects. The data is
quickly collected, filtered, processed and analyzed for
establishing readymade facilities for the upcoming flood event.
Medical camps and emergency shelters are kept ready for the
location that may be affected by the event, in case that region
lacks initial preventive measures such as anti-flood
infrastructures. In such conditions, the only possibility is to
evacuate as many lives as possible instead of thinking about
their property that will soon be damaged by the flood (Van
Ackere et al., 2019). Another system that is supportive during
such disastrous events is the geological disaster monitoring and
warning and post-disaster rescue system. Its main objective is to
solve data acquisitions during real-time, manage the quick
basic supplies for the relief camp and optimize the
infrastructure for that particular location. This geological
rescue system is based on IoT, cloud computing and mega
data, which is known to prepare a successful decision-making
model by sensing and integrating the RFID technology. This,
henceforth, makes the system more feasible and reliable in
providing enough support for reducing the harsh effects of the
disaster (Qin et al., 2018). IoT provides pervasive connectivity
for everything (called objects) around us and where each object
was uniquely identifiable from anywhere and at any time. It was
expected that about 50 billion objects will be connected by
2020. IoT is expected to create an impactful disruption in
many, if not all, aspects of daily lifestyles. Some examples of
applications where IoT could be used for operational
efficiencies and societal benefits were presented. The
applications discussed briefly in their paper include smart
health care, smart transportation, smart environment, smart
energy use, smart agriculture and smart cities. Based on the
broad nature of IoT applications, the field is interdisciplinary in
nature and will certainly bring individuals from multiple
disciplines to work together. Their paper has also discussed the
opportunities that IoT provides and the challenges it poses.
The opportunities that IoT creates are numerous. The business
was eager to participate for their societal benefits. There are
several challenges that IoT deployment poses. These
challenges include management of a massive amount of
information that IoT objects will generate, security/privacy of
information and IoT users, reliability/resilience of IoT’s
operational environment, energy sources and level of users’
comfort. Historically, in the deployment of many technologies,
there have been many challenges and concerns. However, with
the passage of time, the societal benefits outweigh the concerns
and society becomes comfortable in using the technologies for
its good (Ilyas, 2019).
The data dissemination is primarily the most vital deployment of
IoT whenever the disaster is about to happen, occurring or is
surpassed. This deployment is a form of arrangement during such
an event useful for providing people with relevant data about the
event. During the research, a pilot project was designed using a
micro-model for measuring the water body level such as of lakes and
rivers, during the forecast. Here, a water container was used
throughout the experiment resembling the natural water bodies.
The measuring sensor attached to the model is nothing but a simple
open circuit called Netuino Plus 2, that automatically suspended
whenever there was an immediate water contact. Notably, the test
was performed within a strict and secluded environment. The
circuit board was programmed and the resistance to electricity
attached to the water container was at a particular height. Now as
the water level rose and drew up to the resistor, the circuit
suspended or turned off or closed immediately on th e first touch.
Data of the water level immediately and automatically was
transmitted into a computer device through Wi-Fi
connectivity. Once the data was entered on the computer,
the same data was seen over the smartphones anywhere
possible. The experiment, thus, is a micro-model that
provided sufficient impressing results beyond convention
(Hern
andez-Nolasco et al., 2016).
Beyond this experiment, other successful experiments
included the unmanned aerial vehicle (UAV)-based IoT
services and their potential benefits. They were based upon
several key challenges and requirements such as concerns
related to public safety, physical computer architecture,
techniques for detecting obstacles, data collection, wireless
communication such as cellular (3G, 4G, Long-Term
Evolution), Wi-Maximum and Wi-Fi, subjugated issues related
to limited energy resources and providing strict controlling and
commanding actions during the course of emergency situations
such as upcoming flood event or any other disaster. Some of the
well-known IoT appliances are sensors, cameras and RFID and
they try to study and collect necessary data about the situation
using UAVs. IoT does have the potentiality to handle any
emergency situation all alone, but when this technology allows
Early flood detection and rescue
Rijwan Khan et al.
World Journal of Engineering
other perspectives to work for the ground, the target is fulfilled
even with limited energy resources and time period. The overall
target is to make use of time and energy as less and quickly as
possible so that the situation is under-controlled saving millions
of lives and infrastructure. Based on the results, it is found that
natural disasters can be supervised only if they are managed
correctly. Finally, the criteria for disaster management were
provided in detail so as to help future disaster management
planning and studies (Motlagh et al.,2016). A cooperative
multilateral sensing scheme was advocated by certain authors
who seemed to explain how effective it was for tracking an
industrial circumference (Zahra et al.,2018). Even here, the
use of IoT was not outdated. It once again anticipated the
possible major accident or disaster and in advance helped
researchers by presenting open data about the incoming calls to
the smart city 112 public safety system in several regions and
countries. This open data was extracted using the classified
variants, models, methods and data simulation. The foremost
target was to classify the incoming calls (Liu and Wang, 2017).
Geospatial analysis is performed on different types of IoT
devices and their uses. The beneficiaries included the status
deployment of the IoT devices, data transmitting standards and
employments and measurement reliabilities. These subjects are
thoroughly reviewed with 26 reliable initiatives of IoT (Refer to
Liu and Wang, 2017; Khan et al.,2019) The IoT has been
geospatially analyzed two different perspectives –vertical and
horizontal, that is known for deeply understanding IoT, it’s
model and visualization based upon natural and man-made
ecosystems. The rapid development of IoT these days have
installed many architectural models, networks,
communications and technologies applied to subjugated
privacy policies and technical obstacles. The vertical perception
consisted of three different layers, namely, the sensing, the
networking and the application layer. The horizontal
perception, on the other hand, consisted of separate domain
variants in the curriculum. It is quietly a challenging task and
the subject requires more practical study (Kamilaris and
Ostermann, 2018). IoT is not only useful for detecting early
floods and other disasters but also helpful in finding out the
reasons behind sudden climatical changes and their impact
upon the agricultural sector. It is fundamental that agricultural
production depends upon the regular rise in population.
However, when sudden climatical changes such as excessive
rainfall causing floods or lack of rainfall causing droughts and
man-made reasons for causing various types of pollutions and
greenhouse gas emissions, there is a direct or indirect drastic
effect upon the agricultural production. Plants do get affected
by such conditions, and therefore, fail to produce in mass. With
the help of IoT, this is how a concrete review is produced on
agricultural problems and their solutions via early climate
forecasts (Ragnoli et al.,2020). Big data analytics (BDA) and
IoT technologies when work together produce heterogeneous
data and resources for actively managing the upcoming or
caused disaster. This approach is by far one of the quickest
methods because it is sensible in drawing and implementing
decisions and demanding models for the event. Though there
still resides a huge research gap between this collaboration, yet
it manages to guide the event with a proposed conceptual
architecture. The disaster-resilient smart city concept
abbreviated from the term disaster-resilient smart city goals to
offer the strengths of both these technologies for anticipating,
evacuating, monitoring, early warning to the public about the
catastrophic events with the help of various data sources called
UAV, remote sensing, online news media houses and CCTV
cameras (Shah et al., 2019b;Hadi et al., 2020;Grasic et al.,
2018). IoTs have occupied every space, area and field of
expertise. Undoubtedly, they help majorly in maximizing the
rate of production, liability and monitors but they do have
many major drawbacks as well. They fail to yield employment
because of their replacement to the labor force in every sector.
As IoT is customarily beneficial in every sector, industrialists
forego adapting more IoT-based technologies as possible.
These advanced technologies, henceforth, are stealing away the
jobs of humans who had been working to earn some money for
feeding their families. The other major drawback is the security
concern that tends to circulate around IoTs all the time. With
the lack of security on privacies, digital technology such as IoT
is not always trustworthy. These are some of the detailed IoT-
based critical perceptions that are critically found in many
studies. Unless this, the technology of IoT is far more futuristic
and provides the best practices and necessary countermeasures
making people potential-ready for the attacks of critical events
(Hadi et al., 2020;Grasic et al., 2018;Kamilaris and
Ostermann, 2018;Zheng et al., 2011). The security of IoT is
critically based on infrastructures. Specifically, there is a
considerable range of vulnerabilities that adversaries could
exploit to intrude in critical systems. Three broadly assessable
IoT smart-systematized applications are smart transportations,
smart manufacturing and the smart grids from the perspective
of a three-tiered architecture of service layer, operation layer
and management layer. Each application and its layer is
provided with a variety of attacks and examples that go reliably
with each case. In a case study, the smart transportation system
was used as an example, that considered the impacts of attacks
of varying strengths and types, and demonstrated the efficacy of
least effort attacks in crippling complex IoT systems. In the
course of action, the security risking management outlined
some developing countermeasures as a notable theorem and
designing the integrated evaluating platforms for impacting the
attacks and the effectiveness of countermeasures (Elmustafa
and Mujtaba, 2019;Nayyar et al.,2020;Perwej et al.,2019;
Narmilan and Niroash, 2020). The IoT-based disaster
management system can predict the flood and alert the nearby
people immediately of the impending danger. This existing
system consists of an alert system which consists of a sound
buzzer and human monitoring at the dam wall to alert the
respective people of the flood through various media. As this
involves huge manual labor, the existing system is prone to
error and involves very high execution time. This system makes
use of IoT technology, an ultrasonic sensor, a pressure sensing
detector for calculating water overflow and the rising speed at
the dam walls. Not only this, even the Global System for
Mobile Communication (GSM) module is used for sending
SMS to the nearby people about the impending danger. This is
how the victims of the event get alerted before time saving
themselves from the danger. The advantage of this project is
that it takes less time to both detect the flood and alert the
people. As time is a crucial factor in the natural disaster alerting
technique, this project proves to be a great solution for the
cause. In addition, this project involves minimum human
Early flood detection and rescue
Rijwan Khan et al.
World Journal of Engineering
intervention so the chances of human error are reduced to a
huge margin. There is no way one can typically prevent a soon-
approaching flood but with the use of technology, the damage
can be reduced to an extensive level. Thus, this system not only
reduces the time taken for detection and alert but also deprives
the system of the human errors that may occur under the
current manual monitoring and alert system. In addition, this
system also contains an SOS button to warn the people of any
other danger. Speed and accuracy are the major requirements
of any natural disaster monitoring system. This system makes
use of sensors and an automated GSM module to detect the
flood and warn the people about the same. A personal alert is
another advantage of this system as in the current system there
are chances that the alert may not reach a particular person.
The impact of natural disasters on human life, the
environment and the flora and fauna can be contained to large
extent by intelligent human intervention. Human capabilities
can be extended considerably with technology. IoTs provide
opportunities in the prediction and management of manmade/
natural disasters. Heavy rains lead to soil erosion, phenomenon
such as water piping, filling up of dams beyond limits, landslides
and cloud bursts. There is a complex matrix of events which
renders itself much complicated for natural human assessment
models and analysis. Hence, IoT. IoT capability is enhanced
through artificial neural network, Fuzzy, ML, general assembly,
etc., for making proactive decisions and early warning systems
and the like.Floods duringthe consecutive years 2018and 2019
rocked the southern state of Kerala in India creating havoc in
the form of landslides and mudslides and causing deaths of
hundreds of people and displacing thousands. Computing
technology particularly IoTs were used for the management of
floods in the form of forecasting, monitoring, detection and
prediction. Data from various sensors combined with cloud/fog
computing using WSNs were found to be effective for the same.
The monitoring water system is one of the most invulnerable
practices conducted by IoT-based technologies and devices.
Water demand and supply increases according to the rise in
population. Therefore, at that particular rate, water must be
conserved at various resources such as rivers, ponds and lakes.
This conservation process is put to the stage by installing several
monitoring surveillance near the spot so that there is no attempt
to contaminate the resources in any way. These surveillances are
wireless and cloud computed designed for acquiring data in
real-time. All the IoT-based activities involved in this process is
Figure 1 Flowchart of the proposed model
Early flood detection and rescue
Rijwan Khan et al.
World Journal of Engineering
because of the use of algorithms establishing it at a very cheap
cost. It is a user-friendly tool and is an excellent competitor for
efficiently managing water bodies and resources and solving
issues generated from them (Kamaruidzaman and Rahmat,
2020). IoT technology has given rise to many smart
deployments such as planned city, advanced health research
sector, modernized transportations, increased rate in mass
production and digital management for the supply chain,
various energy resources such as solar panels and eco-friendly
batteries, well-planned infrastructures and homes. IoT-based
smart cities are getting more attention for advanced research
works and industrial communities. However, the emergency
response system in the smart city is still in its infancy. Saving
people’s lives, performing rescue and relief operations,
safeguarding infrastructure against natural and counterfeited
calamities are some of the most important tasks performed in a
smart city. International Engineering Task Force assimilates a
routing protocol for low power and lossy networks (RPL) for the
urban environment. RPL uses objective functions such as
Embedded Technology eXtended and hop count for efficient
use of network resources. Efficiency and performance can be
improved by the multi-sink model of RPL. Multi sink RPL for
emergency response in smart cities was evaluated by multi-sink
RPL for energy conception, packet delivery ratio, trafficcontrol
overhead and node participation. The network topology in
Cooja with Contiki OS was simulated with results showing
improved performance of the multi-sink RPLK as compared to
the single sink model. IoT, these days is transpiring rapidly in
the technical and communicational sector. It has great potential
to transform traditional cities into smart cities. Research
communities around the world are engaged in the deployment
of many smart city services and emergency response is the need
of the hour. Traditional methods of responding to emergency
response in cities lack coordination, communication divide, ill-
preparedness, slow response and chaotic restoration. IoT
technology and advanced communication system can play a
great role in making a smart emergency response in quick
response, well prepared, digitally connected and safe to save
citizens and city infrastructure. In such a scenario, multi-link
RPL can act as a backbone on which an emergency response
system can be designed. Simulation results suggest improved
packet delivery ratio, power consumption, control traffic
overhead and node participation. The multi-sink RPL for
emergency response system for Smart City IoT can be
improved further by creating a unique objection function to
meet the needs of emergency response. As discussed earlier, the
approaching results derived from the collaboration of BDA and
IoT give strong evidence for helping the disaster management
sector. The results derived from the amalgamation are
analytically accurate for harvesting valuable data from various
Figure 5 Arduino
Figure 4 Sonar sensor
Figure 3 Water float sensor
Figure 2 DHT11 sensor
Early flood detection and rescue
Rijwan Khan et al.
World Journal of Engineering
resources so that a concrete decision is made in real-time.
Through this paper, a thematic taxonomy is presented that has
attributed the amalgamation mainly during the disaster
management ecosystems. Certain dedicated layers and
deployments such as generating accurate data, on-time
harvests, wireless communications, managing the entire process
have been put forward using authoritative analytics, algorithms,
decisions and prompting applications. At last, the paper is
concluded with a dead-end survey on how the functionalities
and fundamentals of IoT-based technologies and applications
have possibly enrolled a better framework in saving the ecology
from exotic calamities and disasters (Shah et al., 2019a). Among
the numerous natural disasters, floods are an overflow of water
that can destroy many lives and society. It occurs mainly
because of heavy rainfall and dam overflow. It affects in a high
manner people’s lives and area; hence, the government would
end up spending more resources to recover the affected region.
Therefore, it becomes crucial to develop a flood detection
system that spreads awareness to society about the rise in water
levels to potential flooded regions. The system is designed with
the help of raspberry pi and different sensors that play an
important role in gathering timely information from the water
sensor unit. The Way2SMS Application Programming
Interface is used for sending an alert message to the people that
also helps in delivering messages in the case of no internet
connectivity. A calling feature is also developed that notify
people in case of a flood emergency at night. The project targets
to sketch a particular type of system that can measure the rise in
the water level and take water flow readings with the help of
water level sensor and water flow sensor and then effectively
spread the information to the society such preventive measures
and precautions will be put forth prior to the damage. Thus, the
efficiency of the system helps in preventing materialistic and
human loss by sending early alert information. Early flood
detection was put to action using Raspberry pi by collecting data
with the help of water level and flow sensor. It also enacted in
enabling motor and sending alert messages. This detection
system has datalogs in its cloud server for monitoring floods and
Figure 6 Workflow of model
Early flood detection and rescue
Rijwan Khan et al.
World Journal of Engineering
reliably sending alert messages to the people of the nearby
registered area. The basic advantage of the system is about
monitoring the flood with timely-sent SMS notifications in the
non-internet zone and an effective call alerting feature in case of
night-time flood emergency when the majority of people are
asleep. The drawback of the system lies behind the weather
conditions and how that can affect the sensors. Because
Raspberry pi is called a mini-computer because of its several
notable features. The future enhancements of this project can
comprise taking pictures of the situations under different weather
conditions and further applying ML techniques for flood
predictions. Deep learning-based service composition (DLSC)
framework was introduced for suggesting qualified quality of
service (QoS)-centered cloud services. The multi-layered
attributes of QoS stand as a composition of both long short-term
memory and particle swarm optimization (PSO) for solving
flexible obstacles and complications. Both of them are based on
deep learning firms with PSO algorithms willing to execute notable
results from those same cloud services. The respective study is
transparent and visionary to meet the expected results that can use
the future studies in optimizing the compositional frameworks in
less time using several collateral data processing platforms
(Haytamy and Omara, 2020). After DLSC, the next focus goes
toward Gentry’s Payload Operations Contro l Cent er s cheme
that externalizes the homomorphic ciphertexts outsourced
by several data contributors. The scheme stored and
processedinanevaluatorcalledSandtrainedadata
encrypted model in crypto service provider (CSP). The
evaluator S and CSP together interacted to produced ethical
predicting services for future clients where the aim was to
minimize the cost effectivities for both communication and
computations (Li et al., 2018). Applications security is
important (Soni and Singh, 2021;Gomathi et al., 2021).
4. Proposed system
In this proposed system, information is globalized to everyone
living in the area with predictable flooding disasters. This way,
the updates can be seen appropriately and soon the evacuation
process shall follow to other safe places. The process of the
proposed system is shown in Figure 1.
5. Hardware used for a proposed system
5.1 DHT11
It’s a digital low-cost, temperature humidity sensor which
senses these two properties through air or water, converts these
values into digital voltage and spits out the digital pin of the
microcontroller (Figure 2).
5.2 Water float sensor
The water level is measured by the water float sensor as in
Figure 3. It is generally at the rest position and works as an
Figure 7 Login page
Figure 8 Register page
Figure 9 Menu page
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Rijwan Khan et al.
World Journal of Engineering
opening and closing circuit. Whenever the water level drops
under the threshold point (means circuit is complete) the
electricity will pass to the alarm circuit and then to the rotor and
a hall effect sensor within the existing plastic body, hence,
forcing the water to run through the same rotor. The speed will
be seen fluctuating along with distinctive flow rates.
5.3 Ultrasonic range sensor
As shown in Figure 4, It has two drums one for emitting the
sound waves (transmitter) and the other for waiting to receive
their reflection from the object known as the receiver.
1. It works on the principle of sonar. Its range can be
calculated as [...]
Range = tv(1)
t= time taken by the waves from a transmitter to an object.
v= the velocity of sound which is 343 m/s.
2. HC-SR04 has three pins, one for the voltage common
collector, one for the output (OUT) and one for the ground
voltage level. OUT pin is connected to the Arduino to receive
the data in digital form.
5.4 Microcontroller/Arduino
The given experiment makes use of the Arduino kit which is a
development suite in which tmega328p is inbuilt as shown in
Figure 5. It contains digital pins for digital I/O and analog pins
for analog I/O. It will send the values to the server. To test the
experiment, some users are registered on the mobile
applications so that they will be able to see the safe places
nearby them all with the help of Google maps. Once the
experiment goes successful, all the registered persons can easily
access the data after logging in to that same application.
6. Proposed system architecture
The architecture of the proposed system is divided into three
parts/modules as follows.
1 Hardware module.
2 Database module.
3 Mobile application.
With the help of these three modules, architecture is defined
which is shown in Figure 6. This shows how all the parts are
connected together to show the system.
The working model shown in Figure 6 has three parts:
mobile application, database and hardware part. The hardware
part has sensors and microprocessors. All these parts are
connected to the central server.
7. Experimental setup
7.1 Hardware implementation
The hardware was implemented into the module because it was
found more accessible in connecting all the sensors to the
Arduino and then to the Wi-Fi module. This Arduino will
further send the required data to the server through Wi-Fi. This
way, the data will be accessible to every registered user on the
app.
7.2 Software implementation
Here a mobile app is developed for this system as shown in
Figure 7. Screenshots of the application is shown in Figure 7.
This is the login page. When the user will click on the
application icon, this will appear and the user can log in with
the registered mobile number. If the user is not registered on
the app, then he/she will be able to register by clicking on the
register button (Figure 8).
The registration page is given in Figure 7 only for the user. If
the user does not have an account, then by clicking on the
register button, the user can create an account.
The menu page as shown in Figure 9 which gives the
available services to the user. Users can choose whatever he/she
wants. The safe places button will show the safe location near
the user where the user can migrate.
Figure 10 Status page on the app
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Rijwan Khan et al.
World Journal of Engineering
The status window in Figure 10 which will show the current
status of all the natural factors. This will keep updating after the
delay provided and the sensor will keep maximizing the
system’sefficiency. The best thing is that everyone can access
from anywhere wherever the internet connectivity is available.
This will also help the government, farmers and of course
normal public. This collected data is very important to perform
ML tasks. If systems are made smarter using this real-time data,
a ML model will finally be implemented.
The google map in Figure 11 which will show the safe places
where users can migrate. This is the best feature of this app.
This will contribute a lot in rescuing because the user is
independent of any government authorized community.
The helpline numbers are shown on another page of the
application as shown in Figure 12. If an emergency happens
then the user can informthe authority.
When this system is compared to existing systems in
reference (Hadi et al., 2020; Grasic et al., 2018;
Kamaruidzaman and Rahmat, 2020), there is no Android-
based system all are central database system but the system
developed here is a central database and Android-based.
8. Conclusion
In India, there are many flood-dominating areas such as Bihar,
Kerala and Chennai. Recently, due to floods in Bihar, Odisha,
Assam and Kerala, many human lives were lost including
damage to mass properties. These damages could have been
prevented if there would have been effective monitoring and
rescuing system. It would have also helped both the
governments and people in receiving early messages of
approaching floods in the rivers and evacuating to safer places.
This is why this paper has presented a globalized calamity
detecting system in improving more livelihood and the
properties that reflect the growth of the country’s development
and economy. This is to be made sure that this system is
installed as quickly as possible and reach more corners before
Figure 12 Helpline
Figure 11 Google map
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Rijwan Khan et al.
World Journal of Engineering
any massive complication. In future work, more sensors can be
added with powerfuliOS-based applications.
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Corresponding author
Mohammad Shabaz can be contacted at: bhatsab4@gmail.
com
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