=Paper= {{Paper |id=Vol-2683/paper1 |storemode=property |title=Integrated Information System for Regional Flood Monitoring Using Internet of Things |pdfUrl=https://ceur-ws.org/Vol-2683/paper1.pdf |volume=Vol-2683 |authors=Svitlana Kuznichenko,Iryna Buchynska,Ludmila Kovalenko,Tetiana Tereshchenko }} ==Integrated Information System for Regional Flood Monitoring Using Internet of Things== https://ceur-ws.org/Vol-2683/paper1.pdf

Integrated Information System for Regional Flood
Monitoring Using Internet of Things

Svitlana Kuznichenko Iryna Buchynska Ludmila Kovalenko Tetiana Tereshchenko
dept.of Information dept.of Information dept.of Information dept.of Information
Technologies Technologies Technologies Technologies
Odessa State Environmental Odessa State Environmental Odessa State Environmental Odessa State Environmental
University University University University
Odessa, Ukraine Odessa, Ukraine Odessa, Ukraine Odessa, Ukraine
skuznichenko@gmail.com buchinskayaira @gmail.com l.b.kovalenko@ukr.net tereshchenko.odessa
ORCID: 0000-0001-7982- ORCID: 0000-0002-0393- ORCID: 0000-0002-5920- @gmail.com
1298 2781 1697 ORCID: 0000-0001-7691-
6996

Abstract—In the work the methodology of creation of the Possibilities for creating information systems of this class
integrated information system (ІІS) for regional flood are growing every year and are conditioned on the one hand
monitoring is proposed, which is based on a combination of by increasing the spatial and temporal capacity of the
technologies of Internet of Things (IoT) and geographic measuring equipment, the accuracy and detail of the
information systems (GIS). It has been shown that the recorded values, on the other hand by improving the sensors;
effectiveness of flood forecasting and decision support for their Radio Frequency Identification Technology (RFID) designed
caution, prevention and mitigation can be greatly improved for control elements identification by marking chips, not
through the use of the IIS, which provides input, processing, expensive CPUs suitable for mobile calculation by Internet
analysis and visualization of data from various sources of
means (large amount of censor-provide data analysis);
information. Important role in the structure of the IIS is the
analysis of data, based on the combination of GIS and Multiple-
Wireless Sensor Networks (WSNs) enabling the creation of
criteria decision analysis (MCDA). It is shown that the inclusion distributed, self-organizing sensor networks and devices that
of MCDA in GIS improves the intelligence of the system and communicate with the radio channel independently; energy-
improves the processing of spatial data. The proposed IIS efficient data transfer technologies (such as Bluetooth Low
prototype and the results of this study can be used for regional Energy (BLE), Near Field Communication (NFC),
management of territories and water resources. telecommunication technology.
The development of IoT technologies has led to an
Keywords—flood monitoring, integrated information system,
Internet of Things, geographic information systems; multiple-
increase in data volumes that are difficult to process using
criteria decision analysis DBMS data management tools and traditional data
processing applications. Therefore, it is important to predict
the storage of big data in data warehouses or cloud-based
I. INTRODUCTION technologies.
Recently, geographic information systems are
Analysis of recent publications shows that there are many
increasingly used in the simulation of various natural
works in which authors analyze the application of IoT in
processes and phenomena: floods, droughts, snowfalls, forest
urban planning and building smart cities [5, 6], in home
fires, etc. [1–3]. One of the most dangerous natural disasters
automation [7], for environmental monitoring [8], in water
is flood, the negative effects of which are observed on
management [9, 10], etc. In addition, individual technologies
average 27% of the territory of Ukraine. Reliable monitoring
that are widely used in resource management and the
and forecasting of floods are very important for supporting
environment are components of the IoT (RS, GPS, GIS,). So,
decision-making on cautioning, preventing and mitigating
the work [11], presents an integrated approach to water
the effects of disasters by the relevant administrative
resource management based on geoinformatics, Enterprise
authorities.
Information Systems (EIS), and cloud services. Over the past
In this regard, it is very relevant to create a GIS-based few decades, a large number of studies have been conducted
integrated real-time information system for regional to assess the risk of flooding based on the combination of GIS
monitoring and flood forecasting. Such a system typically and MCDA [12]. In this paper, the creation of an integrated
integrates a wireless sensor network for collecting flood monitoring information system is based on IoT for
meteorological and hydrological data in an interactive mode, collecting and inputting data and GIS and MCDA for data
that is, being built on the Internet of Things (IoT) [4]. analysis and visualization. Such an approach allows to

Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)
construct a hazard map and a vulnerability map with certain II. MATERIALS AND METHODS
areas of different probabilities of their occurrence. Based on
the appropriate maps, a decision can be made on flood risk A. Common framework of regional flood monitoring system
management. based on IoT
Common framework of regional flood monitoring system
based on ІоТ is shown in Fig. 1.

Fig.1. Common framework of regional flood monitoring system based on IoT

For real-time environmental data collection a wireless Flood risk map can be obtained by using the GIS-MCDA
sensor network, which consists of separate sensors with spatial model [14], which includes the following methods:
autonomous power supplies, is used. Sensory node is the boolean overlays, weighted linear combination (WLC),
node of the core network, which is responsible for data analytic hierarchy process (AHP), ordered weighted average
collection. Each sensor automatically searches for the data (OWA), etc.
receiver at the appropriate network address. Each sensor
network has a communication server to connect the sensor The methodology based on the GIS-MCDA spatial model
network to an external network (Fig. 2). consists of the following steps:
1) Determination of the main purpose and hierarchical
structure of the model.
2) Determination of the criteria influencing the flood.
3) Data collection and construction of spatial database
criteria.
4) Model GIS-MCDA
a) Fuzzy standardization of criteria.
b) Creating pair-wise comparison matrix and the
calculation of the normalized weight of the criteria (AHP).
c) Aggregation results (WLC).
Fig. 2. Wireless sensor network structure
d) Checking the results.
5) Model GIS-visualization of final decision and
Through the gateway, information can be transferred to recommendations.
the monitoring center via the Internet (Ethernet, Wi-Fi,
3G/GPRS). For real-time data collection, remote sensing C. Selection of criteria
means [13] (namely satellites, balloons, airplanes and radar), Flood risk map is usually based on integrated hazard and
mobile devices (that is GPS, 2G, 3G, 4G and LTE), IEEE vulnerability maps, so the criteria may differ for certain maps.
802.X (namely WiFi, Bluetooth and ZigBee), RFID and other
The hazard map is a zoning for the degree of flood hazard.
sensors.
The choice of criteria for constructing this map is usually
based on expert assessments and field studies of a specific
B. GIS multi-criteria methodology for hazard zones’ area. Usually, the following criteria are used to assess the
mapping flood exposure: elevation, slope, distance from water
The monitoring data enters the geospatial repository and surfaces, rainfall, soil moisture (or groundwater level), soil
can be used in spatial modeling and GIS analysis using type. The set of criteria may be partially changed for different
special GIS platform libraries (ArcGIS, QGIS, MapInfo). territories.
Vulnerability is exposure to hazards. Each hazard type
identifies different groups of risk-sensitive elements,
therefore it is customary to build separate maps of
vulnerabilities of the population, agriculture, transport
infrastructure objects, etc. Therefore, for the construction of
appropriate maps it is necessary to have maps of village
density, roads, population density, land use, etc.
Each criteria that is taken into account when constructing
a flood hazard map is presented in the form of a raster layer
with a raster cell of the same size and is stored in the spatial
geodatabase. The layers of the spatial distribution of rainfall
and soil moisture can be obtained by interpolating reference
points that contain values derived from the wireless sensor
network. Other layers can be obtained using the spatial
modeling tools of a particular GIS package based on data
from different sources of information, such as satellite
images.
Thus, the hierarchical structure of the flood risk
assessment model will look like in Fig. 3.
Fig 4. Fuzzy standardization Slope layer

D. Standardization of criteria
All sets of data should be standardized in units that can be E. Criteria weighing (AHP)
compared. Frequently, fuzzy logic is used to standardize the For calculation of the normalized weight of the criteria
criteria. Since the source data may have discrete or multicriterial technique AHP (Analytic hierarchy process)
continuous values, discrete and continuous fuzzy [14] is often used, which is based on a pair-wise comparison
standardization methods are used. The membership function of elements at a given hierarchical level with respect to the
is selected by experts in accordance with the physical elements at a higher level. Using the pair-wise comparison
characteristics of the investigated area. To assess the method (PCM), you can compare the criteria with each other
similarity of attributes, a continuous scale is used in the range and calculate their relative importance for the top-level
from 0 to 1, where 0 is the least risky, and 1 is the most risky element (goal). The result is a pair-wise comparison matrix
value of the attribute for the possibility of flooding. Fig. 4 based on the formula (1).
shows an example of fuzzy standardization of the slope
criterion using the linear membership function.
 1 r12  r1 j 
1 / r 1  r2 j 
A =  rij  =  ()
12

 
 
1 / r1 j 1 / r2 j  1 

where rij are numbers that represent the relative importance of
the i-th element in comparison with the j-th in relation to the
goal.
If, according to some criteria, it is possible to obtain
objective quantitative estimates of elements, then the relation
of these estimates is taken as a priority. When evaluating
criteria on the basis of subjective judgments of experts, the 9-
point scale of relative importance Saaty [15] is used.
At the next stage, there are eigenvalues and eigenvector of
Fig. 3. Hierarchy of flood risk assessment the matrix and a vector of local priorities is formed.
To control the consistency of expert assessments, two
related characteristics are introduced - the Consistency Index
(C.I.) and the Consistency Ratio (C.R.):

 max − n
C.I . = , ()
n −1
where n is the number of criteria and λmax is the biggest F. Aggregation of the composite map
eigenvalue. To obtain a composite map in the GIS they most often use
the technique of Weighted Linear Combination (WLC) [14],
C.I .
which is based on the weighted average calculation (4).
C.R. = , ()
R.I .
S =  wi xi ()
where R.I. is the Random Inconsistency index that is
dependent on the sample size. A reasonable level of where S is hazard index, wi is normalized weight of the
consistency in the pair-wise comparisons is assumed if C.R. < criteria i, and xi is fuzzy flood hazard value according to
0.10, while C.R. ≥ 0.10 indicates inconsistent judgments. criterion i.
Thus, the weight of the criteria derived from the AHP is
multiplied by the fuzzy cell of each criterion, and as a result,
the resulting composite flood hazard map is generated.
According to formula (1), an index of flood hazard index Fig 5. Flood Hazard Map
and a vulnerability index can be calculated. A flood risk map
is the result of a combination of these two components (5). The analysis of the results of cartographic modeling has
shown that the area with the greatest danger of flooding is
27% (1757 km2) of the investigated territory. On the other
Srisk = Shazard  Svulnerability () hand, 9.8% (640 km2) do not have a real danger of flooding
(FHI = 1, FHI = 2). The most dangerous central and southern
parts of the region, which are located on the plains along the
III. RESULT AND DISCUSSION riverbeds.
The methodology proposed in this study was used to The simulation results are in good agreement with the
construct a flood hazard map for the southern areas of Odessa maps of flooding of the territory based on historical flood
region, namely for the region including Tarutinskyi, data, which took place in September 2013. These cards have
Artsyzkyi, Tatarbunarskyi and Saratskyi districts. been provided by Odessa Regional Water Resources
The hazard map was presented in the same range of fuzzy Management Agency.
values as the criteria from 0 to 1, and then reclassed to five
classes of the Flood Hazard Index (FHI) from very low (FHI IV. CONCLUSION
= 1) to very high (FHI = 5) . The raster cells with higher
values characterize the territory more risky in terms of The work proposes a methodology for creating an
flooding. The final flood hazard map is presented in Fig. 5. integrated regional flood monitoring information system
based on a combination of Internet of Things and geographic
information systems. IoT technologies are used to collect and
enter of data, GIS and MCDA are used for analysis and data
visualization. This approach allows to construct maps of
hazard and vulnerability of flood, on the basis of which a
flood risk map can be obtained. The proposed IIS prototype
and the results of this study can be used for regional
management of territories and water resources of different
regions with similar geographical characteristics. It should be
noted that the model can be improved through the use of
modern WEB-technologies.

REFERENCES
[1] Tomaszewski B. Geographic information systems (GIS) for disaster
management. CRC Press, 297 (2014) .
[2] S. Kuznichenko, L. Kovalenko, I. Buchynska, Y. Gunchenko,
“Development of a multi-criteria model for making decisions on the
location of solid waste landfills”. Eastern-European Journal of
Enterprise Technologies, 2018. Vol.2, No. 3(92). P. 21–31. DOI:
10.15587/1729-4061.2018.129287
[3] S. Kuznichenko, Yu. Gunchenko, I. Buchynska, “Fuzzy model of
geospatial data processing in multi-criteria suitability analysis”.
Collection of scientific works of the Military Institute of Kyiv National
Taras Shevchenko University, 2018. Vol. 61. Р.90–103.
[4] Dr. V. Bhuvaneswari, Dr. R Porkodi. The Internet of Things (IoT)
Applications and Communication Enabling Technology Standards: An
Overview. International Conference on Intelligent Computing [10] Poonam J, Chavan, Manoj AM IoT Based Water Quality Monitoring.
Application, pp. 324-329 (2014). Int J Modern Trends Eng Res 3(4), рр. 746–750 (2016).
[5] M. Mazhar Rathore, Awais Ahmad, Anand Paul, Seungmin Rho. [11] Shifeng Fang, LidaXu, Huan Pei , Yongqiang Liu, Zhihui Liu,
Urban planning and building smart cities based on the Internet of Yunqiang Zhu, Jianwu Yan and Huifang Zhang. An Integrated
Things using Big Data analytics. Computer Networks 101, pp. 63–80 Approach to Snowmelt Flood Forecasting in Water Resource
(2016). Management. Industrial Informatics, IEEE Transactions, Volume:10
[6] Andrea Z, Nicola B, Angelo C, Lorenzo V, Michele Z. Internet of ,Issue: 1, April 2013, pp. 548 – 558 (2013).
Things for Smart Cities. IEEE Internet Things J 1(1), pp.22–32 (2014). [12] Brito M., Evers M.. Multi-criteria decision-making for flood risk
[7] B. R. Pavithra, D. Iot based monitoring and control system for home management: a survey of the current state of the art. Nat. Hazards
automation, pp. 169 – 173, (2015). Earth Syst. Sci. 16, 1019–1033 (2016).
[8] Murat Dener. Development a New Intelligent System for Monitoring [13] Jensen, J.R.. Remote Sensing of the Environment: An Earth Resource
Environment Information using Wireless Sensor Networks. Perspective, 2nd Edition. Pearson.Prentice-Hall, Inc.: Upper Saddle
International Journal of Intelligent Systems and Applications in River, NJ., 592 pp. (2007), ISBN 0-13-188950-8
Engineering. IJISAE, 5(4), рр. 237-241 (2017). [14] Malczewski J., Rinner C. Multicriteria Decision Analysis in
[9] T. Robles, R. Alcarria, D. Martın, and A. Morales. An Internet of Geographic Information Science. Springer Science+Business Media
Things-based model for smart water management,” in Proc. of the 8th New York, 33 (2015)
International Conference on Advanced Information Networking and [15] T. L. Saaty, The Analytic Hierarchy Process: Planning, Priority
Applications Workshops (WAINA’14), Victoria, Canada. IEEE, pp. Setting, Resource Allocation, Mcgraw-Hill, New York, NY. (1980)
821–826 (2014).