Big Data Analytic and IoT for Water Resources

Elhassan Jamal

, Aniss Moumen

, Youssef Rissouni

, Jamal Chao

and Aimad Tahi

Geoscience Laboratory, Faculty of Science, Ibn Tofail University, Kenitra, Morocco

System Engineering Laboratory, National School of Applied Sciences, Ibn Tofail University, Kenitra, Morocco

Founder Le nid IT, France

Keywords: Big Data Analytics (BDA), Water Resources, IoT.

Abstract: Water is an increasingly scarce commodity. This life-preserving resource is an integral component of all

industries, from agribusiness to power generation. New technologies like Big Data can enable businesses,

communities, and people to overcome this crucial issue for humanity. However, the digital transformation of

water management can only attain significant success if designed and executed efficiently. Indeed, Analytics

and Big Data could prove decisive in our fight against the loss of water resources. Combined with the Internet

of Things, Big Data analytics technologies could help us optimize resource consumption, and reduce their

losses. This paper, will investigate the role of these new techniques and their provision of qualitative water

sources to facilitate and improve the average human life. What appropriate architecture can we implement to

solve our water scarcity issues compared to existing digital architectures?

1 INTRODUCTION

Water resources are the starting point for life in all the

species that live on our planet. Most living things

need freshwater, but only 0.3% of the water on Earth

is potable. The water demand has increased due to

population growth due to economic development. At

the same time, in various regions, they suffer flood

and drought, leading to mismanagement of water

resources. Furthermore, climate change has a

significant impact on water systems. This causes

major changes in water resources due to its direct

effects on hydrological processes such as

evaporation, humidity, and precipitation. The

combination of growth in the water demand, the

hydrological gap, and the climate pushed resource

managers and decision-makers to seek strategies for

the effective management of water resources. To

achieve this (Moumen, A, 2016)benefit, it is

necessary to increase the Information and

Communication Technologies capacity (ICT) to help

solve many types of problems that water management

currently faces. In retrospect, the development of

technology and the social economy has expanded the

field of data services for water resources. Moreover,

https://orcid.org/0000-0003-0141-1226

https://orcid.org/0000-0001-5330-0136

the development and application of RS, GIS, GPS,

IoT (Internet of Things), and other modern

technologies for collecting information that considers

the spatial and temporal types of data, generate a solid

increase in the volume and data types stored in

clusters, or other technologies like cloud servers.

According to Mocanu et al. (2013), there are several

challenges related to the development of ICT for

water management today:

- The amount of data grows progressively, so they

need methods to manage large volumes of data;

- The data comes from numerous legacy systems that

collect and process information, such as that related

to tributaries, for example. As a result, decision-

makers often base their decisions on outdated

applications.

- The geographical area for the analysis is wide.

- The data is of a different type, which analyzes the

same more complex.

2 METHODOLOGY

The literature review defines a process and reporting

structure to classify and identify research and results

Jamal, E., Moumen, A., Rissouni, Y., Chao, J. and Tahi, A.

Big Data Analytic and IoT for Water Resources.

DOI: 10.5220/0010736000003101

In Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning (BML 2021), pages 433-439

ISBN: 978-989-758-559-3

433

that were published for a given topic (in our case, big

data Analytics and Internet of Things in the field of

natural resources)(Elhassan et al., 2020). The

objective of the literature review is the classification,

thematic analysis, and identification of the main

forums of publication. The process followed in this

study is illustrated in the following figure, which

bases itself on a survey of 96 articles in the various

databases subsequently saved in Zotero and analyzed

via NVIVO:

Figure 1: Literature review processes.

In this paper we will focus on the articles that have

reached the stage of implementing a big data

analytical architecture in the field of natural resource

management.

3 EXISTENT BDA & IOT

ARCHITECTURES

FRAMEWORK

According to the literature review process described

above, we have arrived at a set of existing

architectures in the field of water resources. At first,

we will make a comparison of the different

architectures proposed and, in the end, we will

present our architecture that encompasses the

different existing elements so that it will be

implementable in reality:

 Big Data Analytics for Water Resources

Sustainability Evaluation : The author (Zhao and An,

2019)of this paper tries to show us the structure of

their architecture based on four layers described as

follows:

 Material layer

 Communication layer

 Middleware layer

 Application layer

For the processing of a large mass of data, the

authors carried out a parallel analysis which that

decomposes sub-similar tasks.

Figure 2: Big Data Analytics for Water Resources

Sustainability Evaluation(Zhao and An, 2019) proposed by

Yinghui Zhao &al.

 An IoT-based system for water resources

monitoring and management (Xiaocong et al., 2015):

The authors propose an IoT-based solution to support

decision-making using a water resources

management and monitoring system. The architecture

consists of four layers as a suite:

 Perception Layer

 Network Layer

 Middleware Layer

 Application Layer

The efficiency of connected objects and their rapid

development capabilities, along with their high level

of security and availability, are the main reasons why

the government in Beijing launched an IoT project in

2021 to monitor large cities.

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Figure 3: An IoT-based system for water resources

monitoring and management (Xiaocong et al., 2015)

proposed by Mo Xiaocong &al.

 A secure cloud-based solution for real-time

monitoring and management of Internet of

underwater things (IOUT)(Gopinath et al., 2019) :

The upgraded IOUT-based architecture is a

replacement of its predecessor, which works using

base stations with monitoring centers. In turn, the

upgraded version works with the following nodes:

 Sensor nodes

 Receptor nodes

 Cloud nodes: cloud-based monitoring

center

All three nodes use a cluster-based topology so

that the communications between the three are based

on the cluster heads.

Figure 4: A secure cloud-based solution for real-time

monitoring and management of Internet of underwater

things (IOUT)(Gopinath et al., 2019) proposed by M. P.

Gopinath &al.

 Big Data Open Platform for Water

Resources Management(Chalh et al., 2015) : in this

work, the authors create a platform to solve and

discuss the problems of water resources offered for a

massive volume of the collection, analyzed and

displayed data, to explore the heterogeneity of data

resulting from various sources, including semi-

structured ,structured and unstructured, also to forbid

and, or avoid a catastrophic event concerning floods

and, or droughts, thanks to water infrastructure

designed for purposes. This work focuses particularly

on the hypsometric focus developed in J2EE. This

tool is a decision tool that allows users to compare the

effects of different management scenarios, both

current and future, with the possibility to preserve the

natural and environment resources.

Figure 5: Big Data Open Platform for Water Resources

Management(Chalh et al., 2015) proposed by Ridouane

Chalh &al.

 A Framework for Processing Water

Resources Big Data and Application (Ai and Yue,

2014): The limitations of traditional methods include

efficiency, storage, real-time processing, and rapid

analysis of current water resources data. These

limitations are the leading causes for the proposal of

this architecture.

The author’s design architecture with four layers:

 Data acquisition layer

 Resources organization layer

 Data analysis layer

 Application service layer

The authors try to present their applications for

analyzing big data in this process and present the

framework of application and processing of this water

resources data.

Big Data Analytic and IoT for Water Resources

435

Figure 6: A Framework for Processing Water Resources

Big Data and Application (Ai and Yue, 2014) proposed by

Ping Ai &al.

 Big Data analytics and IoT in Operation

safety management in Under Water Management

(Nie et al., 2020): Mastering the use and conservation

of water has become one of the most critical priorities

of water providers. To do this, the authors of this

paper try to propose an architecture based on

connected objects and big data analytics to solve their

water conservation concerns in urban areas where it

is complicated to keep the recording of water

consumption.

The main components of this architecture are

based on five:

 Sensors …

 Cloud

 Internet & Wifi

 Mike Urban

 SCADA (Supervisory controller and data

acquirement)

Figure 7: Big Data analytics and IoT in Operation safety

management in Under Water Management(Nie et al., 2020)

proposed by Xiangtian Nie &al.

4 DISCUSSION

Studies on Big Data architectures, designed to

manage water resources of a locality, present a vision

of the research available in this field, allow the

formulation of new research papers, and determine

the most and least exploited topics in the area.

In this part, we make a higher-level analysis and

comparison of six existing architecture. Since the

latter have different views or perspectives on how

architecture models are represented, reliable

comparison frameworks need to be characterized by

fundamental elements: objectives, inputs and results.

The following table provide a more detailed

comparison of the different architectures proposed.

At first, we detail the objectives of each architecture

and its reasons to be presented. Then we present the

various inputs and checkmarks of each architecture.

Finally, we check their implementation and results.

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Table 1: Comparison of proposed architectures.

Architectures objectives inputs layers results

Implemented

Y/N

(Zhao and

An, 2019)

They intend to create a

prototype for assessing

the sustainability of

regional water resources

using big data from

regional economic and

social growth.

 data from different

resources such as:

 IOT

 Internet

 Folder

 Database

 Hardware layer

 Communication layer

 Middleware layer

 Application layer

An implemented

architecture that allows

the assessment of the

sustainability of regional

water resources in the

city of zhoushan

yes

(Xiaocong et

al., 2015)

Propose a system based

on the internet of things

(IoT) for water resources

monitoring and

management

 IOT

 Equipment perception

layer

 Information

transmission layer

 Data application layer

Proposed architecture

based on IOT for water

resources management

and monitoring

(Gopinath et

al., 2019)

Energy-aware and

secure communication

strategies are required in

the IoUT environment.

 IOT

 Data capture (using

sensor, robot,Sink …)

 Data monitoring

 Data analysis &

prediction

The suggested

architecture is more

secure and private than

the current system.

yes

(Chalh et al.,

2015)

to solve and discuss

water resources

problems involving a

large volume of

collected, analyzed, and

visualized data, to

analyze the

heterogeneity of data

resulting from various

sources, including

structured, unstructured,

and semi-structured

data, and to prevent

and/or avoid a

catastrophic event

related to floods and/or

droughts using hydraulic

infrastructures designed

for such purposesor

strategic planning.

 GIS data

 Decision Support

Tools

 Knowledge-Based

System

 Geographic

Information System

(GIS)

 Big Data Analysis

System

 Simulation Models

 Computation and

Processing

 Communication

System

 Search Engine

Users Interfaces

Propose o prototype of

big data that can offer

possibility for water

resources management

(Ai and Yue,

2014)

framework for

processing water

resources big data, to

process and analyze

modern water resources

data for real-time and

rapid

 Structured and

unstructured data

 Sensors

 Video monitor

 Office documents

 others

 Data acquisition layer

 Resource organization

layer

 Data analysis layer

 Application service

layer

the framework for

processing water

resources big data and

application provides

some reference for big

data processing in the

field of water

conservancy industry.

(Nie et al.,

2020)

Based on IoT and Big

Data Analytics, a

Supervisory Controller

and Data Acquisition

(SCADA) approach for

sustainable water

management in the smart

city.

 Internet

 Sensors

 Smart Pipes

 Smart Meters

 Data collection

 Data dissemination

 Data integration

 Modeling & analytics

 Visualisation

 Management &

control

 Decision support

The implementation

intends to produce better

levels of sustainable

water supply by

proactively controlling

water usage by both

companies and

customers.

Yes

Big Data Analytic and IoT for Water Resources

437

5 PROPOSED ARCHITECTURE

We base ourselves on the comparative table between

the different architectures proposed; we study their

limits at the level of implementation. We present our

architecture prototype that tries to respond to the

various difficulties encountered.

Figure 8: Proposed architecture.

6 CONCLUSION

In this paper, we compared and analyzed six digital

architectural frameworks proposed for water

management, using their different layers and

modules. As a result of this study, we proposed our

architecture prototype that combines all layers and

modules and can also be implemented and tested

quickly.

Looking forward, we believe that in the next

decade, new and innovative development methods

will be adopted within the context of water resource

management. These will incorporate and encompass

the challenges of requirements gathering, data

validation, model-driven development, and the list

goes on.

ACKNOWLEDGEMENT

We like to express our deepest respect and gratitude

to the conference's organizers and reviewers.

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