Big Data and Machine Learning for PhD in Water Management with Environment - Phdassistance

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BIG DATA AND MACHINE LEARNING FOR PhD IN WATER
MANAGEMENT WITH ENVIRONMENT
Dr. Nancy Agnes, Head,
Technical Operations, Phdassistance
info@phdassistance.com
IN-BRIEF
Rain Water Harvesting is the best method
to invest the Water, which induces the
groundwater levels. Big data manipulates
and deals with the massive amount of
datasets generated via dynamic data that
will generate every second. Due to many
contaminants in the groundwater, the
purity of drinking water diminishes its
certainty.
KEYWORDS
Smart farming, Big Data, Machine
Learning, Water Management, Gradient
Boost Regression algorithm, Aquatic
Biodiversity, Information Systems,
Sensors, Data Analytics, Artificial Neural
Network, Data Science, Groundwater
level
I. WATER MANAGEMENT
71% of the World's water content is
scarce, to 60% due to improper and
irresponsible water management. Need not
panic about the word Water Management.
It stores and preserves the water from rain,
kitchens, gardens, and other areas for
future purposes. Rain Water Harvesting is
the best method to invest the Water, which
induces the groundwater levels. It gives
double benefits, Increases the water table
and ensures the healthy Environment.
Water Management is the stage, where the
PhD scholars using Big Data Analytics
have an enormous scope to develop the
solutions for the crisis.
II. RECENT PHD RESEARCH BDA
To safeguard the Environment and
preserve the Water for next-generation,
many Researched it in ideal ways.
 The Recent Research Article from
Brazil concludes with System
analysis models that lend a hand to
address and track the variety of
water risks and issues all over the
World. 1
BIG DATA:
As the name suggests, Big data
manipulates and deals with the massive
amount of datasets generated via dynamic
data that will generate every second. There
are many tools in Big Data to manipulates
the data and produce accurate results.
 The work2
proposed that using Big
Data and IoT and monitoring the
Environment changes, based on
differences, will generate the data.
These data will be used for
analytics purpose.

MACHINE LEARNING:
Machine Learning is the Emerging
Technology to reduce the complex
manipulations and computations by
Humans.
The Machine will learn itself according to
its source code, what exactly it has to do.
Machine Learning implies it's root on
Water Management to reduce the
unsolvable manual complexities.
ML possesses its algorithms and
techniques for all kind of Data Analytics
and Data Science purposes. Some of the
familiar algorithms,
1. Super-vised Machine Learning
algorithms
2. Unsuper-vised Machine Learning
algorithms
3. Artificial Neural Networks
4. Badger Prairie Needs Network
5. Gradient Boost Regression algorithm
 The proposed article 3
pictures the
Water's quality by remote-sensing in
rural areas using Machine Learning
algorithms based on image pixels.
Those images will be treated as evident
data for Data analysis procedures.
Figure 1. Scatter plot of BPNN model to
express the deviations in threats for
Water. 3
III. BIG DATA IN WATER
MANAGEMENT WITH
ENVIRONMENT
To kickstarts Farming and improves crop
production, a bit of need for technology in
it.
 Article 4
proposed the theory of
Human-Centered Intelligent Systems
to monitor and track the crop's growth,
climatic conditions, pest control,
manure rate through sensors and
information systems.
 The impact of the Environment in
Water Management hits the
groundwater levels in the Urban life
cycle. The proposed article 5
will
resolve the issue of water scarcity due
to climatic conditions.
IV. MACHINE LEARNING IN WATER
MANAGEMENT
Due to many contaminants in the
groundwater, the purity of drinking water

diminishes its certainty.
 The proposed work 6
enhances the
originality using the Machine Learning
Algorithm XG Boost, ANN, to analyze
the groundwater's purity texture and
whether it is legitimate to consume.
 The impact of Anthropogenic activities
shakes and hits biodiversity. The
deviation and extinction of aquatic
biodiversity are due to anthropogenic
emissions. The core review of
literature 7
is to preserve marine
biodiversity using Machine Learning
algorithms.
Table. 1 for Big Data and Machine
Learning in Water Management:
V. FUTURE SCOPE FOR BIG DATA
AND MACHINE LEARNING IN
WATER MANAGEMENT
BDA and ML have already planted its root
deeply. Still, Water Management,
Environmental Ethics, Farming and
Agriculture is in hunger to feed with
technology. Let's drive into some future
ehancements for BDA and ML in
respective fields,
1. BDA and ML lend a hand to improve
the water utilities, which scrutinize the
Customer Service.
EXAMPLE: Intensity of purity can be
determined efficiently.
2. Data Analytics has the long way to go,
but Water preservation doesn't have the
same. Using BDA, we can efficiently
predict future climatic conditions and
generate the report. Based on the report,
further forecasting procedures will be
enhanced.
EXAMPLE: Identify the water scarcity
region and Magnifies the causation for the
scarcity. Put forth an action accordingly.
3. Using Machine Learning Algorithms,
we can efficiently forecast the project's
cost-effectiveness, which implemented to
preserve Water. Every detail will be
monitor and control the water level
deviations.
EXAMPLE: Comparing with other
algorithms, machine learning algorithms
produces best results with high accuracy
4. Communicating through sensors,
forecasting the water levels using Machine
Learning algorithms is emerging.
EXAMPLE: To access and analyse the
remote-sensing areas will become tedious
one. Like Quarries, Mine fields and
Marine areas in such cases, Sensors is
mandatory.
REFERENCE
[1] Nazli Kosegulu, "A Systems Analysis Approach to
Addressing Contemporary Water Challenges:
S.NO METHOD REFERENCE PURPOSE
1 System Model
Analysis
[1] To analyze and
address the variety
of water risks
2 Radio Frequency
Identification
[2] To identify the
Environment and
climatic conditions
3 Badger Prairie
Needs
Network (BPNN)
[3] To build the
estimation models
using BDA
4 Information
systems and
Sensors
[4] To detect and
predict the crop's
growth
5 SCADA, GIS and
ESPANET
[5] To ensures the
groundwater level
6 XG Boost [6] To speed up the live
tracking of purity
level of
groundwater
7 XG Boost and
Random Forest
[7] To boost the
tracking of aquatic
biodiversity and
cluster according to
the species

Management Improvements in Brazil and Beyond"
(2021).
[2] Yosra Hajjaji, Wadii Boulila, Imed Riadh Farah,
Imed Romdhani, Amir Hussain, Big data and IoT-based
applications in intelligent environments:
https://doi.org/10.1016/j.cosrev.2020.100318.
[3] Bo Jiang, Hailong Liu, Qianguo Xing, Jiannan
Cai, Xiangyang Zheng, Lin Li, Sisi Liu, Zhiming
Zheng, Huiyan Xu, Ling Meng "Evaluating Traditional
Empirical Models and BPNN Models in Monitoring the
Concentrations of Chlorophyll-A and Total Suspended
Particulate of Eutrophic and Turbid Waters" (2021).
[4] Ouafiq E., Elrharras A., Mehdary A., Chehri A.,
Saadane R., Wahbi M. (2021) IoT in Smart Farming
Analytics, Big Data Based Architecture. In:
Zimmermann A., Howlett R., Jain L. (eds) Human-
Centred Intelligent Systems. Smart Innovation,
Systems and Technologies, vol 189. Springer,
Singapore.
https://doi.org/10.1007/978-981-15-5784-2_22
[5] Inês Figueiredo, Paulo Esteves, Paulo Cabrita,
"Water-wise – a digital water solution for smart cities
and water management entities, Procedia Computer
Science," (2021). [6] Sudhakar Singha, Srinivas
Pasupuleti, Soumya S. Singha, Rambabu Singh,
Suresh Kumar, "Prediction of groundwater quality
using efficient machine learning technique,
Chemosphere," (2021),
https://doi.org/10.1016/j.chemosphere.2021.130265.
[7] Carlotta Valerio, Lucia De Stefano, Gonzalo
Martínez-Muñoz, Alberto Garrido, "A machine
learning model to assess the ecosystem response to
water policy measures in the Tagus River Basin
(Spain), Science of The Total Environment,"(2020)
https://doi.org/10.1016/j.scitotenv.2020.141252.

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