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HBRP Publication Page 29-41 2023. All Rights Reserved Page 29
Journal of Water Resource Research and Development
Volume 6 Issue 1
DOI: https://doi.org/10.5281/zenodo.7878771
Water Resource Management Using Artificial Intelligence
Enabled RS & GIS
Samirsinh P Parmar*
Assistant Prof. Dept. of Civil Engineering, Dharmsinh Desai University, Nadiad, Gujarat
*Corresponding Author
E-mail Id:-spp.cl@ddu.ac.in
ABSTRACT
Research and applications of RS & GIS in water resources management were practiced in
the last two decades. The pitfalls and shortcomings of GIS technology can be minimized by
artificial intelligence. Artificial intelligence (AI)-based techniques have become more
prevalent in recent years because of the most appropriate applications with precision.
Compared to sectors like energy, healthcare, or transportation, the usage of AI-based
techniques in the water domain is comparatively modest. This paper reviews current AI
applications for managing water resources and offers some speculative applications for AI in
tandem with GIS technology. It outlines the step-by-step implementation of GIS technology
with AI for managing water resources. Finally, the future of AI-GIS technology for managing
water resources is examined.
Keywords:-AI, GIS, DSS, GeoAI, predictive modelling, data integration.
ABBREVIATIONS
RS : Remote Sensing
GIS : Geographical Information System
AI : Artificial Intelligence
GIS : Geographic Information System
ANI : Artificial Narrow Intelligence
AGI : Artificial General Intelligence
ASI : Artificial Super Intelligence
IoT : Internet of Things
DSS : Decision Support System
ESRI : Environmental Systems Research
Institute
GeoAI : Geospatial Artificial Intelligence
INTRODUCION
Water resource management is the process
of planning, developing, distributing, and
managing water resources for optimal
usage.[1]
Water resource management also
entails managing water-related risks,
including floods, drought, and
contamination (World Bank, 2017). Water
resources management is a complex
problem even today as it involves whole
water cycle incorporating activities carried
out by humans. The factors affecting water
resources management are climatic
conditions, geography, precipitation,
ecosystem, runoff, and utilization of it
before it accumulates in oceans. Integrated
water resources management system
involves four basic steps, (i) availability of
total water (ii) managing events related to
water resources (i.e., losses, flood,
draught, landslide etc.) (iii) Utilization of
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Journal of Water Resource Research and Development
Volume 6 Issue 1
DOI: https://doi.org/10.5281/zenodo.7878771
water by various stockholders (iv) decision
making based on technical, engineering,
economical and policies related to water
resources.
AI INTRODUCTION
AI (Artificial Intelligence) is a branch of
computer science that focuses on the
development of intelligent machines that
can perform tasks that typically require
human intelligence, such as visual
perception, speech recognition, decision-
making, and language translation. AI
systems can learn from experience, adapt
to new situations, and improve their
performance over time.
AI is based on the idea of creating
machines that can simulate human thought
processes, such as reasoning, learning, and
problem-solving. These machines use
algorithms, statistical models, and deep
learning techniques to analyse large
amounts of data, identify patterns, and
make predictions. There are three main
types of AI, which is depicted in figure-
1.[1]
Artificial Narrow Intelligence (ANI) or
Weak AI: ANI is intended to perform a
specific task, such as playing chess or
translating languages. These systems are
focused on solving one particular problem
and are not capable of generalizing to new
problems.
Artificial General Intelligence (AGI) or
Strong AI: AGI is intended to perform
any intellectual task that a human can do.
These systems can reason, learn, and adapt
to new situations, and are capable of
generalizing to new problems.
Fig.1:-Classification of AI based on capabilities (Ref: https://chat.openai.com/)
Artificial Super Intelligence (ASI): ASI
is a hypothetical level of intelligence that
surpasses human intelligence in every
aspect.
AI has the potential to transform various
fields, including healthcare, finance,
transportation, and energy, by enabling
automation, improving efficiency, and
unlocking new insights from data.
However, there are also concerns about the
ethical implications of AI, such as bias,
privacy, and job displacement.
GIS INTRODUCTION
GIS (Geographic Information System) is a
computer-based tool that allows users to
store, manage, analyse, and visualize
geospatial data. GIS can combine different
types of data, such as satellite imagery,
maps, and demographic information, to
provide a better understanding of the
geographic relationships between different
phenomena. Figure -2 describes the
transformation of real-world problem to
segregated raster to vector thematic maps
and data set generated out of it.
GIS software is designed to manage and
process spatial data using spatial analysis
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Journal of Water Resource Research and Development
Volume 6 Issue 1
DOI: https://doi.org/10.5281/zenodo.7878771
tools, including overlay analysis, buffer
analysis, and proximity analysis. This
allows users to identify patterns,
relationships, and trends in the data, and
make informed decisions about a variety of
topics, such as land use planning, natural
resource management, emergency
response, and urban design.
Fig.2:- The concept of layers (Courtesy-ESRI)
GIS is used in various fields, including
environmental science, geology, forestry,
urban planning, transportation, and
agriculture. GIS can also be used in
combination with other technologies, such
as GPS (Global Positioning System),
remote sensing, and 3D modelling, to
provide a more comprehensive view of the
geospatial data.
The benefits of GIS include improved
decision-making, increased efficiency, and
enhanced communication. GIS can provide
decision-makers with valuable insights
into complex spatial relationships,
enabling them to make informed decisions.
It can also help reduce costs and increase
efficiency by automating routine tasks and
streamlining data management processes.
Additionally, GIS can help communicate
complex spatial information in a clear and
accessible way, enabling a wider audience
to understand the implications of decisions
related to geospatial data. GIS is a useful
tool for controlling land use within a
drainage basin because of its capacity to
characterise and model regional
differences in hydrological processes
(Stuart and Stocks, 1993).
Overall, GIS is a powerful tool that
enables users to better understand and
manage the complex relationships between
different geospatial phenomena.
GIS APPLICATIONS IN WATER
RESOURCES MANAGEMENT
GIS has emerged as a highly complex
database management system for
assembling and storing the massive
amounts of data required in hydrologic
modelling (Bhaskar et al., 1992; Vieux et
al., 1989). GIS may be used to quickly
compile geomorphic parameters of
drainage basins such as drainage densities
and channel frequency. GIS is a useful tool
for controlling land use within a drainage
basin because of its capacity to
characterise and model regional
differences in hydrological processes
(Stuart and Stocks, 1993). Figure 3
represents combined knowledge of Remote
Sensing (RS) with the Global Positioning
System (GPS) and Geographical
Information System (GIS) can be
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Journal of Water Resource Research and Development
Volume 6 Issue 1
DOI: https://doi.org/10.5281/zenodo.7878771
applicable to land use, soil moisture,
rainfall, evapotranspiration, availability of
surface and groundwater, and the use that
could be used in the areas of snowmelt-
runoff forecasts, irrigation performance
evaluation, drought monitoring, watershed
treatment, reservoir sedimentation, and
flood mapping and management.
Fig.3:-Contribution of RS and GIS in water resources management.
Table 1, gives the track record of
contributions in published form from the
researchers in the field of “RS and GIS
applications in water resources
management”. It indicates that per decade,
on average, 21,000 pieces of research were
published. But in the last 3 years only,
there have been 25000 and above
publications available, which indicates that
there has been a saturation of research
topics in above mentioned subject.
Therefore, it is the demand for the time
that the researchers need to incorporate
new innovative technologies in the fields
of both RS and GIS and water resource
management.
Table 1:-Duration and number of publications in the field of RS & GIS applications in
water resources management.
Sr. No. Duration Number of Publications*
1 1990- 2000 21,700
2 2001-2010 21,400
3 2011-2020 24,700
4 2021-2023 25, 500
*Data generated from google scholar publications per year
COMBINATION OF AI AND GIS
AI (Artificial Intelligence) and GIS
(Geographic Information Systems) can be
used together for water resources
management to improve the efficiency and
effectiveness of decision-making
processes. Figure-4 depicts the step wise
application of combined AI and GIS in
water resources management:
Fig.4:- step-wise process for working with the combination of AI and GIS.
HBRP Publication Page 29-41 2023. All Rights Reserved Page 33
Journal of Water Resource Research and Development
Volume 6 Issue 1
DOI: https://doi.org/10.5281/zenodo.7878771
Step-1 Predictive Modelling: AI can be
used to develop predictive models to
forecast water demand, water quality, and
flood events. These models can be
integrated into GIS to provide spatially
referenced information for decision-
making.
Step-2 Real-time Monitoring: AI can be
used to analyse real-time data from sensors
and other monitoring systems, such as
satellite imagery, to detect changes in
water quality, flow rates, and weather
patterns. GIS can then be used to visualize
and analyse this data in a spatial context.
Step-3 Data Integration: AI can be used
to integrate data from multiple sources,
such as weather data, hydrological models,
and social media, to provide a more
comprehensive understanding of water
resources. GIS can be used to store and
analyse this data, allowing for better
decision-making.
Step-4 Decision Support: AI can be used
to provide decision support for water
resources management by analysing data,
developing scenarios, and recommending
actions. GIS can be used to visualize the
results of these analyses in a spatial
context, allowing for better
communication among stakeholders.
Step-5 Resource Allocation: AI can be
used to optimize the allocation of water
resources, such as determining the most
efficient use of irrigation water in
agriculture. GIS can be used to map the
location of water resources and to analyse
the spatial distribution of demand.
The integration of AI and GIS can provide
a powerful tool for water resources
management, allowing for better decision-
making, improved efficiency, and effective
communication among stakeholders.
AI IN PREDICTIVE MODELLING OF
WATER RESOURCES
AI (Artificial Intelligence) can be used for
predictive modelling of water resources to
forecast water availability, water quality,
and flood events. Here are some examples
of how AI can be used in predictive
modelling of water resources (figure-5):
Fig.5:-AI based predictive modelling of water resources management
Machine Learning Algorithms: AI can use
machine learning algorithms to analyse
large datasets of historical water resource
data and to develop predictive models
based on past patterns and trends. These
models can then be used to forecast future
water availability, water quality, and flood
events.
Remote Sensing: AI can use remote
sensing data, such as satellite imagery, to
monitor water resources and to detect
changes in water availability and quality.
Machine learning algorithms can then be
used to analyse this data and to develop
predictive models. Real-time Monitoring:
AI can use real-time monitoring data from
sensors and other monitoring systems to
detect changes in water availability and
quality. Machine learning algorithms can
then be used to develop predictive models
based on this data.
Climate Data: AI can use climate data to
predict future weather patterns and to
forecast the impacts of climate change on
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Journal of Water Resource Research and Development
Volume 6 Issue 1
DOI: https://doi.org/10.5281/zenodo.7878771
water resources. Machine learning
algorithms can then be used to develop
predictive models based on these
predictions. Decision Support: AI can use
predictive modelling to provide decision
support for water resources management.
By providing forecasts of water
availability, quality, and flood events, AI
can help decision-makers to plan and
allocate water resources more effectively.
AI can provide a powerful tool for
predictive modelling of water resources,
allowing for better decision-making,
improved efficiency, and effective
communication among stakeholders.
REAL-TIME MONITORING OF
WATER RESOURCES
AI (Artificial Intelligence) can be used for
real-time monitoring of water resources to
detect changes in water quality, quantity,
and flow patterns. Here are some instances
of how artificial intelligence can be used to
monitor the availability of water in real-
time:
Sensor Networks: AI can be used to
analyse real-time data from sensors
installed in water bodies, pipes, and
treatment plants to detect changes in water
quality and quantity. Machine learning
algorithms can then be used to identify
patterns and anomalies in the data.
Remote Sensing: AI can use remote
sensing data, such as satellite imagery, to
monitor water resources from a distance.
Machine learning algorithms can then be
used to analyse this data and to detect
changes in water quality and quantity.
Smart Metering: AI can be used to
analyse real-time data from smart meters
installed in households and commercial
buildings to detect changes in water usage
patterns. Machine learning algorithms can
then be used to develop predictive models
of water demand.
Predictive Analytics: AI can use
predictive analytics to forecast water
quality and quantity based on real-time
monitoring data. Machine learning
algorithms can then be used to provide
early warnings of potential water quality
issues or water scarcity.
Decision Support: AI can use real-time
monitoring data to provide decision
support for water resources management.
By providing timely and accurate
information, AI can help decision-makers
to respond quickly to changes in water
resources.
AI can provide a powerful tool for real-
time monitoring of water resources,
allowing for better decision-making,
improved efficiency, and effective
communication among stakeholders.
DATA INTEGRATION OF WATER
RESOURCES
AI (Artificial Intelligence) can be used for
data integration of water resources by
combining data from multiple sources,
such as weather data, hydrological models,
and social media, to provide a more
comprehensive understanding of water
resources. Figure-6 explains the
accumulation of data from various sources
and then it is refined into the AI based
environment.
Fig.6:-Data integration in AI environment
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Journal of Water Resource Research and Development
Volume 6 Issue 1
DOI: https://doi.org/10.5281/zenodo.7878771
Machine Learning Algorithms: AI can
use machine learning algorithms to analyse
large datasets of water resource data and to
identify patterns and trends. Machine
learning can then be used to integrate this
data with other sources of information.
Social Media Analysis: AI can be used to
analyse social media data, such as Twitter
feeds, to gather information on water
quality and quantity issues. Machine
learning algorithms can then be used to
integrate this data with other sources of
information.
Internet of Things (IoT): AI can be used
to analyse data from IoT sensors installed
in water bodies, pipes, and treatment
plants. Machine learning algorithms can
then be used to integrate this data with
other sources of information.
Weather Data: AI can use weather data to
forecast water availability and to predict
the impacts of climate change on water
resources. Machine learning algorithms
can then be used to integrate this data with
other sources of information.
Decision Support: AI can use data
integration to provide decision support for
water resources management. By
integrating data from multiple sources, AI
can provide decision-makers with a more
comprehensive understanding of water
resources.
AI can be a strong tool for water resource
data integration, facilitating better
decision-making, increased efficiency, and
effective communication among
stakeholders.
AI IN WATER RESOURCE
ALLOCATION
AI (Artificial Intelligence) can be used to
allocate water resources by delivering
accurate and timely information on water
availability, demand, and quality to
decision-makers. Figure-7 delineates the
allocation of water resources data,
processed under AI environment and how
it can be applicable to modern usage of AI
as modern tool for water resources
allocation.
Fig.7:-AI based water resources allocation
Predictive Analytics: AI can use
predictive analytics to forecast water
demand and to identify potential supply
shortages. Machine learning algorithms
can then be used to develop water
allocation plans based on this information.
Optimization Models: AI can use
optimization models to identify the most
efficient and effective ways to allocate
water resources. Machine learning
algorithms can then be used to optimize
water allocation plans based on real-time
data.
Smart Metering: AI can be used to
analyse water usage data from smart
meters installed in households and
commercial buildings. Machine learning
algorithms can then be used to develop
personalized water allocation plans based
on usage patterns.
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Journal of Water Resource Research and Development
Volume 6 Issue 1
DOI: https://doi.org/10.5281/zenodo.7878771
Real-time Monitoring: AI can use real-
time monitoring data from sensors and
other monitoring systems to detect changes
in water availability and quality. Machine
learning algorithms can then be used to
adjust water allocation plans based on this
data.
Decision Support: AI can use water
resource allocation models to provide
decision support for water resources
management. By providing accurate and
timely information on water availability
and demand, AI can help decision-makers
to allocate water resources more
effectively.
AI IN WATER RESOURCES
MANAGEMENT DECISION
SUPPORT
Decision support system for water
resources management is an interactive
field where knowledge of water resources
engineering, meteorology, climatology,
geology, mathematics, computer
programming, and relevant software’s. The
factors affecting DSS are very large in
number hence the database generated from
various fields is quantitative but may not
be qualitative for further application. To
validate data mathematical operations such
as statistical analysis, and probabilistic
methods are adopted. Computer tools and
software are used to facilitate such
operations. While doing such operations
also for data operations, further decision-
making is required. To minimize manual
involvement in data operations, the
software uses algorithms and they take
their own decision and proceed further to
carry out mathematical operations. AI
(Artificial Intelligence) can be used for
decision support in water management by
providing decision-makers with accurate
and timely information on water resources.
Here are some examples of how AI can be
used in decision support for water
management:
Predictive Analytics: AI can use
predictive analytics to forecast water
availability and demand, and to identify
potential water quality issues. Machine
learning algorithms can then be used to
develop water management strategies
based on this information.
Optimization Models: AI can use
optimization models to identify the most
efficient and effective ways to manage
water resources. Machine learning
algorithms can then be used to optimize
water management plans based on real-
time data.
Real-time Monitoring: AI can use real-
time monitoring data from sensors and
other monitoring systems to detect changes
in water availability and quality. Machine
learning algorithms can then be used to
adjust water management plans based on
this data.
Risk Assessment: AI can use risk
assessment models to identify potential
risks to water resources, such as drought,
flooding, or contamination. Machine
learning algorithms can then be used to
develop contingency plans to mitigate
these risks.
Decision Support Systems: AI can use
decision support systems to provide
decision-makers with timely and accurate
information on water resources. By
providing a comprehensive overview of
water resources, decision-makers can
make more informed decisions about water
management strategies.
FUTURE OF AI AND GIS BASED
WATER RESOURCES
MANAGEMENT
The future of AI (Artificial Intelligence)
and GIS (Geographic Information
Systems) in water resources management
looks very promising. Here are some
potential developments that could shape
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Journal of Water Resource Research and Development
Volume 6 Issue 1
DOI: https://doi.org/10.5281/zenodo.7878771
the future of AI and GIS-based water
resources management:
Increased Data Integration: As more data
becomes available from various sources
such as IoT sensors, remote sensing
satellites, and social media, AI and GIS-
based water resources management will
become more data-intensive. With
machine learning algorithms, data can be
analysed and integrated into decision-
making models for water resource
management.
Advanced Predictive Analytics: With the
help of AI, advanced predictive analytics
models can be developed to forecast water
availability and demand, detect anomalies
and changes in water quality, and identify
potential risks and hazards in real-time.
This will enable water resource managers
to take proactive measures to address
issues before they become significant
problems.
Enhanced Decision Support Systems: AI
can be used to develop more advanced and
efficient decision support systems for
water resource management. These
systems will provide decision-makers with
real-time and accurate information on
water resources, enabling them to make
more informed decisions that lead to better
outcomes.
Increased Automation: AI can be used to
automate routine tasks such as data
processing, analysis, and reporting. This
will free up water resource managers to
focus on more critical tasks such as
strategic planning and decision-making.
Improved Efficiency and Sustainability:
With AI and GIS-based water resources
management, we can expect more efficient
and sustainable management practices.
This will help conserve and manage water
resources more effectively, while
minimizing waste and reducing
environmental impacts.
The future of AI and GIS-based water
resources management is very exciting. As
technology advances, we can expect to see
more efficient, sustainable, and effective
management practices that will help
address the growing challenges of water
resource management.
GEO-INTELLIGENCE
Geo-intelligence is a broad word that
encompasses geospatial visualization,
analysis, decision-making, design, and
control based on GIS, remote sensing, and
satellite positioning technologies. The
most distinctive feature that distinguishes
GIS from other data technologies is geo-
intelligence. It has four stages (figure-8)
that contribute to the Geo-intelligence
pyramid: Geo-visualization, Geo-decision,
Geographic-design, and Geo-control. The
complexity of the pyramid increases from
bottom to top, while maturity decreases.
Geo-intelligence will usher in a new era of
technological innovation and create more
value with the advent of AI.
Fig.8:- AI Enhanced Geo-Intelligence
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Journal of Water Resource Research and Development
Volume 6 Issue 1
DOI: https://doi.org/10.5281/zenodo.7878771
The basis of GeoAI (AI Enhanced Geo-
Intelligence) starts with visualization
followed by decision, design and control
over AI Intelligence. (Figure-8) The
complexity is low at the base of pyramid
and it is on the most complex stage when it
reached to control level. On the
contradiction the control stage is less
developed at control level and highest at
the visualization stage.
AI for GIS
AI for GIS refers to the application of
artificial intelligence (AI) technology to
improve the intelligence of GIS software,
such as AI Attribute Collecting, AI Survey
& Mapping, AI Cartography, and AI
interaction. AI Attribute Collecting can
contribution users in intelligently
classifying and classifying multi-source
targets such as video images; AI Survey &
Mapping can provide lower cost and more
convenient indoor mapping solutions; AI
Cartography saves users from the time-
consuming process of manual mapping,
and style transfer from image to map can
be realised through simple operation; AI
interaction includes rich application
interaction such as voice, gestures, body,
and so on.
Fig.9:- AI-GIS Technology for Water resources management
GIS for AI
When confronted with simulated
intelligence acknowledgment results, GIS
can additionally process and mine
information by utilizing its spatial
representation and spatial examination
abilities, that is to say, GIS engages
computer based intelligence. Applications
for map visualization such as traffic flow
monitoring, city component management,
and cases can offer decision makers a
method of information expression that is
easier to understand; while inside and out
handling and mining of simulated
intelligence extraction results can
empower constant geo-wall cautions and
vehicle following, etc to additionally grow
applications.
A relatively complete AI GIS technology
system can be constructed using a four-
layer structure from the bottom up to
effectively support the three components
of AI GIS. The domain library follows the
data layer at the bottom. Several AI
frameworks can be reasonably abstracted
and encapsulated in the framework layer.
The three parts of the AI GIS that are
specifically introduced constitute the top
functional layer.
AI GIS Future
An effective solution to the problem of the
current intelligent GIS system is to use AI
GIS to enhance and develop the next
generation of GIS technology systems. AI
GIS was the first to use computer vision to
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Journal of Water Resource Research and Development
Volume 6 Issue 1
DOI: https://doi.org/10.5281/zenodo.7878771
extract geographic data from videos and
photos taken with remote sensing
equipment. Moreover, artificial
intelligence advances, for example,
discourse acknowledgment and language
handling can be executed, considering
more prominent strengthening. In any
case, computer based intelligence GIS is
still in the Tight computer based
intelligence stage and is quite far from
general simulated intelligence (AGI).
Consequently, general AI technology is an
important path for AI GIS development in
the future.
Fig.10:- Gartner Hype Cycle for AI (2019)
(Reference: https://www.supermap.com/en-us/news/?82_2701.html)
ADVANTAGES OF COMBINATION
OF AI AND GIS
The combination of AI (Artificial
Intelligence) and GIS (Geographic
Information Systems) can provide several
advantages in various fields, including
water resources management. Here are
some of the advantages of the combination
of AI and GIS:
Improved Spatial Analysis: GIS allows for
the analysis of spatial data, while AI can
be used to develop more advanced and
sophisticated analytical models. Together,
AI and GIS can provide more accurate and
efficient spatial analysis, such as land use
classification, land cover change detection,
and habitat mapping.
Enhanced Data Management: With the
help of GIS, AI can be used to analyse,
process, and manage large volumes of
geospatial data. This can be especially
useful for water resource management,
where large amounts of data from various
sources need to be analysed and integrated
into decision-making models.
Better Predictive Analytics: AI can be used
to develop more advanced predictive
models, while GIS can provide the spatial
context necessary for such models. By
combining AI and GIS, we can develop
more accurate and reliable predictive
models for water resource management,
such as flood forecasting, drought
prediction, and water quality modelling.
Improved Decision-making: The
combination of AI and GIS can provide
decision-makers with real-time and
accurate information on water resources.
This can help inform better decision-
making, such as water allocation,
infrastructure planning, and disaster
management.
Increased Efficiency: By automating
routine tasks, such as data processing and
analysis, the combination of AI and GIS
can help increase efficiency in water
resource management. This can help save
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Journal of Water Resource Research and Development
Volume 6 Issue 1
DOI: https://doi.org/10.5281/zenodo.7878771
time and resources, and enable decision-
makers to focus on more critical tasks.
The combination of AI and GIS provides a
powerful tool for water resource
management. By integrating advanced
analytical models and spatial data, AI and
GIS can help inform better decision-
making, improve efficiency, and enable
more sustainable management practices.
CONCLUSION
This paper presents a survey and an
unpleasant classification of various sorts of
utilizations of artificial intelligence in the
water space. Modeling, prediction and
forecasting, decision support and
operational management, and optimization
fall into these categories. The reviewed
literature on AI in the water domain and
the most recent literature on AI and ethics
show that the ethical aspects of AI
applications in the water domain receive
little attention. Late writing on simulated
intelligence and morals proposes a
rundown of five rules that ought to be
considered while creating artificial
intelligence applications. Transparency,
fairness and justice, accountability and
responsibility, privacy, and non-malice are
all examples of these.
When compared to its use in other
industries, AI's application in the water
domain remains somewhat limited. This
presents an opportunity to avoid and gain
knowledge from past errors. Insights
regarding AI-based applications of the
water resources domain have been
discussed in this paper. AI has the
potential to improve theory-guided data
science and steer clear of some of the
drawbacks of strictly data-driven models.
Luckily, man-made intelligence is
progressively perceived and there is more
than adequate involvement in participatory
dynamic in the water space. Water
professionals, data scientists, and experts
from the social and humanities are
required to collaborate on the development
and implementation of responsible AI
techniques for the water resource domain.
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HBRP Publication Page 29-41 2023. All Rights Reserved Page 41
Journal of Water Resource Research and Development
Volume 6 Issue 1
DOI: https://doi.org/10.5281/zenodo.7878771
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total environement 755(2021)
Cite this article as:
Samirsinh P Parmar. (2023). Water
Resource Management Using Artificial
Intelligence Enabled RS & GIS. Journal
of Water Resource Research and
Development, 6(1), 29–41.
https://doi.org/10.5281/zenodo.7878771

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Water Resource Management Using Artificial Intelligence Enabled RS & GIS (1).pdf

  • 1. HBRP Publication Page 29-41 2023. All Rights Reserved Page 29 Journal of Water Resource Research and Development Volume 6 Issue 1 DOI: https://doi.org/10.5281/zenodo.7878771 Water Resource Management Using Artificial Intelligence Enabled RS & GIS Samirsinh P Parmar* Assistant Prof. Dept. of Civil Engineering, Dharmsinh Desai University, Nadiad, Gujarat *Corresponding Author E-mail Id:-spp.cl@ddu.ac.in ABSTRACT Research and applications of RS & GIS in water resources management were practiced in the last two decades. The pitfalls and shortcomings of GIS technology can be minimized by artificial intelligence. Artificial intelligence (AI)-based techniques have become more prevalent in recent years because of the most appropriate applications with precision. Compared to sectors like energy, healthcare, or transportation, the usage of AI-based techniques in the water domain is comparatively modest. This paper reviews current AI applications for managing water resources and offers some speculative applications for AI in tandem with GIS technology. It outlines the step-by-step implementation of GIS technology with AI for managing water resources. Finally, the future of AI-GIS technology for managing water resources is examined. Keywords:-AI, GIS, DSS, GeoAI, predictive modelling, data integration. ABBREVIATIONS RS : Remote Sensing GIS : Geographical Information System AI : Artificial Intelligence GIS : Geographic Information System ANI : Artificial Narrow Intelligence AGI : Artificial General Intelligence ASI : Artificial Super Intelligence IoT : Internet of Things DSS : Decision Support System ESRI : Environmental Systems Research Institute GeoAI : Geospatial Artificial Intelligence INTRODUCION Water resource management is the process of planning, developing, distributing, and managing water resources for optimal usage.[1] Water resource management also entails managing water-related risks, including floods, drought, and contamination (World Bank, 2017). Water resources management is a complex problem even today as it involves whole water cycle incorporating activities carried out by humans. The factors affecting water resources management are climatic conditions, geography, precipitation, ecosystem, runoff, and utilization of it before it accumulates in oceans. Integrated water resources management system involves four basic steps, (i) availability of total water (ii) managing events related to water resources (i.e., losses, flood, draught, landslide etc.) (iii) Utilization of
  • 2. HBRP Publication Page 29-41 2023. All Rights Reserved Page 30 Journal of Water Resource Research and Development Volume 6 Issue 1 DOI: https://doi.org/10.5281/zenodo.7878771 water by various stockholders (iv) decision making based on technical, engineering, economical and policies related to water resources. AI INTRODUCTION AI (Artificial Intelligence) is a branch of computer science that focuses on the development of intelligent machines that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision- making, and language translation. AI systems can learn from experience, adapt to new situations, and improve their performance over time. AI is based on the idea of creating machines that can simulate human thought processes, such as reasoning, learning, and problem-solving. These machines use algorithms, statistical models, and deep learning techniques to analyse large amounts of data, identify patterns, and make predictions. There are three main types of AI, which is depicted in figure- 1.[1] Artificial Narrow Intelligence (ANI) or Weak AI: ANI is intended to perform a specific task, such as playing chess or translating languages. These systems are focused on solving one particular problem and are not capable of generalizing to new problems. Artificial General Intelligence (AGI) or Strong AI: AGI is intended to perform any intellectual task that a human can do. These systems can reason, learn, and adapt to new situations, and are capable of generalizing to new problems. Fig.1:-Classification of AI based on capabilities (Ref: https://chat.openai.com/) Artificial Super Intelligence (ASI): ASI is a hypothetical level of intelligence that surpasses human intelligence in every aspect. AI has the potential to transform various fields, including healthcare, finance, transportation, and energy, by enabling automation, improving efficiency, and unlocking new insights from data. However, there are also concerns about the ethical implications of AI, such as bias, privacy, and job displacement. GIS INTRODUCTION GIS (Geographic Information System) is a computer-based tool that allows users to store, manage, analyse, and visualize geospatial data. GIS can combine different types of data, such as satellite imagery, maps, and demographic information, to provide a better understanding of the geographic relationships between different phenomena. Figure -2 describes the transformation of real-world problem to segregated raster to vector thematic maps and data set generated out of it. GIS software is designed to manage and process spatial data using spatial analysis
  • 3. HBRP Publication Page 29-41 2023. All Rights Reserved Page 31 Journal of Water Resource Research and Development Volume 6 Issue 1 DOI: https://doi.org/10.5281/zenodo.7878771 tools, including overlay analysis, buffer analysis, and proximity analysis. This allows users to identify patterns, relationships, and trends in the data, and make informed decisions about a variety of topics, such as land use planning, natural resource management, emergency response, and urban design. Fig.2:- The concept of layers (Courtesy-ESRI) GIS is used in various fields, including environmental science, geology, forestry, urban planning, transportation, and agriculture. GIS can also be used in combination with other technologies, such as GPS (Global Positioning System), remote sensing, and 3D modelling, to provide a more comprehensive view of the geospatial data. The benefits of GIS include improved decision-making, increased efficiency, and enhanced communication. GIS can provide decision-makers with valuable insights into complex spatial relationships, enabling them to make informed decisions. It can also help reduce costs and increase efficiency by automating routine tasks and streamlining data management processes. Additionally, GIS can help communicate complex spatial information in a clear and accessible way, enabling a wider audience to understand the implications of decisions related to geospatial data. GIS is a useful tool for controlling land use within a drainage basin because of its capacity to characterise and model regional differences in hydrological processes (Stuart and Stocks, 1993). Overall, GIS is a powerful tool that enables users to better understand and manage the complex relationships between different geospatial phenomena. GIS APPLICATIONS IN WATER RESOURCES MANAGEMENT GIS has emerged as a highly complex database management system for assembling and storing the massive amounts of data required in hydrologic modelling (Bhaskar et al., 1992; Vieux et al., 1989). GIS may be used to quickly compile geomorphic parameters of drainage basins such as drainage densities and channel frequency. GIS is a useful tool for controlling land use within a drainage basin because of its capacity to characterise and model regional differences in hydrological processes (Stuart and Stocks, 1993). Figure 3 represents combined knowledge of Remote Sensing (RS) with the Global Positioning System (GPS) and Geographical Information System (GIS) can be
  • 4. HBRP Publication Page 29-41 2023. All Rights Reserved Page 32 Journal of Water Resource Research and Development Volume 6 Issue 1 DOI: https://doi.org/10.5281/zenodo.7878771 applicable to land use, soil moisture, rainfall, evapotranspiration, availability of surface and groundwater, and the use that could be used in the areas of snowmelt- runoff forecasts, irrigation performance evaluation, drought monitoring, watershed treatment, reservoir sedimentation, and flood mapping and management. Fig.3:-Contribution of RS and GIS in water resources management. Table 1, gives the track record of contributions in published form from the researchers in the field of “RS and GIS applications in water resources management”. It indicates that per decade, on average, 21,000 pieces of research were published. But in the last 3 years only, there have been 25000 and above publications available, which indicates that there has been a saturation of research topics in above mentioned subject. Therefore, it is the demand for the time that the researchers need to incorporate new innovative technologies in the fields of both RS and GIS and water resource management. Table 1:-Duration and number of publications in the field of RS & GIS applications in water resources management. Sr. No. Duration Number of Publications* 1 1990- 2000 21,700 2 2001-2010 21,400 3 2011-2020 24,700 4 2021-2023 25, 500 *Data generated from google scholar publications per year COMBINATION OF AI AND GIS AI (Artificial Intelligence) and GIS (Geographic Information Systems) can be used together for water resources management to improve the efficiency and effectiveness of decision-making processes. Figure-4 depicts the step wise application of combined AI and GIS in water resources management: Fig.4:- step-wise process for working with the combination of AI and GIS.
  • 5. HBRP Publication Page 29-41 2023. All Rights Reserved Page 33 Journal of Water Resource Research and Development Volume 6 Issue 1 DOI: https://doi.org/10.5281/zenodo.7878771 Step-1 Predictive Modelling: AI can be used to develop predictive models to forecast water demand, water quality, and flood events. These models can be integrated into GIS to provide spatially referenced information for decision- making. Step-2 Real-time Monitoring: AI can be used to analyse real-time data from sensors and other monitoring systems, such as satellite imagery, to detect changes in water quality, flow rates, and weather patterns. GIS can then be used to visualize and analyse this data in a spatial context. Step-3 Data Integration: AI can be used to integrate data from multiple sources, such as weather data, hydrological models, and social media, to provide a more comprehensive understanding of water resources. GIS can be used to store and analyse this data, allowing for better decision-making. Step-4 Decision Support: AI can be used to provide decision support for water resources management by analysing data, developing scenarios, and recommending actions. GIS can be used to visualize the results of these analyses in a spatial context, allowing for better communication among stakeholders. Step-5 Resource Allocation: AI can be used to optimize the allocation of water resources, such as determining the most efficient use of irrigation water in agriculture. GIS can be used to map the location of water resources and to analyse the spatial distribution of demand. The integration of AI and GIS can provide a powerful tool for water resources management, allowing for better decision- making, improved efficiency, and effective communication among stakeholders. AI IN PREDICTIVE MODELLING OF WATER RESOURCES AI (Artificial Intelligence) can be used for predictive modelling of water resources to forecast water availability, water quality, and flood events. Here are some examples of how AI can be used in predictive modelling of water resources (figure-5): Fig.5:-AI based predictive modelling of water resources management Machine Learning Algorithms: AI can use machine learning algorithms to analyse large datasets of historical water resource data and to develop predictive models based on past patterns and trends. These models can then be used to forecast future water availability, water quality, and flood events. Remote Sensing: AI can use remote sensing data, such as satellite imagery, to monitor water resources and to detect changes in water availability and quality. Machine learning algorithms can then be used to analyse this data and to develop predictive models. Real-time Monitoring: AI can use real-time monitoring data from sensors and other monitoring systems to detect changes in water availability and quality. Machine learning algorithms can then be used to develop predictive models based on this data. Climate Data: AI can use climate data to predict future weather patterns and to forecast the impacts of climate change on
  • 6. HBRP Publication Page 29-41 2023. All Rights Reserved Page 34 Journal of Water Resource Research and Development Volume 6 Issue 1 DOI: https://doi.org/10.5281/zenodo.7878771 water resources. Machine learning algorithms can then be used to develop predictive models based on these predictions. Decision Support: AI can use predictive modelling to provide decision support for water resources management. By providing forecasts of water availability, quality, and flood events, AI can help decision-makers to plan and allocate water resources more effectively. AI can provide a powerful tool for predictive modelling of water resources, allowing for better decision-making, improved efficiency, and effective communication among stakeholders. REAL-TIME MONITORING OF WATER RESOURCES AI (Artificial Intelligence) can be used for real-time monitoring of water resources to detect changes in water quality, quantity, and flow patterns. Here are some instances of how artificial intelligence can be used to monitor the availability of water in real- time: Sensor Networks: AI can be used to analyse real-time data from sensors installed in water bodies, pipes, and treatment plants to detect changes in water quality and quantity. Machine learning algorithms can then be used to identify patterns and anomalies in the data. Remote Sensing: AI can use remote sensing data, such as satellite imagery, to monitor water resources from a distance. Machine learning algorithms can then be used to analyse this data and to detect changes in water quality and quantity. Smart Metering: AI can be used to analyse real-time data from smart meters installed in households and commercial buildings to detect changes in water usage patterns. Machine learning algorithms can then be used to develop predictive models of water demand. Predictive Analytics: AI can use predictive analytics to forecast water quality and quantity based on real-time monitoring data. Machine learning algorithms can then be used to provide early warnings of potential water quality issues or water scarcity. Decision Support: AI can use real-time monitoring data to provide decision support for water resources management. By providing timely and accurate information, AI can help decision-makers to respond quickly to changes in water resources. AI can provide a powerful tool for real- time monitoring of water resources, allowing for better decision-making, improved efficiency, and effective communication among stakeholders. DATA INTEGRATION OF WATER RESOURCES AI (Artificial Intelligence) can be used for data integration of water resources by combining data from multiple sources, such as weather data, hydrological models, and social media, to provide a more comprehensive understanding of water resources. Figure-6 explains the accumulation of data from various sources and then it is refined into the AI based environment. Fig.6:-Data integration in AI environment
  • 7. HBRP Publication Page 29-41 2023. All Rights Reserved Page 35 Journal of Water Resource Research and Development Volume 6 Issue 1 DOI: https://doi.org/10.5281/zenodo.7878771 Machine Learning Algorithms: AI can use machine learning algorithms to analyse large datasets of water resource data and to identify patterns and trends. Machine learning can then be used to integrate this data with other sources of information. Social Media Analysis: AI can be used to analyse social media data, such as Twitter feeds, to gather information on water quality and quantity issues. Machine learning algorithms can then be used to integrate this data with other sources of information. Internet of Things (IoT): AI can be used to analyse data from IoT sensors installed in water bodies, pipes, and treatment plants. Machine learning algorithms can then be used to integrate this data with other sources of information. Weather Data: AI can use weather data to forecast water availability and to predict the impacts of climate change on water resources. Machine learning algorithms can then be used to integrate this data with other sources of information. Decision Support: AI can use data integration to provide decision support for water resources management. By integrating data from multiple sources, AI can provide decision-makers with a more comprehensive understanding of water resources. AI can be a strong tool for water resource data integration, facilitating better decision-making, increased efficiency, and effective communication among stakeholders. AI IN WATER RESOURCE ALLOCATION AI (Artificial Intelligence) can be used to allocate water resources by delivering accurate and timely information on water availability, demand, and quality to decision-makers. Figure-7 delineates the allocation of water resources data, processed under AI environment and how it can be applicable to modern usage of AI as modern tool for water resources allocation. Fig.7:-AI based water resources allocation Predictive Analytics: AI can use predictive analytics to forecast water demand and to identify potential supply shortages. Machine learning algorithms can then be used to develop water allocation plans based on this information. Optimization Models: AI can use optimization models to identify the most efficient and effective ways to allocate water resources. Machine learning algorithms can then be used to optimize water allocation plans based on real-time data. Smart Metering: AI can be used to analyse water usage data from smart meters installed in households and commercial buildings. Machine learning algorithms can then be used to develop personalized water allocation plans based on usage patterns.
  • 8. HBRP Publication Page 29-41 2023. All Rights Reserved Page 36 Journal of Water Resource Research and Development Volume 6 Issue 1 DOI: https://doi.org/10.5281/zenodo.7878771 Real-time Monitoring: AI can use real- time monitoring data from sensors and other monitoring systems to detect changes in water availability and quality. Machine learning algorithms can then be used to adjust water allocation plans based on this data. Decision Support: AI can use water resource allocation models to provide decision support for water resources management. By providing accurate and timely information on water availability and demand, AI can help decision-makers to allocate water resources more effectively. AI IN WATER RESOURCES MANAGEMENT DECISION SUPPORT Decision support system for water resources management is an interactive field where knowledge of water resources engineering, meteorology, climatology, geology, mathematics, computer programming, and relevant software’s. The factors affecting DSS are very large in number hence the database generated from various fields is quantitative but may not be qualitative for further application. To validate data mathematical operations such as statistical analysis, and probabilistic methods are adopted. Computer tools and software are used to facilitate such operations. While doing such operations also for data operations, further decision- making is required. To minimize manual involvement in data operations, the software uses algorithms and they take their own decision and proceed further to carry out mathematical operations. AI (Artificial Intelligence) can be used for decision support in water management by providing decision-makers with accurate and timely information on water resources. Here are some examples of how AI can be used in decision support for water management: Predictive Analytics: AI can use predictive analytics to forecast water availability and demand, and to identify potential water quality issues. Machine learning algorithms can then be used to develop water management strategies based on this information. Optimization Models: AI can use optimization models to identify the most efficient and effective ways to manage water resources. Machine learning algorithms can then be used to optimize water management plans based on real- time data. Real-time Monitoring: AI can use real- time monitoring data from sensors and other monitoring systems to detect changes in water availability and quality. Machine learning algorithms can then be used to adjust water management plans based on this data. Risk Assessment: AI can use risk assessment models to identify potential risks to water resources, such as drought, flooding, or contamination. Machine learning algorithms can then be used to develop contingency plans to mitigate these risks. Decision Support Systems: AI can use decision support systems to provide decision-makers with timely and accurate information on water resources. By providing a comprehensive overview of water resources, decision-makers can make more informed decisions about water management strategies. FUTURE OF AI AND GIS BASED WATER RESOURCES MANAGEMENT The future of AI (Artificial Intelligence) and GIS (Geographic Information Systems) in water resources management looks very promising. Here are some potential developments that could shape
  • 9. HBRP Publication Page 29-41 2023. All Rights Reserved Page 37 Journal of Water Resource Research and Development Volume 6 Issue 1 DOI: https://doi.org/10.5281/zenodo.7878771 the future of AI and GIS-based water resources management: Increased Data Integration: As more data becomes available from various sources such as IoT sensors, remote sensing satellites, and social media, AI and GIS- based water resources management will become more data-intensive. With machine learning algorithms, data can be analysed and integrated into decision- making models for water resource management. Advanced Predictive Analytics: With the help of AI, advanced predictive analytics models can be developed to forecast water availability and demand, detect anomalies and changes in water quality, and identify potential risks and hazards in real-time. This will enable water resource managers to take proactive measures to address issues before they become significant problems. Enhanced Decision Support Systems: AI can be used to develop more advanced and efficient decision support systems for water resource management. These systems will provide decision-makers with real-time and accurate information on water resources, enabling them to make more informed decisions that lead to better outcomes. Increased Automation: AI can be used to automate routine tasks such as data processing, analysis, and reporting. This will free up water resource managers to focus on more critical tasks such as strategic planning and decision-making. Improved Efficiency and Sustainability: With AI and GIS-based water resources management, we can expect more efficient and sustainable management practices. This will help conserve and manage water resources more effectively, while minimizing waste and reducing environmental impacts. The future of AI and GIS-based water resources management is very exciting. As technology advances, we can expect to see more efficient, sustainable, and effective management practices that will help address the growing challenges of water resource management. GEO-INTELLIGENCE Geo-intelligence is a broad word that encompasses geospatial visualization, analysis, decision-making, design, and control based on GIS, remote sensing, and satellite positioning technologies. The most distinctive feature that distinguishes GIS from other data technologies is geo- intelligence. It has four stages (figure-8) that contribute to the Geo-intelligence pyramid: Geo-visualization, Geo-decision, Geographic-design, and Geo-control. The complexity of the pyramid increases from bottom to top, while maturity decreases. Geo-intelligence will usher in a new era of technological innovation and create more value with the advent of AI. Fig.8:- AI Enhanced Geo-Intelligence
  • 10. HBRP Publication Page 29-41 2023. All Rights Reserved Page 38 Journal of Water Resource Research and Development Volume 6 Issue 1 DOI: https://doi.org/10.5281/zenodo.7878771 The basis of GeoAI (AI Enhanced Geo- Intelligence) starts with visualization followed by decision, design and control over AI Intelligence. (Figure-8) The complexity is low at the base of pyramid and it is on the most complex stage when it reached to control level. On the contradiction the control stage is less developed at control level and highest at the visualization stage. AI for GIS AI for GIS refers to the application of artificial intelligence (AI) technology to improve the intelligence of GIS software, such as AI Attribute Collecting, AI Survey & Mapping, AI Cartography, and AI interaction. AI Attribute Collecting can contribution users in intelligently classifying and classifying multi-source targets such as video images; AI Survey & Mapping can provide lower cost and more convenient indoor mapping solutions; AI Cartography saves users from the time- consuming process of manual mapping, and style transfer from image to map can be realised through simple operation; AI interaction includes rich application interaction such as voice, gestures, body, and so on. Fig.9:- AI-GIS Technology for Water resources management GIS for AI When confronted with simulated intelligence acknowledgment results, GIS can additionally process and mine information by utilizing its spatial representation and spatial examination abilities, that is to say, GIS engages computer based intelligence. Applications for map visualization such as traffic flow monitoring, city component management, and cases can offer decision makers a method of information expression that is easier to understand; while inside and out handling and mining of simulated intelligence extraction results can empower constant geo-wall cautions and vehicle following, etc to additionally grow applications. A relatively complete AI GIS technology system can be constructed using a four- layer structure from the bottom up to effectively support the three components of AI GIS. The domain library follows the data layer at the bottom. Several AI frameworks can be reasonably abstracted and encapsulated in the framework layer. The three parts of the AI GIS that are specifically introduced constitute the top functional layer. AI GIS Future An effective solution to the problem of the current intelligent GIS system is to use AI GIS to enhance and develop the next generation of GIS technology systems. AI GIS was the first to use computer vision to
  • 11. HBRP Publication Page 29-41 2023. All Rights Reserved Page 39 Journal of Water Resource Research and Development Volume 6 Issue 1 DOI: https://doi.org/10.5281/zenodo.7878771 extract geographic data from videos and photos taken with remote sensing equipment. Moreover, artificial intelligence advances, for example, discourse acknowledgment and language handling can be executed, considering more prominent strengthening. In any case, computer based intelligence GIS is still in the Tight computer based intelligence stage and is quite far from general simulated intelligence (AGI). Consequently, general AI technology is an important path for AI GIS development in the future. Fig.10:- Gartner Hype Cycle for AI (2019) (Reference: https://www.supermap.com/en-us/news/?82_2701.html) ADVANTAGES OF COMBINATION OF AI AND GIS The combination of AI (Artificial Intelligence) and GIS (Geographic Information Systems) can provide several advantages in various fields, including water resources management. Here are some of the advantages of the combination of AI and GIS: Improved Spatial Analysis: GIS allows for the analysis of spatial data, while AI can be used to develop more advanced and sophisticated analytical models. Together, AI and GIS can provide more accurate and efficient spatial analysis, such as land use classification, land cover change detection, and habitat mapping. Enhanced Data Management: With the help of GIS, AI can be used to analyse, process, and manage large volumes of geospatial data. This can be especially useful for water resource management, where large amounts of data from various sources need to be analysed and integrated into decision-making models. Better Predictive Analytics: AI can be used to develop more advanced predictive models, while GIS can provide the spatial context necessary for such models. By combining AI and GIS, we can develop more accurate and reliable predictive models for water resource management, such as flood forecasting, drought prediction, and water quality modelling. Improved Decision-making: The combination of AI and GIS can provide decision-makers with real-time and accurate information on water resources. This can help inform better decision- making, such as water allocation, infrastructure planning, and disaster management. Increased Efficiency: By automating routine tasks, such as data processing and analysis, the combination of AI and GIS can help increase efficiency in water resource management. This can help save
  • 12. HBRP Publication Page 29-41 2023. All Rights Reserved Page 40 Journal of Water Resource Research and Development Volume 6 Issue 1 DOI: https://doi.org/10.5281/zenodo.7878771 time and resources, and enable decision- makers to focus on more critical tasks. The combination of AI and GIS provides a powerful tool for water resource management. By integrating advanced analytical models and spatial data, AI and GIS can help inform better decision- making, improve efficiency, and enable more sustainable management practices. CONCLUSION This paper presents a survey and an unpleasant classification of various sorts of utilizations of artificial intelligence in the water space. Modeling, prediction and forecasting, decision support and operational management, and optimization fall into these categories. The reviewed literature on AI in the water domain and the most recent literature on AI and ethics show that the ethical aspects of AI applications in the water domain receive little attention. Late writing on simulated intelligence and morals proposes a rundown of five rules that ought to be considered while creating artificial intelligence applications. Transparency, fairness and justice, accountability and responsibility, privacy, and non-malice are all examples of these. When compared to its use in other industries, AI's application in the water domain remains somewhat limited. This presents an opportunity to avoid and gain knowledge from past errors. Insights regarding AI-based applications of the water resources domain have been discussed in this paper. AI has the potential to improve theory-guided data science and steer clear of some of the drawbacks of strictly data-driven models. Luckily, man-made intelligence is progressively perceived and there is more than adequate involvement in participatory dynamic in the water space. Water professionals, data scientists, and experts from the social and humanities are required to collaborate on the development and implementation of responsible AI techniques for the water resource domain. REFERENCES 1. Reddy K.S.(2021). Water Resources management, Training manual. Ministry of Housing and Urban Affairs Government of India NIUA:9. 2. Parekh, A, Patel, AS, Parmar, S, Patel, V. Web usage mining: frequent pattern generation using association rule mining and clustering. Int. J. Eng. Res. Technol. 4 (4):1243-1246. 3. Shamsi, U. M. S. (1999). GIS and water resources modeling: state-of- the-art. Journal of Water Management Modeling. 4. McKinney, D. C., & Cai, X. (2002). Linking GIS and water resources management models: an object- oriented method. Environmental Modelling & Software, 17(5), 413- 425. 5. Fedra, K. (1996). Distributed models and embedded GIS: integration strategies and case studies. GIS and environmental modeling: Progress and research issues, 413-418. 6. Watkins Jr, D. W., & McKinney, D. C. (1995). Recent developments associated with decision support systems in water resources. Reviews of Geophysics, 33(S2), 941-948.. 7. Belaineh, G., Peralta, R. C., & Hughes, T. C. (1999). Simulation/optimization modeling for water resources management. Journal of water resources planning and management, 125(3), 154-161.. 8. Bozorg-Haddad, O. (Ed.). (2021). Essential Tools for Water Resources Analysis, Planning, and Management. Springer. 9. IWRM (2019) Integrated Water Resources Management Data Portal. http://iwrmdataportal.unepdhi.org/
  • 13. HBRP Publication Page 29-41 2023. All Rights Reserved Page 41 Journal of Water Resource Research and Development Volume 6 Issue 1 DOI: https://doi.org/10.5281/zenodo.7878771 10. Parmar S.P(2011), RS and GIS in solid waste management in urban area: A case study Vijay colony ward, Dehradun, National Conference.:1-12. 11. Swain NR, Latu K, Christensen SD, Jones NL, Nelson EJ, Ames DP, Williams GP (2015) A review of open source software solutions for developing water resources web applications. Environ Model Softw. 67:108–117. 12. TsihrintzisVA, Hamid R, Fuentes HR (1996) Use of geographic information systems (GIS) in water resources: a review. Water Resour. Manage. 10:251–277. 13. Safavi HR, Golmohammadi MH, Sandoval-Solis S (2016) Scenario analysis for integrated water resources planning and management under uncertainty in the Zayandehrud river basin. J Hydrol. 539:625–639. 14. Neelke Doorn, Artificial intelligence in the water domain: Opportunities for responsible use, Science of the total environement 755(2021) Cite this article as: Samirsinh P Parmar. (2023). Water Resource Management Using Artificial Intelligence Enabled RS & GIS. Journal of Water Resource Research and Development, 6(1), 29–41. https://doi.org/10.5281/zenodo.7878771