Water management is a major issue already addressed in most
international forums. Water harvesting and recycling are critical criteria for
meeting the per capita availability of water (Krishna et.al., 2008). In this
regard, we must focus on water management approaches that can be easily
implemented across a wide range of applications (Benos et.al., 2021). Amid
population increase and varied challenges, there is an urgent need to establish
intelligent water management mechanisms for the effective distribution,
conservation, and maintenance of water (Safder et.al., 2022). The present work
highlights a few important application areas that are essential for precision
water management in which artificial neural network (ANN), recurrent neural
network (RNN) and random Forest (RF) are some of the most useful developments
in machine learning (ML) models (Mokhtari et.al.,2020) in different aspects of
water such as wastewater recycling, water distribution, rainfall estimation and
irrigation water management that can be used to predict the future scenario. As
a result, there is an urgent need to generate datasets and models/algorithms
that can be used to deliver solutions for the above-mentioned applications.
Machine learning architecture can aid in the development of a smart model for
the sustainable use of natural resources (Lowe et.al.,2022), as well as the
usage of AI/ML in conjunction with different neural network models and simple
statistical analysis to create an effective water management framework to deal
all water-related problems.
Author(s) Details:
Aditya V Machnoor,
Discipline of Water Science and Technology, Water Technology Centre,
ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India.
Ajayakumar
Department of Agricultural Economics, University of Agricultural Sciences,
Bengaluru-560065, Karnataka, India.
Mallanna Malagatti
Discipline of Seed Science and Technology, ICAR-Indian Agricultural
Research Institute, New Delhi 110012, India.
Please see the link here: https://stm.bookpi.org/STRUFP-V1/article/view/14325
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