Water management using genetic algorithm-based machine learning

Water scarcity is the major problem presently being faced globally; it is to be managed in an efficient manner. The water management procedure is one of the techniques to condense necessary water. The objective of water management system is that water supply agency to collect, distribute quality wat...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2020-11, Vol.24 (22), p.17153-17165
Main Authors: Gino Sophia, S. G., Ceronmani Sharmila, V., Suchitra, S., Sudalai Muthu, T., Pavithra, B.
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computational Intelligence
Control
Dams
Decision making
Engineering
Genetic algorithms
Groundwater
Infrastructure
Machine learning
Mathematical analysis
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Neural networks
Robotics
Support vector machines
Water consumption
Water management
Water quality
Water resources
Water supply
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Summary:Water scarcity is the major problem presently being faced globally; it is to be managed in an efficient manner. The water management procedure is one of the techniques to condense necessary water. The objective of water management system is that water supply agency to collect, distribute quality water without any delay and scarcity. An intelligent system is necessary for efficient production, collection and distribution. The proposed intelligent system consists of genetic operations with fitness value and neural network for training. The fitness function is used to make new intelligent members from existing population of water resources for water collection and distribution. The system is applicable for prediction about water consumption, distribution using decision-making algorithms to increase optimization performance by calculation of objective function of various population types. The regression performance of proposed intelligent system is calculated and compared with other algorithms.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-020-05009-0