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Data-Driven Aquatics: The Future of Water Management With IoT and Machine Learning
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Author(s): V. Dankan Gowda (BMS Institute of Technology and Management, India), Anil Sharma (Amity School of Engineering and Technology, Amity University, Noida, India), Rama Chaithanya Tanguturi (PACE Institute of Technology and Sciences, India), K. D. V. Prasad (Symbiosis Institute of Business Management, Symbiosis International University, India)and Vasifa Sameer Kotwal (Dr. D.Y. Patil Polytechnic, Kolhapur, India)
Copyright: 2024
Pages: 23
Source title:
Innovations in Machine Learning and IoT for Water Management
Source Author(s)/Editor(s): Abhishek Kumar (Chandigarh University, India), Arun Lal Srivastav (Chitkara University, India), Ashutosh Kumar Dubey (University of Castilla-La Mancha, Spain), Vishal Dutt (AVN Innovations Pvt. Ltd., India)and Narayan Vyas (AVN Innovations Pvt. Ltd., India)
DOI: 10.4018/979-8-3693-1194-3.ch009
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Abstract
The water management industry has undergone a sea change since the advent of machine learning (ML) and internet of things (IoT) technology. In this chapter, the utilization of ML and IoT applications for assisting with the fundamentals of water management data gathering and preprocessing will be explored. In order to make educated decisions toward water sustainability, sensors and gadgets connected to the IoT have improved monitoring and evaluation of water resources. In the initial paragraphs, the primary focus of the chapter is introduced: the importance of data collection in water management and the challenges of using traditional data collection techniques. However, before the data acquired from these sensors can be used for analysis and modeling, it must frequently undergo some form of preprocessing. Important data preparation tasks including data cleansing, outlier identification, and data fusion are discussed in this chapter. The reliability of future ML algorithms is enhanced by preprocessing the data to verify its correctness and consistency.
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