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Wireless Sensor Networks (WSNs)-Integrated Machine Learning Algorithms for Water Resource Management
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Author(s): Ashay Devidas Shende (Department of Civil Engineering, K.D.K. College of Engineering, Nagpur, India), J. Bibiana Jenifer (Department of Information Science and Engineering, New Horizon College of Engineering, Bengaluru, India), Gururaj L. Kulkarni (Department of Computer Science and Engineering, Vardhaman College of Engineering, Hyderabad, India), Amar Choudhary (Department of Electronics and Communication Engineering, Alliance College of Engineering and Design, Bengaluru, India), Aparajita Mukherjee (Department of Computer Science and Engineering, IEM Newtown Campus, Kolkata, India)and Sampath Boopathi (Department of Mechanical Engineering, Muthayammal Engineering College, Namakkal, India)
Copyright: 2025
Pages: 34
Source title:
Enhancing Data-Driven Electronics Through IoT
Source Author(s)/Editor(s): Bhagwan Das (Centre for Artificial Intelligence Research and Optimization (AIRO), Torrens University Australia, Australia), Muhammad Zakir Shaikh (Universidad de Málaga, Spain), Samreen Hussain (Dawood University of Engineering and Technology, Pakistan)and Enrique Nava Baro (Universidad de Málaga, Spain)
DOI: 10.4018/979-8-3693-5448-3.ch017
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Abstract
The integration of Wireless Sensor Networks (WSNs) with advanced machine learning algorithms is revolutionizing water resource management. This chapter delves into the synergistic application of these technologies to address critical challenges in monitoring and managing water resources. It begins with an introduction to WSN technology, emphasizing its role in real-time data collection for water quality monitoring, irrigation systems, and flood detection. The chapter then explores various machine learning algorithms, such as Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), highlighting their applications in predictive analytics and anomaly detection. The chapter discusses successful implementations of WSNs and machine learning in water distribution and proactive infrastructure maintenance, emphasizing the importance of accurate data interpretation for informed decision-making in real-time data analysis techniques.
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