The deployment of Machine Learning (ML) for improving Water Treatment Plants (WTPs) predictive maintenance is investigated in the present article. Proactively detecting and fixing functional difficulties which might cause catastrophic effects has historically been an endeavour for reactive or schedule-based maintenance methods. Anomaly Detection (AD) in WTP predictive maintenance frameworks is the primary goal of this investigation, which recommends a novel approach based on autoencoder (AE)-based ML models. For the objective of examining high-dimensional time-series sensor data collected from a WTP over a long time, Sparse Autoencoders (SAEs) are implemented. The data collected involves an array of operational measurements that, evaluated together, describe the plant's overall performance. With the support of the AE, this work aims to develop a practical framework for WTP operation predictive maintenance. Anomalies are all system findings from testing that might result in flaws or malfunctions. The research article analyses January and July 2023 WTP data from Jiangsu Province China. The AE paradigm had been evaluated using F1-scores, recall, accuracy, and precision. SAE has substantially improved AD functionality.

The processing, security, and storage of real-world applications have been made possible by recent developments in big computing. This study investigates the many methods and tools that have been created and used to raise the efficacy and efficiency of large-scale computing systems. The need to manage enormous volumes of data and carry out complicated computational operations in various industries, such as education, healthcare, finance, energy, retail, and transportation, is driving this development. Big computing's potential must be fully realized, so it's important to understand practical applications that might boost various companies’ bottom lines. This chapter examines current findings in the field of massive computing methods based on practical applications and covers the terms and procedures associated with big data in connection to a number of different factors, including performance data techniques, and the efficacy of current models used. The chapter ends with a summary of big data processing frameworks, future research objectives, and a discussion of the advantages and disadvantages of big data. <!----> <!----> <!----> <!---->