Novel IoT &amp; Machine Learning Base Water Flow Monitoring System

Bharadwaj, Kanakadandi Venkata Chaitanya; Bharadwaj, Dusarlapudi Kalyan; Sudhakar, Kattupalli; Polasi, Venkata Satya Kireeti; Tiruvuri, Charan Sai; Raju, K. Narasimha

doi:10.1109/mysurucon55714.2022.9972658

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…However, instead of real-time display and online acquisition, it utilizes an LED screen to display data. To address this issue, the IoT technology is utilized to monitor the data and summarize it for cloud-based data display [ 30 ]. To improve the efficiency of field monitoring network management, an improved algorithm [ 31 ] has been proposed.…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of a Li River water quality monitoring and analysis system based on outlier data analysis

Lu,

Zou,

et al. 2024

PLoS ONE

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The detection of water quality indicators such as Temperature, pH, Turbidity, Conductivity, and TDS involves five national standard methods. Chemically based measurement techniques may generate liquid residue, causing secondary pollution. The water quality monitoring and data analysis system can effectively address the issues that conventional methods require multiple pieces of equipment and repeated measurements. This paper analyzes the distribution characteristics of the historical data from five sensors at a specific time, displays them graphically in real time, and provides an early warning of exceeding the standard; It selects four water samples from different sections of the Li River, based on the national standard method, the average measurement errors of Temperature, PH, TDS, Conductivity and Turbidity are 0.98%, 2.23%, 2.92%, 3.05% and 3.98%.;It further uses the quartile method to analyze the outlier data over 100,000 records and five historical periods are selected. Experiment results show the system is relatively stable in measuring Temperature, PH and TDS, and the proportion of outlier is 0.42%, 0.84% and 1.24%. When Turbidity and Conductivity are measured, the proportion is 3.11% and 2.92%. In the experiment of using 7 methods to fill outlier, K nearest neighbor algorithm is better than others. The analysis of data trends, outliers, means, and extreme values assists in making decisions, such as updating and maintaining equipment, addressing extreme water quality situations, and enhancing regional water quality oversight.

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Section: Introductionmentioning

confidence: 99%

Design and implementation of a Li River water quality monitoring and analysis system based on outlier data analysis

Lu,

Zou,

et al. 2024

PLoS ONE

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Water Quality Monitoring Station Through Nitrate Measuring with IoT

Villalobos-Villegas,

Carrasquilla-Batista,

Hernández-Alpízar

2023

2023 IEEE 5th International Conference on BioInspired Processing (BIP)

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Data-Driven Aquatics

Dankan Gowda,

Sharma,

Tanguturi

et al. 2023

Innovations in Machine Learning and IoT for Water Management

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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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Novel IoT & Machine Learning Base Water Flow Monitoring System

Cited by 3 publications

References 15 publications

Design and implementation of a Li River water quality monitoring and analysis system based on outlier data analysis

Design and implementation of a Li River water quality monitoring and analysis system based on outlier data analysis

Water Quality Monitoring Station Through Nitrate Measuring with IoT

Data-Driven Aquatics

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