A REVIEW ON TECHNIQUES FOR WATER QUALITY MONITORING USING IOT DEVICES

In this paper is discussed the different kinds of environment monitoring systems related to water goodness. Different parameters are discussed to elaborate each water monitoring system with different aspects. The technology aspects of different approach techniques related to water quality monitoring with their way of implementation with the IOT aspect has been evaluated of past four years. In this paper, the workflow of different approaches of the technologies exploited will be discussed critically and also that which approaches focused on what parameters.

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Study of IoT Based Smart Water Quality Monitoring System

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.37483 ◽

2021 ◽

Vol 9 (VIII) ◽

pp. 794-799

Author(s):

Shubham Srivastava

Keyword(s):

Water Quality ◽

Monitoring System ◽

Traditional Approach ◽

Water Quality Monitoring ◽

Quality Parameter ◽

Quality Monitoring ◽

Water Quality Parameter ◽

Monitoring Systems ◽

Smart Water ◽

Parameter Monitoring

IoT based water quality parameter monitoring system is a significant interest in the field of cost-effective smart water quality monitoring systems. As we know that the population growth of your country is high in last few decades. In India, the demand for freshwater for drinking purposes, agriculture, and other activities is much higher than compared to other countries. The requirement of a smart water quality parameter monitoring system is necessary to reduce the time required in the traditional approach of water quality monitoring, and for real time monitoring. This literature survey work has been conducted in the field of smart water quality parameter monitoring systems. Sensor-based smart water quality parameter monitoring in past some research carried out which is deployed in the water.

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A Low-Cost Multi-Parameter Water Quality Monitoring System

Sensors ◽

10.3390/s21113775 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3775

Author(s):

Arif Ul Alam ◽

Dennis Clyne ◽

M. Jamal Deen

Keyword(s):

Water Quality ◽

Monitoring System ◽

Electrochemical Sensors ◽

Limit Of Detection ◽

Low Cost ◽

Water Quality Monitoring ◽

Water Monitoring ◽

Quality Monitoring ◽

Sensor Data ◽

Skilled Workers

Multi-parameter water quality monitoring is crucial in resource-limited areas to provide persistent water safety. Conventional water monitoring techniques are time-consuming, require skilled personnel, are not user-friendly and are incompatible with operating on-site. Here, we develop a multi-parameter water quality monitoring system (MWQMS) that includes an array of low-cost, easy-to-use, high-sensitivity electrochemical sensors, as well as custom-designed sensor readout circuitry and smartphone application with wireless connectivity. The system overcomes the need of costly laboratory-based testing methods and the requirement of skilled workers. The proposed MWQMS system can simultaneously monitor pH, free chlorine, and temperature with sensitivities of 57.5 mV/pH, 186 nA/ppm and 16.9 mV/°C, respectively, as well as sensing of BPA with <10 nM limit of detection. The system also provides seamless interconnection between transduction of the sensors’ signal, signal processing, wireless data transfer and smartphone app-based operation. This interconnection was accomplished by fabricating nanomaterial and carbon nanotube-based sensors on a common substrate, integrating these sensors to a readout circuit and transmitting the sensor data to an Android application. The MWQMS system provides a general platform technology where an array of other water monitoring sensors can also be easily integrated and programmed. Such a system can offer tremendous opportunity for a broad range of environmental monitoring applications.

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Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine

Sensors ◽

10.3390/s21124118 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4118

Author(s):

Leonardo F. Arias-Rodriguez ◽

Zheng Duan ◽

José de Jesús Díaz-Torres ◽

Mónica Basilio Hazas ◽

Jingshui Huang ◽

...

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Extreme Learning Machine ◽

Monitoring System ◽

Field Measurements ◽

Water Quality Monitoring ◽

Quality Monitoring ◽

Chl A ◽

Learning Machine ◽

Level 2

Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2=0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.

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