IoT Based Water Quality Monitoring System for Safe Drinking Water in Pakistan

2020 ◽  
Author(s):  
Abdul Rauf Memon ◽  
Saadia Kulsoom Memon ◽  
Abdul Aziz Memon ◽  
Tayab Din Memon
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Monitoring System ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Safe Drinking Water

scholarly journals Iot Based Water Quality Monitoring System using Machine Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 11801-11805
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Drinking Water ◽  
Distributed Computing ◽  
Monitoring System ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Web Of Things ◽  
Water Condition ◽  
Minimal Effort

In the present occasions, because of urbanization and contamination, it has gotten important to screen and assess the nature of water arriving at our homes. Guaranteeing safe inventory of drinking water has become a major test for the cutting edge progress. In this desk work, we present a structure and improvement of a minimal effort framework for continuous checking of the water quality (WQ) in IoT (web of things). The framework comprise of a few sensors are accustomed to guesstimatingsomatic and element limitations of the water. The parameters like temperature, PH, turbidity, conductivity, broke up oxygen of the water can be estimated. The deliberate qualities from the sensors can be prepared by the center controller. The RBPI B+ (RBPI) model can be consumed as a center controller. At last, the instrument facts can be understood on web utilizing distributed computing. Here the information's are handled utilizing AI calculation it sense the water condition if the WQis great it open the entryway divider else it shuts the door divider. This whole procedure happens naturally without human mediation therefore spare an opportunity to contract with the circumstance physically. The uniqueness of our proposed research is to get the water observing framework with high recurrence, high portability, and low controlled.

scholarly journals An Open Source Water Quality Measurement System for Remote Areas

2021 ◽  
Vol 12 (1) ◽  
pp. 50
Author(s):  
Muhammad Owais Tariq ◽  
Asif Siddiq ◽  
Hafsa Irshad ◽  
Muhammad Aman ◽  
Muhammad Shahbaz Khan
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Monitoring System ◽  
Ph Value ◽  
Water Quality Monitoring ◽  
Ion Concentration ◽  
Quality Monitoring ◽  
World Health ◽  
Average Error ◽  
Remote Areas

The unavailability of safe drinking water leads to poor conditions related to mental and physical health. To quantify the quality of water, laboratories testing the water are present in major cities which assess the basic quality parameters of drinking water, e.g., total dissolved salts (TDS), ion concentration (conductivity), turbidity, and pH value as recommended by the World Health Organization (WHO). The unavailability of such testing laboratories at remote locations makes the testing of the drinking water difficult. Establishing such laboratories is a tedious job as it requires a lot of costly equipment and specially trained personnel to operate them, making them difficult to handle. To address these issues, a water quality monitoring system for remote areas was designed which is capable of measuring basic measurable qualities of salt concentration, ion concentration, turbidity, and pH value. With the utilization of such a system, the user can qualify the water present in the vicinity as safe or unsafe for drinking purposes. The results from the proposed system are evaluated based on standard testing results and it is found that our water quality monitoring system is in agreement with the standard lab results with an average error of 2.9%, 1.4%, 1.2%, and 1.2% for pH, turbidity, conductivity, and TDS, respectively.

Methodological Approaches to Organization of Drinking Water Quality Monitoring Programs

2020 ◽  
pp. 4-8
Author(s):  
Yu.A. Novikova ◽  
I.O. Myasnikov ◽  
A.A. Kovshov ◽  
N.A. Tikhonova ◽  
N.S. Bashketova
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Water Supply ◽  
Cold Water ◽  
Water Quality Monitoring ◽  
Drinking Water Quality ◽  
Quality Monitoring ◽  
Water Supply Systems ◽  
Monitoring Programs ◽  
Supply Systems

Summary. Introduction: Drinking water is one of the most important environmental factors sustaining life and determining human health. The goal of the Russian Federal Clean Water Project is to improve drinking water quality through upgrading of water treatment and supply systems using advanced technologies, including those developed by the military-industrial complex. The most informative and reliable sources of information for assessing drinking water quality are the results of systematic laboratory testing obtained within the framework of socio-hygienic monitoring (SGM) and production control carried out by water supply organizations. The objective of our study was to formulate approaches to organizing quality monitoring programs for centralized cold water supply systems. Materials and methods: We reviewed programs and results of drinking water quality laboratory tests performed by Rospotrebnadzor bodies and institutions within the framework of SGM in 2017–2018. Results: We established that drinking water quality monitoring in the constituent entities of the Russian Federation differs significantly in the number of monitoring points (566 in the Krasnoyarsk Krai vs 10 in Sevastopol) and measured indicators, especially sanitary and chemical ones (53 inorganic and organic substances in the Kemerovo Region vs one indicator in the Amur Region). Discussion: For a more complete and objective assessment of drinking water quality in centralized cold water supply systems, monitoring points should be organized at all stages of water supply with account for the coverage of the maximum number of people supplied with water from a particular network. Thus, the number of points in the distribution network should depend, inter alia, on the size of population served. In urban settlements with up to 10,000 inhabitants, for example, at least 4 points should be organized while in the cities with more than 3,000,000 inhabitants at least 80 points are necessary. We developed minimum mandatory lists of indicators and approaches to selecting priority indices to be monitored at all stages of drinking water supply.

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

Sensors ◽  
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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