scholarly journals Water Quality Monitoring and Prediction of Water Quality at College Premises using Internet of Things

2019 ◽  
Vol 8 (5S3) ◽  
pp. 53-57
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Random Forest ◽  
Real Time ◽  
Multilayer Perceptron ◽  
Low Cost ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Machine Learning Techniques ◽  
Learning Techniques

IoT is becoming more popular and effective tool for any real time application. It has been involved for various water quality monitoring system to maintain the water hygiene level. The main objective is to build a system that regularly monitors the water quality and manages the sustainability. This system deals with specific standards like low cost background and system efficiency when compared to other studies. In this paper, IoT based real time monitoring of water quality system is implemented along with Machine learning techniques such as J48, Multilayer Perceptron (MLP), and Random Forest. These machine learning techniques are compared based on the hyper-parameters and the results were obtained. The attributes such as pH, Dissolved Oxygen (DO), turbidity, conductivity obtained from the corresponding sensors are used to create a prediction model which classifies the quality of water. Measurement of water quality and reporting system is implemented by using Arduino controller, GSM/GPRS module for gathering data in real time. The collected data are then analyzed using WEKA interface which is a visualization tool used for the analysis of data and prediction modeling.The Random forest technique outperforms J48 and Multilayer perceptron by giving 98.89% of correctly classified instances.

scholarly journals Water quality monitoring: from conventional to emerging technologies

2019 ◽  
Vol 20 (1) ◽  
pp. 28-45
Author(s):  
Umair Ahmed ◽  
Rafia Mumtaz ◽  
Hirra Anwar ◽  
Sadaf Mumtaz ◽  
Ali Mustafa Qamar
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Industrial Development ◽  
Low Cost ◽  
Research Work ◽  
Water Quality Monitoring ◽  
Machine Learning Techniques ◽  
Rapid Urbanization ◽  
Analysis Process ◽  
Learning Techniques

Abstract The rapid urbanization and industrial development have resulted in water contamination and water quality deterioration at an alarming rate, deeming its quick, inexpensive and accurate detection imperative. Conventional methods to measure water quality are lengthy, expensive and inefficient, including the manual analysis process carried out in a laboratory. The research work in this paper focuses on the problem from various perspectives, including the traditional methods of determining water quality to gain insight into the problem and the analysis of state-of-the-art technologies, including Internet of Things (IoT) and machine learning techniques to address water quality. After analyzing the currently available solutions, this paper proposes an IoT-based low-cost system employing machine learning techniques to monitor water quality in real time, analyze water quality trends and detect anomalous events such as intentional contamination of water.

Smart and Low Cost Real Time Water Quality Monitoring System Using IoT

2019 ◽  
Author(s):  
Jeba Anandh S ◽  
Anandharaj M ◽  
Aswinrajan J ◽  
Karankumar G ◽  
Karthik P
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Monitoring System ◽  
Low Cost ◽  
Water Quality Monitoring ◽  
Quality Monitoring

scholarly journals Real Time Efficient Accident Predictor System using Machine Learning Techniques (kNN, RF, LR, DT)

2020 ◽  
Vol 10 (2) ◽  
pp. 108-111
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Real Time ◽  
Classification Accuracy ◽  
Nearest Neighbors ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Classification Methods ◽  
K Nearest Neighbors ◽  
Learning Techniques

Real time crash predictor system is determining frequency of crashes and also severity of crashes. Nowadays machine learning based methods are used to predict the total number of crashes. In this project, prediction accuracy of machine learning algorithms like Decision tree (DT), K-nearest neighbors (KNN), Random forest (RF), Logistic Regression (LR) are evaluated. Performance analysis of these classification methods are evaluated in terms of accuracy. Dataset included for this project is obtained from 49 states of US and 27 states of India which contains 2.25 million US accident crash records and 1.16 million crash records respectively. Results prove that classification accuracy obtained from Random Forest (RF) is96% compared to other classification methods.

scholarly journals Design of smart sensors for real time drinking water quality monitoring and contamination detection in water distributed mains

2017 ◽  
Vol 7 (1.1) ◽  
pp. 47 ◽  
Author(s):  
S. Kavi Priya ◽  
G. Shenbagalakshmi ◽  
T. Revathi
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Real Time ◽  
Water Contamination ◽  
Distribution Systems ◽  
Water Distribution ◽  
Low Cost ◽  
Water Quality Monitoring ◽  
Drinking Water Quality ◽  
Quality Monitoring

Drinking Water Distribution Systems facilitate to carry portable water from water resources such as reservoirs, river, and water tanks to industrial, commercial and residential consumers through complex buried pipe networks. Determining the consequences of a water contamination event is an important concern in the field of water systems security and in drinking water distribution systems. The proposed work is based on the development of low cost fuzzy based water quality monitoring system using wireless sensor networks which is capable of measuring physiochemical parameters of water quality such as pH, temperature, conductivity, oxidation reduction potential and turbidity. Based on selected parameters a sensing unit is developed along with several microsystems for analog signal conditioning, data aggregation, sensor data analysis and logging, and remote representation of data to the consumers. Finally, algorithms for fusing the real time data and decision making using fuzzy logic at local level are developed to assess the water contamination risk. Based on the water contamination level in the distribution pipeline the drinking water quality is classified as acceptable/reject/desirable. When the contamination is detected, the sensing unit with ZigBee sends signals to close the solenoid valve inside the pipeline to prevent the flow of contaminated water supply and it intimates the consumers about drinking water quality through mobile app. Experimental results indicate that this low cost real time water quality monitoring system acts as an ideal early warning system with best detection accuracy. The derived solution can also be applied to different IoT (Internet of Things) scenario such as smart cities, the city transport system etc.

scholarly journals Machine learning based soft sensor model for BOD estimation using intelligence at edge

2021 ◽  
Author(s):  
Bhawani Shankar Pattnaik ◽  
Arunima Sambhuta Pattanayak ◽  
Siba Kumar Udgata ◽  
Ajit Kumar Panda
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Real Time ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Soft Sensor ◽  
Quality Monitoring ◽  
Sensor Model ◽  
Statistical Measures ◽  
Computational Performance

AbstractReal-time water quality monitoring is a complex system as it involves many quality parameters to be monitored, the nature of these parameters, and non-linear interdependence between themselves. Intelligent algorithms crucial in building intelligent systems are good candidates for building a reliable and convenient monitoring system. To analyze water quality, we need to understand, model, and monitor the water pollution in real time using different online water quality sensors through an Internet of things framework. However, many water quality parameters cannot be easily measured online due to several reasons such as high-cost sensors, low sampling rate, multiple processing stages by few heterogeneous sensors, the requirement of frequent cleaning and calibration, and spatial and application dependency among different water bodies. A soft sensor is an efficient and convenient alternative approach for water quality monitoring. In this paper, we propose a machine learning-based soft sensor model to estimate biological oxygen demand (BOD), a time-consuming and challenging process to measure. We also propose a system architecture for implementing the soft sensor both on the cloud and edge layers, so that the edge device can make adaptive decisions in real time by monitoring the quality of water. A comparative study between the computational performance of edge and cloud nodes in terms of prediction accuracy, learning time, and decision time for different machine learning (ML) algorithms is also presented. This paper establishes that BOD soft sensors are efficient, less costly, and reasonably accurate with an example of a real-life application. Here, the IBK ML technique proves to be the most efficient in predicting BOD. The experimental setup uses 100 test readings of STP water samples to evaluate the performance of the IBK technique, and the statistical measures are reported as correlation coefficient = 0.9273, MAE = 0.082, RMSE = 0.1994, RAE = 17.20%, RRSE = 37.62%, and edge response time = 0.15 s only.

Implementation of an innovative sensor technology for effective online water quality monitoring in the distribution network

2012 ◽  
Vol 7 (4) ◽  
Author(s):  
B. R. de Graaf ◽  
F. Williamson ◽  
Marcel Klein Koerkamp ◽  
J. W. Verhoef ◽  
R. Wuestman ◽  
...  
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Real Time ◽  
Low Cost ◽  
Distribution Network ◽  
Warning System ◽  
High Sensitivity ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Sensor Technology

For safe supply of drinking water, water quality needs to be monitored online in real time. The consequence of inadequate monitoring can result in substantial health risks, and economic and reputational damages. Therefore, Vitens N.V., the largest drinking water company of the Netherlands, set a goal to explore and invest in the development of intelligent water supply. In order to do this Vitens N.V. has set up a demonstration network for online water quality monitoring, the Vitens Innovation Playground (VIP). With the recent innovative developments in the field of online sensoring Vitens kicked off, in 2011, its first major online sensoring program by implementing a sensor grid based on EventLab systems from Optiqua Technologies Pte Ltd in the distribution network. EventLab utilizes bulk refractive index as a generic parameter for continuous real time monitoring of changes in water quality. Key characteristics of this innovative optical sensor technology, high sensitivity generic sensors at low cost, make it ideal for deployment as an early warning system. This paper describes different components of the system, the technological challenges that were overcome, and presents performance data and conclusions from deployment of Optiqua's EventLab systems in the VIP.

scholarly journals Real-time water quality monitoring of an artificial lake using a portable, affordable, simple, Arduino-based open source sensor

2019 ◽  
Vol 6 (1) ◽  
pp. 7-14 ◽  
Author(s):  
Murat Gökhan Eskin ◽  
Milad Torabfam ◽  
Meral Yüce ◽  
Hasan Kurt ◽  
Alessandra Cincinelli ◽  
...  
Keyword(s):  
Water Quality ◽  
Open Source ◽  
Real Time ◽  
Low Cost ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Artificial Lake ◽  
Testing Platform ◽  
Water Testing ◽  
Untreated Wastewater

Water quality assessment is vital to identify existing problems and any changes that emerge in water sources over a period of time. Conventional water quality monitoring systems remain to be limited to on-site sample collection and further analysis in environmental laboratories. The progress in Arduino-based low-cost and open-source hardware has paved the way for the development of low-cost, portable, and on-site measuring platforms. In this work, we have assembled an Arduino-based open-source water testing platform out of commercially available sensors and controllers. The water testing system was powered by a 9 V battery and had the capability of measuring water turbidity, acidity, and temperature on-site in real-time. The calibration and validation studies were carried out to assess the measurement capabilities of turbidity and pH sensors in the lab using calibration samples and UV-Vis-NIR absorption spectroscopy. The water quality platform was tested in an artificial lake that is located at Sabanci University Campus (Istanbul, Turkey), which serves as a reservoir for treated wastewaters and rainwater. Untreated wastewater samples were collected from the wastewater treatment station of the university for comparison. The measurements performed on several locations along the coast of the artificial lake were also validated in the laboratory. The water testing platform showed significant potential for miniaturization and portability of such analytical platforms for on-site environmental monitoring.

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