scholarly journals Real-time water quality monitoring through Internet of Things and ANOVA-based analysis: a case study on river Krishna

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Prasad M. Pujar ◽  
Harish H. Kenchannavar ◽  
Raviraj M. Kulkarni ◽  
Umakant P. Kulkarni
Keyword(s):  
Water Quality ◽  
Internet Of Things ◽  
Real Time ◽  
River Basin ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Quality Monitoring ◽  
Quality Analysis ◽  
Water Quality Analysis

AbstractIn this paper, an attempt has been made to develop a statistical model based on Internet of Things (IoT) for water quality analysis of river Krishna using different water quality parameters such as pH, conductivity, dissolved oxygen, temperature, biochemical oxygen demand, total dissolved solids and conductivity. These parameters are very important to assess the water quality of the river. The water quality data were collected from six stations of river Krishna in the state of Karnataka. River Krishna is the fourth largest river in India with approximately 1400 km of length and flows from its origin toward Bay of Bengal. In our study, we have considered only stretch of river Krishna flowing in state of Karnataka, i.e., length of about 483 km. In recent years, the mineral-rich river basin is subjected to rapid industrialization, thus polluting the river basin. The river water is bound to get polluted from various pollutants such as the urban waste water, agricultural waste and industrial waste, thus making it unusable for anthropogenic activities. The traditional manual technique that is under use is a very slow process. It requires staff to collect the water samples from the site and take them to the laboratory and then perform the analysis on various water parameters which is costly and time-consuming process. The timely information about water quality is thus unavailable to the people in the river basin area. This creates a perfect opportunity for swift real-time water quality check through analysis of water samples collected from the river Krishna. IoT is one of the ways with which real-time monitoring of water quality of river Krishna can be done in quick time. In this paper, we have emphasized on IoT-based water quality monitoring by applying the statistical analysis for the data collected from the river Krishna. One-way analysis of variance (ANOVA) and two-way ANOVA were applied for the data collected, and found that one-way ANOVA was more effective in carrying out water quality analysis. The hypotheses that are drawn using ANOVA were used for water quality analysis. Further, these analyses can be used to train the IoT system so that it can take the decision whenever there is abnormal change in the reading of any of the water quality parameters.

Statistical and Data Mining Techniques for Understanding Water Quality Profiles in a Mining-Affected River Basin

2018 ◽  
Vol 9 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Jose Simmonds ◽  
Juan A. Gómez ◽  
Agapito Ledezma
Keyword(s):  
Data Mining ◽  
Water Quality ◽  
River Basin ◽  
Suspended Solids ◽  
Water Quality Monitoring ◽  
High Flow ◽  
Quality Monitoring ◽  
Low Flow ◽  
Inverse Relation

This article contains a multivariate analysis (MV), data mining (DM) techniques and water quality index (WQI) metrics which were applied to a water quality dataset from three water quality monitoring stations in the Petaquilla River Basin, Panama, to understand the environmental stress on the river and to assess the feasibility for drinking. Principal Components and Factor Analysis (PCA/FA), indicated that the factors which changed the quality of the water for the two seasons differed. During the low flow season, water quality showed to be influenced by turbidity (NTU) and total suspended solids (TSS). For the high flow season, main changes on water quality were characterized by an inverse relation of NTU and TSS with electrical conductivity (EC) and chlorides (Cl), followed by sources of agricultural pollution. To complement the MV analysis, DM techniques like cluster analysis (CA) and classification (CLA) was applied and to assess the quality of the water for drinking, a WQI.

scholarly journals Intelligent Wide-Area Water Quality Monitoring and Analysis System Exploiting Unmanned Surface Vehicles and Ensemble Learning

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 681 ◽  
Author(s):  
Huiru Cao ◽  
Zhongwei Guo ◽  
Shian Wang ◽  
Haixiu Cheng ◽  
Choujun Zhan
Keyword(s):  
Water Quality ◽  
Monitoring System ◽  
Ensemble Learning ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Wide Area ◽  
Quality Analysis ◽  
Water Quality Analysis ◽  
Analysis Algorithm ◽  
Analysis System

Water environment pollution is an acute problem, especially in developing countries, so water quality monitoring is crucial for water protection. This paper presents an intelligent three-dimensional wide-area water quality monitoring and online analysis system. The proposed system is composed of an automatic cruise intelligent unmanned surface vehicle (USV), a water quality monitoring system (WQMS), and a water quality analysis algorithm. An automatic positioning cruising system is constructed for the USV. The WQMS consists of a series of low-power water quality detecting sensors and a lifting device that can collect the water quality monitoring data at different water depths. These data are analyzed by the proposed water quality analysis algorithm based on the ensemble learning method to estimate the water quality level. Then, a real experiment is conducted in a lake to verify the feasibility of the proposed design. The experimental results obtained in real application demonstrate good performance and feasibility of the proposed monitoring system.

scholarly journals GSM Based Water Quality Monitoring System Using Arduino

2019 ◽  
Vol 6 (4) ◽  
pp. 22-26
Author(s):  
S Gokulanathan ◽  
P Manivasagam ◽  
N Prabu ◽  
T Venkatesh
Keyword(s):  
Water Quality ◽  
Monitoring System ◽  
Rapid Development ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Vital Role ◽  
Quality Monitoring ◽  
Human Society ◽  
Power Efficient

This paper investigates about water quality monitoring system through a wireless sensor network. Due to the rapid development and urbanization, the quality of water is getting degrade over year by year, and it leads to water-borne diseases, and it creates a bad impact. Water plays a vital role in our human society and India 65% of the drinking water comes from underground sources, so it is mandatory to check the quality of the water. In this model used to test the water samples and through the data it analyses the quality of the water. This paper delivers a power efficient, effective solution in the domain of water quality monitoring it also provides an alarm to a remote user, if there is any deviation of water quality parameters.

scholarly journals Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing

Water ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 22
Author(s):  
Qi Cao ◽  
Gongliang Yu ◽  
Shengjie Sun ◽  
Yong Dou ◽  
Hua Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Real Time ◽  
Training Model ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Hyperspectral Remote Sensing ◽  
Water Quality Parameters ◽  
Quality Monitoring ◽  
Haihe River

The Haihe River is a typical sluice-controlled river in the north of China. The construction and operation of sluice dams change the flow and other hydrological factors of rivers, which have adverse effects on water, making it difficult to study the characteristics of water quality change and water environment control in northern rivers. In recent years, remote sensing has been widely used in water quality monitoring. However, due to the low signal-to-noise ratio (SNR) and the limitation of instrument resolution, satellite remote sensing is still a challenge to inland water quality monitoring. Ground-based hyperspectral remote sensing has a high temporal-spatial resolution and can be simply fixed in the water edge to achieve real-time continuous detection. A combination of hyperspectral remote sensing devices and BP neural networks is used in the current research to invert water quality parameters. The measured values and remote sensing reflectance of eight water quality parameters (chlorophyll-a (Chl-a), phycocyanin (PC), total suspended sediments (TSS), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH4-N), nitrate-nitrogen (NO3-N), and pH) were modeled and verified. The results show that the performance R2 of the training model is above 80%, and the performance R2 of the verification model is above 70%. In the training model, the highest fitting degree is TN (R2 = 1, RMSE = 0.0012 mg/L), and the lowest fitting degree is PC (R2 = 0.87, RMSE = 0.0011 mg/L). Therefore, the application of hyperspectral remote sensing technology to water quality detection in the Haihe River is a feasible method. The model built in the hyperspectral remote sensing equipment can help decision-makers to easily understand the real-time changes of water quality parameters.

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.

scholarly journals A Study on IOT for Smart Water Quality Monitoring Using MQTT Algorithm

2019 ◽  
pp. 869-872
Author(s):  
G. Vadivel ◽  
A. P. Thangamuthu ◽  
A. Priyadharshini
Keyword(s):  
Water Quality ◽  
Low Cost ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Quality Monitoring ◽  
Smart Water ◽  
Quality Detection ◽  
Quality Of Water ◽  
System Water

The decrease in quality of water resources has become a common problem. The standard methods of water quality surveillance include water sample manual collection from various locations. These water samples were tested in laboratory using intelligence capabilities. Such approaches take time and are no longer considered inefficient. The old method of water quality detection was time consuming, less accurate and expensive. By focusing on the above problems, IOT can be used to monitor water quality in real time, a low cost water quality monitoring system. Water quality parameters in the proposed system are measured by various sensors such as pH, temperature and dissolved oxygen to transfer data on a platform via a microcontroller system. Therefore, to meet these needs, you can use other technologies such as MQTT (Message Sorting Delimiter Transform), allowing the Sensor and End device rankings to publish and subscribe. And the number of data simultaneously between sensors and servers with the help of the MQTT algorithm.

Real-Time Wireless Water Quality Monitoring System Based on MODBUS Protocol

2013 ◽  
Vol 336-338 ◽  
pp. 1703-1707
Author(s):  
Yun Zhang ◽  
Fei Zhang ◽  
Zhong Hua Hong
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Radio Station ◽  
System Structure ◽  
Quality Monitoring ◽  
Modbus Protocol ◽  
Long Time ◽  
Monitoring Center

Recently, the environment problem has been more serious, especially the water-quality problem. In this paper, we develop the portable water-quality monitoring terminal, which can collect and real-time transmit water-quality parameters. The monitoring center received the data based on Modbus protocol by using wireless radio station. System structure and transmitting data frame are discussed here. The paper also introduced the monitoring software function in the monitoring center. The result of system test proved that the water-quality terminal can transfer the data to the remote monitoring center for a long time successfully, the system can achieve real-time and on-line water-quality monitoring functions.

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