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

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.

Download Full-text

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

Applied Water Science ◽

10.1007/s13201-019-1111-9 ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 4

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.

Download Full-text

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

Water ◽

10.3390/w14010022 ◽

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.

Download Full-text

Long-term water quality monitoring in Lake Simcoe based on the empirical method and machine learning

10.5194/egusphere-egu21-9027 ◽

2021 ◽

Author(s):

Bo Wang ◽

Jinhui Huang ◽

Hongwei Guo

Keyword(s):

Machine Learning ◽

Water Quality ◽

Chlorophyll A ◽

Total Phosphorus ◽

Water Quality Monitoring ◽

Quality Parameters ◽

Empirical Method ◽

Quality Monitoring ◽

Lake Simcoe

Abstract: The traditional water quality monitoring methods are time-consuming and laborious, which can only reflect the water quality status of single point scale, and have some problems such as irregular sampling time and limited sample size. Remote sensing technology provides a new idea for water quality monitoring, and the temporal resolution of MODIS is one day, which is suitable for long-term, continuous real-time large-scale monitoring of lakes. In this study, Lake Simcoe (located in Ontario, Canada) was selected as the research area. The long-term spatiotemporal changes of chlorophyll-a, transparency, total phosphorus and dissolved oxygen were analyzed by comparing the empirical method, multiple linear regression, random forest and neural network with MODIS data. Finally, the water quality condition of Lake Simcoe is evaluated. The results show that the overall retrieval results of two machine learning models are better than that of the empirical method. The optimal retrieval accuracy R&#178; for four water quality parameters are 0.976, 0.988, 0.943, 0.995, and RMSE are 0.13&#956;g/L, 0.3m, 0.002mg/L and 0.14mg/L, respectively. On the annual scale, the annual mean values of the four water quality parameters during the 10-year period from 2009 to 2018 were 1.37&#956;g/L, 6.9m, 0.0112mg/L and 10.17mg/L, respectively. On the monthly scale, chlorophyll a, total phosphorus and dissolved oxygen first decreased and then increased at the time of year. The higher concentrations of chlorophyll a and total phosphorus in the south and east of Lake Simcoe are related to the input of nutrients from the surrounding residents and farmland.Key words: water quality monitoring; MODIS; empirical method; machine learning

Download Full-text

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.336-338.1703 ◽

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.

Download Full-text

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

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.e1014.0785s319 ◽

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.

Download Full-text