Design and implementation of a hybrid model based on two-layer decomposition method coupled with extreme learning machines to support real-time environmental monitoring of water quality parameters

2019 ◽  
Vol 648 ◽  
pp. 839-853 ◽  
Author(s):  
Elham Fijani ◽  
Rahim Barzegar ◽  
Ravinesh Deo ◽  
Evangelos Tziritis ◽  
Konstantinos Skordas
Keyword(s):  
Water Quality ◽  
Environmental Monitoring ◽  
Real Time ◽  
Hybrid Model ◽  
Decomposition Method ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Extreme Learning Machines ◽  
Learning Machines ◽  
Design And Implementation

scholarly journals Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1547
Author(s):  
Jian Sha ◽  
Xue Li ◽  
Man Zhang ◽  
Zhong-Liang Wang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Deep Learning ◽  
Real Time ◽  
Best Management Practices ◽  
Input Data ◽  
Management Practices ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Real Time Monitoring

Accurate real-time water quality prediction is of great significance for local environmental managers to deal with upcoming events and emergencies to develop best management practices. In this study, the performances in real-time water quality forecasting based on different deep learning (DL) models with different input data pre-processing methods were compared. There were three popular DL models concerned, including the convolutional neural network (CNN), long short-term memory neural network (LSTM), and hybrid CNN–LSTM. Two types of input data were applied, including the original one-dimensional time series and the two-dimensional grey image based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) decomposition. Each type of input data was used in each DL model to forecast the real-time monitoring water quality parameters of dissolved oxygen (DO) and total nitrogen (TN). The results showed that (1) the performances of CNN–LSTM were superior to the standalone model CNN and LSTM; (2) the models used CEEMDAN-based input data performed much better than the models used the original input data, while the improvements for non-periodic parameter TN were much greater than that for periodic parameter DO; and (3) the model accuracies gradually decreased with the increase of prediction steps, while the original input data decayed faster than the CEEMDAN-based input data and the non-periodic parameter TN decayed faster than the periodic parameter DO. Overall, the input data preprocessed by the CEEMDAN method could effectively improve the forecasting performances of deep learning models, and this improvement was especially significant for non-periodic parameters of TN.

scholarly journals Research and Design of Distributed IoT Water Environment Monitoring System Based on LoRa

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Wei Chen ◽  
Xiao Hao ◽  
JianRong Lu ◽  
Kui Yan ◽  
Jin Liu ◽  
...  
Keyword(s):  
Water Quality ◽  
Water Temperature ◽  
Real Time ◽  
Monitoring System ◽  
Water Environment ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Environment Monitoring ◽  
Long Distance ◽  
Internet Of Things Technology

In order to solve the problems of high labor cost, long detection period, and low degree of information in current water environment monitoring, this paper proposes a lake water environment monitoring system based on LoRa and Internet of Things technology. The system realizes remote collection, data storage, dynamic monitoring, and pollution alarm for the distributed deployment of multisensor node information (water temperature, pH, turbidity, conductivity, and other water quality parameters). Moreover, the system uses STM32L151C8T6 microprocessor and multiple types of water quality sensors to collect water quality parameters in real time, and the data is packaged and sent to the LoRa gateway remotely by LoRa technology. Then, the gateway completes the bridging of LoRa link to IP link and forwards the water quality information to the Alibaba Cloud server. Finally, end users can realize the water quality control of monitored water area by monitoring management platform. The experimental results show that the system has a good performance in terms of real-time data acquisition accuracy, data transmission reliability, and pollution alarm success rate. The average relative errors of water temperature, pH, turbidity, and conductivity are 0.31%, 0.28%, 3.96%, and 0.71%, respectively. In addition, the signal reception strength of the system within 2 km is better than -81 dBm, and the average packet loss rate is only 94%. In short, the system’s high accuracy, high reliability, and long distance characteristics meet the needs of large area water quality monitoring.

Design of Real-Time Monitoring System for Surface Water Pollution Based on GPRS

2011 ◽  
Vol 383-390 ◽  
pp. 213-217 ◽  
Author(s):  
Guang Jian Chen ◽  
Jin Ling Jia
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
Surface Water ◽  
Real Time ◽  
Remote Monitoring ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Wireless Transmission ◽  
Surface Water Pollution ◽  
Monitoring Center

To implement the remote and real-time monitoring of surface water pollution, a design scheme of water quality monitoring system based on GPRS technology is put forward, which is composed of monitoring terminal, monitoring center and communication network. The various parameters of surface water are acquired using water quality detection sensor terminal and uploaded to the remote monitoring center via GPRS module by monitoring, and then the water quality parameters acquisition, processing and wireless transmission are realized. Water quality parameters are received through the internet network by the monitoring center, to realize its remote monitoring and management. According to the practice result, the system has materialized functions on GPRS service platform, such as real-time water quality parameters acquisition, procession, wireless transmission, remote monitoring and management, which is suitable for surface water pollution continuous monitoring and has the good application in the future.

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 Establishment of an Automatic Real-Time Monitoring System for Irrigation Water Quality Management

2020 ◽  
Vol 17 (3) ◽  
pp. 737 ◽  
Author(s):  
Wei-Jhan Syu ◽  
Tsun-Kuo Chang ◽  
Shu-Yuan Pan
Keyword(s):  
Heavy Metals ◽  
Water Quality ◽  
Quality Management ◽  
Real Time ◽  
Monitoring System ◽  
Irrigation Water ◽  
Water Quality Management ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Irrigation Water Quality

In order to provide the real-time monitoring for identifying the sources of pollution and improving the irrigation water quality management, the integration of continuous automatic sampling techniques and cloud technologies is essential. In this study, we have established an automatic real-time monitoring system for improving the irrigation water quality management, especially for heavy metals such as Cd, Pb, Cu, Ni, Zn, and Cr. As a part of this work, we have first provided several examples on the basic water quality parameters (e.g., pH and electrical conductance) to demonstrate the capacity of data correction by the smart monitoring system, and then evaluated the trend and variance of water quality parameters for different types of monitoring stations. By doing so, the threshold (to initiate early warming) of different water quality parameters could be dynamically determined by the system, and the authorities could be immediately notified for follow-up actions. We have also provided and discussed the representative results from the real-time automatic monitoring system of heavy metals from different monitoring stations. Finally, we have illustrated the implications of the developed smart monitoring system for ensuring the safety of irrigation water in the near future, including integration with automatic sampling for establishing information exchange platform, estimating fluxes of heavy metals to paddy fields, and combining with green technologies for nonpoint source pollution control.

Comparative assessment of gene quantification using real-time PCR and water quality parameters in unsanitary landfill

2009 ◽  
Vol 59 (2) ◽  
pp. 331-338 ◽  
Author(s):  
J. S. Han ◽  
C. G. Kim
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Real Time Pcr ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Monitoring Methods ◽  
Biological Investigation ◽  
Gene Quantification ◽  
Landfill Monitoring ◽  
The Relationship

Because of increasing demanding for development of direct ecological landfill monitoring methods, there is a requirement for the condition of landfills and their influence on the environment to be characterized by the behavior of enzymes and bacteria mainly concerned with biochemical reaction in the landfills. This study was thus conducted to understand the fates of contaminants in association with groundwater quality parameters. For the study, groundwater was seasonally sampled from four closed unsanitary landfills in which microbial diversity was simultaneously obtained by 16S rDNA methods. Subsequently, a number of the specific genes of representative bacteria and encoding enzymes were quantified by real-time PCR. The relationship between water quality parameters and gene quantification were compared based on correlation factors. Correlation between DSR gene and BOD was greater than 0.8 while NSR gene and nitrate were related more than 0.9. For MTOT, it was at the highest related at 100% over BOD/COD and Dde genes were correlated over 0.8. In addition, anaerobic genes and DO were also related more than 0.8, showing anaerobic reactions generally dependent upon DO. As demonstrated in the study, molecular biological investigation and water quality parameters are highly co-linked, so that quantitative real-time PCR could be cooperatively used for assessing landfill stabilization in association with the conventional monitoring parameters.

Data driven methods for real time flood, drought and water quality monitoring: applications for Internet of Water

2020 ◽  
Author(s):  
Brianna Pagán ◽  
Nele Desmet ◽  
Piet Seuntjens ◽  
Erik Bollen ◽  
Bart Kuijpers
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Water Quality ◽  
Data Assimilation ◽  
Real Time ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Weather Data ◽  
Model Estimate ◽  
The University

<p>The Internet of Water (IoW) is a large-scale permanent sensor network with 2500 small, energy-efficient wireless water quality sensors spread across Flanders, Belgium. This intelligent water management system will permanently monitor water quality and quantity in real time. Such a dense network of sensors with high temporal resolution (sub-hourly) will provide unprecedented volumes of data for drought, flood and pollution management, prediction and decisions. While traditional physical hydrological models are obvious choices for utilizing such a dataset, computational costs or limitations must be considered when working in real time decision making.</p><p>In collaboration with the Flemish Institute for Technological Research (VITO) and the University of Hasselt, we present several data mining and machine learning initiatives which support the IoW. Examples include interpolating grab sample measurements to river stretches to monitor salinity intrusion. A shallow feed forward neural network is trained on historical grab samples using physical characteristics of the river stretches (i.e. soil properties, ocean connectivity). Such a system allows for salinity monitoring without complex convection-diffusion modeling, and for estimating salinity in areas with less monitoring stations. Another highlighted project is the coupling of neural network and data assimilation schemes for water quality forecasting. A long short-term memory recurrent neural network is trained on historical water quality parameters and remotely sensed spatially distributed weather data. Using forecasted weather data, a model estimate of water quality parameters are obtained from the neural network. A Newtonian nudging data assimilation scheme further corrects the forecast leveraging previous day observations, which can aid in the correction for non-point or non-weather driven pollution influences. Calculations are supported by an optimized database system developed by the University of Hasselt which further exploits data mining techniques to estimate water movement and timing through the Flanders river network system. As geospatial data increases exponentially in both temporal and spatial resolutions, scientists and water managers must consider the tradeoff between computational resources and physical model accuracy. These type of hybrid approaches allows for near real-time analysis without computational limitations and will further support research to make communities more climate resilient.</p>

scholarly journals Real-Time Fine-Scale Measurement of Water Quality Parameters Along the Bagmati River in the Kathmandu Valley

2021 ◽  
Vol 20 (3) ◽  
Author(s):  
M. P. Adhikari ◽  
N. B. Rawal ◽  
N. B. Adhikari
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Sensor System ◽  
Kathmandu Valley ◽  
Fine Scale ◽  
Mobile Sensor ◽  
Space Domain ◽  
Bagmati River

Real-time fine-scale data was collected along the Bagmati River, in Kathmandu Valley, using mobile and fixed sensor system during the winter season. The water quality parameters; pH, conductivity, total dissolved salt (TDS), salinity, oxygen reduction potential (ORP), dissolved oxygen (DO), and turbidity were measured in the space domain using a fixed sensor and mobile sensor (small rafting boat loaded with sensor) systems. The water quality parameters from the fixed sensor system revealed that the Bagmati River was comparatively less polluted upstream from Gokarna to Tilganga sites and molecular oxygen present in the water was enough to decompose organic pollutants. However, the water quality downstream from Tinkune to Balkhu sites was degraded drastically making it unfit for living organisms. Temporal variation of water quality attributes that human activity significantly enhanced pollutants which severely degraded the water quality in the daytime. The fine-scale space domain heat map data of the mobile sensor system also suggested that the water quality continuously deteriorated from Shankhamul to Sundarighat sites. The ORP value was always negative and decreased with downflow and becomes -263 mV near the Sundarighat Bridge. The mixing of tributaries and increment of solid waste and untreated sewer along the river enhanced pollutants excessively and decreased oxygen level to zero. The results attributed that decomposition of the sewer by microorganism consumed almost all oxygen which produced volatile compounds and generated malodorous odor downstream of Bagmati River.

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