Online Water Research Articles

Integrated STL-DBSCAN algorithm for online hydrological and water quality monitoring data cleaning

Online hydrological and water quality monitoring data has become increasingly crucial for water environment management such as assessment and modeling. However, online monitoring data often contains erroneous or incomplete datasets, consequently affecting its operational use. In the study, we developed an automated data cleaning algorithm grounded in Seasonal-Trend decomposition using Loess (STL) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). STL identifies and corrects more obvious anomalies in the time series, followed by DBSCAN for further refinement, in which the reverse nearest neighbor method was employed to enhance the clustering accuracy. To improve anomaly detection, a two-level residual judgment threshold was applied. The algorithm has been successfully applied to three reservoirs in Shanghai, China, achieving the precision rate of 0.91 and recall rate of 0.81 for dissolved oxygen and pH. The proposed algorithm can be potentially applied for cleaning of environment monitoring data with high accuracy and stability.

A low-cost IoT-based deep learning method of water gauge measurement for flood monitoring

Real-time and accurate measurement of the water level is a critical step in flood monitoring and management of water resources. In recent years, with the advent of the Internet of Things (IoTs) and cloud computing platforms and resources, the surveillance technology for water monitoring has been revolutionized due to the availability of high-resolution and portable cameras, robust image processing techniques, and cloud-enabled data fusion centers. However, despite the potential advantages of online water level monitoring of the rivers and lakes, some technical challenges need to be addressed before they can be fully utilized. Submersible sensor devices are frequently used for measuring water levels but are prone to damage from sediment deposition and many gauge detection techniques are inefficient at nighttime. In response, this paper presents a novel Internet of Things (IoT) based deep learning methodology that uses Mask-RCNN to accurately segment gauges from images even when there are distortions present. An automated and immediate water stage estimate is provided by this simple, low-cost method. The methodology’s applicability to water resource management systems and flood disaster prevention engineering opens up new possibilities for the deployment of intelligent IoT-based flood monitoring systems in the future.

VARDARA: An Online IOT Based Water Delivery Application

This paper presents a novel online water and delivery system, "VARDARA," designed to address the pressing need for convenient and efficient water distribution. Our solution integrates an intuitive Android application with smart IoT vehicles to enable seamless online ordering and timely delivery of water. By optimizing delivery routes and leveraging IoT technology, we ensure quicker and more environmentally friendly deliveries, bridging the gap between e-commerce and efficient water distribution. This innovative approach enhances customer convenience and contributes to sustainable resource management. Key Words: water, delivery, online, IoT, efficiency, sustainability.

Online water vapor removal membrane inlet mass spectrometer for high-sensitivity detection of dissolved methane

Underwater mass spectrometry is characterized by excellent consistency, strong specificity, and the ability to simultaneously detect multiple substances, making it a valuable tool in research fields such as aquatic ecosystems, hydrothermal vents, and the global carbon cycle. Nevertheless, current underwater mass spectrometry encounters challenges stemming from the high-water vapor content, constituting proportions of nearly 90%. This results in issues such as peak overlap, interference with peak height, decreased ionization efficiency and, consequently, make it difficult to achieve low detection limits for extremely low concentrations of gases, such as methane, and impede the detection of background CH4 levels. In this study, we optimized the design of the sampling gas path and developed a high gas-tightness, high pressure-resistant membrane inlet system, coupled with a small-volume, low-power online water vapor removal system. This innovation efficiently eliminates water vapor while maintaining a high permeation flux of the target gases. By elevating the vacuum level to the order of 1E-6 Torr, the ionization efficiency and detection performance were improved. Based on this, we created an online water vapor removal membrane inlet mass spectrometer and conducted experimental research. Results indicated that the water removal efficiency approached 100%, and the vacuum level was elevated by more than 2 orders of magnitude. The detection limit for CH4 increased from over 600 nmol/L to 0.03 nmol/L, representing an improvement of over 4 orders of magnitude, and reaching the level of detecting background CH4 signals in deep-sea and lakes. Furthermore, the instrument exhibited excellent responsiveness and tracking capability to concentration changes on the second scale, enabling in situ analysis of rapidly changing concentration scenarios.

A "Prediction - Detection - Judgment" framework for sudden water contamination event detection with online monitoring

The contamination detection technology helps in water quality management and protection in surface water. It is important to detect sudden contamination events timely from dynamic variations due to various interference factors in online water quality monitoring data. In this study, a framework named “Prediction - Detection - Judgment” is proposed with a method framework of “Time series increment - Hierarchical clustering - Bayes' theorem model”. Time to detection is used as an evaluation index of contamination detection methods, along with the probability of detection and false alarm rate. The proposed method is tested with available public data and further applied in a monitoring site of a river. Results showed that the method could detect the contamination events with a 100% probability of detection, a 17% false alarm rate and a time to detection close to 4 monitoring intervals. The proposed index time to detection evaluates the timeliness of the method, and timely detection ensures that contamination events can be responded to and dealt with in time. The site application also demonstrates the feasibility and practicability of the framework proposed in this study and its potential for extensive implementation.

Method for rapid warning and activity concentration estimates in online water γ-spectrometry systems

Method for rapid warning and activity concentration estimates in online water γ-spectrometry systems

Online water quality monitoring: a case study in the umbulan drinking water supply system, East Java, Indonesia

Conventional systems that monitor the supply system periodically are considered to be very inadequate. This can cause the water supply distributed to the community to be of poor quality. Therefore, this study aims to evaluate water quality periodically by measuring water parameters such as pH, chlorine and turbidity for 30 days using an online system and portable unit. Portable tools used to measure pH, turbidity and residual chlorine are as follows, namely pH meters (Hanna, HI98017), Turbidity Meters (Hanna, HI 98703), Residual Chlorine meters and measurements with an online system using "Supervisory Control and Data Acquisition”. The average parameter value of the results of online daily monitoring for a month for the pH parameter was a minimum 6.92, a maximum of 7.46, a minimum turbidity of 0.08 nephelometric turbidity unit (NTU) and a maximum of 0.94 NTU. While the residual chlorine minimum of 0.29 mg/L and a maximum of 0.58 mg/L. To determine the accuracy of the offline measurements, measurements were also performed with a portable device four times a month. The comparison of the measurement results is very small, which is the difference in pH parameters is between 0.37-0.50, turbidity is between 0.05-0.21 NTU, and residual chlorine is 0.002-0.006 mg/L. Offline measurements using the portable device are consistent with the online daily monitoring. Future research should focus on optimizing sensor technology for enhanced accuracy and reliability, and developing automated data analysis algorithms to streamline evaluation processes.

Assessing water quality in a distribution network based on hydraulic conditions

Abnormalities in hydraulic conditions inside a water distribution network are strongly related to the deterioration of drinking water quality. Leaking pipes and valves cause changes to the hydraulic conditions and allow the entry of impurities into the distribution system. Sudden flow and pressure shocks can detach soft deposits and biofilms from the pipe surface, resulting in deterioration of water quality. Online water quality measurements in a distribution network are scarce, but more common online flow and pressure measurements reveal the changes in hydraulic conditions in a distribution network and can be utilized to assess the water quality continuously and near real-time via modelling. Here, a data-driven model based solely on the online flow and pressure measurements in a distribution network for assessing the water quality at the end of an urban district metered area is presented. With the accuracy of R2 0.77, the developed data-driven model is able to assess the level of and the changes in potable water quality in a non-laborious and cost-effective way, also enabling proactive operations to ensure the distribution of high-quality drinking water to the consumers.

Effects of Water-to-Air Mass Ratio on Long-Term Washing Efficiency and Erosion Risk in an Axial Compressor Under Online Washing Conditions

Abstract One of the main reasons for gas turbines’ performance losses is the deposition of dirt on the compressor blades. Dirt deposit has to be periodically removed to keep the engine performance as high as possible. This is the reason motivating the presence of online water washing systems in most compressor gas turbines. Such systems aim at cleaning the compressor blades to recover efficiency; thus, the larger the water flow, the better it is assumed the compressor is cleaned (fixing all the other conditions). In the present work, we simulate the long-term behavior of a real axial compressor, from the inlet to the first-stage rotor, subject to online water washing with different water flowrates. The frozen rotor approach is adopted to solve the flow field in the rotor region. Simulations are performed by using the unsteady k-ɛ realizable model coupled with a Lagrangian tracking of the injected liquid phase. Water droplet erosion is handled by using a semi-empirical model developed by the authors. In each simulation, 504,000 parcels have been tracked, providing statistically reliable predictions. To simulate the long-term evolution of the washing process, a discrete mesh morphing technique coupled with the use of specific scale factors is adopted. Each of the tested configurations is composed of three successive erosive steps up to the blade compressor end-of-life. By varying the water-to-air mass fraction (WAMF*), six different injection configurations are assessed in terms of long-time average washing efficiency and erosion risk. The results predicted show the dependence of the considered washing indices on water mass flowrate and set the stage for the development of a washing optimization tool, which can help the design and management processes. In scenarios where washing indices are given minimal importance and the objective is to reduce the risk of erosion, the optimal injection configuration was shown to correspond to a WAMF* value of 0.250. Conversely, when washing efficiency is prioritized, the optimal injection configuration has been shown to correspond to the case where WAMF* = 0.750.

CSD: An Online Spectral Sensing Method for Wastewater Quality Monitoring Based on Compressed Sensing and Incremental Learning

The outstanding spectral resolution of full-spectrum detection (FSD) ensures excellent performance for complex scenarios in which target signals are affected by particle interference. However, FSD is limited by the cost of its measurement devices and demanding install requirements. Therefore, it is not always practical to utilize FSD for online water quality monitoring in wastewater treatment plants (WWTPs). In this study, we developed a novel detection approach entitled “compressive spectrum detection (CSD)” to overcome the hardware restrictions posed by FSD. An intelligent source-encoding laser is first developed to perform spectrum encoding, which avoids the utilization of sophisticated equipment and reduces the detection cost. Subsequently, a dual-stage compressive detection framework was proposed based on incremental learning to accurately reconstruct spectral signals with full-spectrum resolution, which consisted of three innovative algorithms and could solve difficulties brought about by sample insufficiency and changing environments in practical situations. Based on our constructed online detection prototype, the superiority of CSD was evaluated through practical application in which the training samples were reduced to a quarter of their original size with an overall improvement in detection precision greater than 50%.

The Effectiveness of Consumer Protection in Online Water Account Payment Services on Ease of Access and Customer Satisfaction (Case Study on Regional Public Drinking Water Company Tirta Bahari Tegal)

The Effectiveness of Consumer Protection in Online Water Account Payment Services on Ease of Access and Customer Satisfaction (Case Study on Regional Public Drinking Water Company Tirta Bahari Tegal)

Design and Research of Multi-point Water Quality Monitoring System Based on Internet of Things

With the increasing demand for water quality monitoring and the increasing severity of water pollution, the water environmental quality in China faces a worrying situation, and water body monitoring has become an extremely important task. Now more than ever, new strategies for water quality monitoring are needed, and the rapid development of wireless monitoring technology perfectly meets people’s pursuit of low-cost, automated and real-time water quality monitoring. However, there are still problems such as few monitoring points cannot reflect the overall situation of water bodies, high artificial costs, and time-consuming analysis. This paper designs and implements a new water monitoring platform built on an Internet of Things platform. Taking water quality monitoring sensors as the core and small boats as the basic framework, it carries control, positioning, navigation equipment, uses wireless network as the data transmission means, monitors multi-point cruising sampling in different areas, realizes real-time collection, data storage and data analysis to complete related environmental monitoring. The multi-point online water quality monitoring system comprehensively applies sensor technology, Internet of Things technology and cloud computing technology to form an integrated online monitoring system. It aims to solve the problems of harsh working environment, strong repetitiveness and long sampling time of water quality monitoring tasks, and achieve fast, efficient and accurate completion of water quality monitoring tasks.

Water Quality Prediction Based on Hybrid Deep Learning Algorithm

Pollution from many different sources severely affects the quality of our water supply. Over the past few years, a large number of online water quality monitoring stations have been used to gather time series data on water quality monitoring. These numbers are the foundation for deep learning techniques for forecasting water quality. In particular, typical deep learning approaches struggle to accurately estimate water quality in the presence of net promoter system (NPS) contamination. To overcome this shortcoming, a new deep learning model called long short-term memory (LSTM)–gray wolf optimization (GWO)–fish swarm optimization (FSO) was developed to enhance the precision of water quality prediction with NPS pollution. The well-established model may remedy the mechanism models’ inability to foretell changes in water quality on a minute-by-minute basis. Thamirabarani river watershed was used for the model’s application. Based on experimental data, the suggested model outperformed the mechanism model and the LSTM model in predicting extreme values. Maximum relative errors in anticipated against observed dissolved oxygen, chemical oxygen demand, and NH3─N values were 7.58%, 18.45%, and 22.25%, respectively. In comparison to the artificial neural network (ANN), back propagation neural network (BPNN), and recurrent neural network (RNN) models, the created LSTM–GWO–FSO model was shown to have greater computational performance (RNN). LSTM–GWO–FSO outperformed ANN, BPNN, and RNN regarding R2 of 3.1%–38.4% improvements. The suggested approach may provide a fresh perspective when predicting water quality in the presence of NPS contamination.

Methodological Approach for an Online Water Quality Monitoring System in an Iron Ore Tailing Dam

Monitoring the concentration of potentially toxic elements (PTEs) in the aquatic ecosystems of the Amazon is critical to guarantee the maintenance of the ecological balance and the life quality of human populations that reside in or use these environments for survival. In this sense, many rivers in the region are dammed to form lakes for depositing mining tailings. Among these, the Gelado Project has the largest iron ore dam in the Amazon that occupies about 13.5 km2 of surface area with 142 million m3 of water and tailings volume, which are currently being mined for exploration, and its upstream waters and downstream are historically used by traditional populations. Based on this, to monitor the impacts of this activity, an online system for sampling and automatic analysis of water quality, composed of three process analyzers monitoring more than 20 parameters, including the PTEs (Cd, Pb, Fe, Mn, Ni, and Cu), was installed downstream of this dam. Therefore, this short communication describes this system’s development, installation, operation, and main advantage over conventional methods.

Water Quality Data Analysis from the Onlimo Monitoring System at 16 Stations in Priority Watersheds and Lakes

Currently, the decline in the quality of river and lake water continues, for this reason, activities to control pollution and improve river and lake water quality are required. One effort to prevent this problem is to implement online water quality monitoring technology continuously and in real time. The information generated from the water quality monitoring system can be used as a basis for planning watershed management and determining river and lake water management policies at the national and regional levels. The purpose of this activity is to analyze river and lake water quality data and evaluate the performance of the monitoring systems in 16 water quality monitoring stations that have been built. The data being analyzed is from 2016 to 2020 and includes the parameters of temperature, conductivity, TDS, DO, pH, and turbidity. The 16 station locations are spread over the Ciliwung River, Cisadane River, Citarum River, Serayu River, Bengawan Solo River, and Lake Toba. This research method uses descriptive statistical analysis and the STORET method, which refers to the Decree of the Minister of Environment and Forestry Number 115 of 2003 and Government Regulation of the Republic of Indonesia Number 82 of 2001. The results of this study indicate that most of the rivers monitored fall into the moderate to heavily polluted category, especially the rivers downstream of the Ciliwung, Cisadane, and Bengawan Solo rivers. The results of the analysis of DO, TDS, conductivity, and turbidity parameters from 16 monitoring stations, mostly met the Class II quality standards.

Application of the concept of smart city and smart water management for the new capital city

The Indonesian government has decided to move the State Capital (IKN) from DKI Jakarta to East Kalimantan Province. One of IKN’s visions is to build a smart, modern city that meets international standards. This paper examines the concept of smart city and smart water management (SWM) that will be applied to the new IKNs to build an integrated, sophisticated and sustainable clean water supply system. The method used was analyzing concepts related to smart cities and SWM applications in the world and Indonesia. Based on the results of the analysis of the abovementioned concepts, several conclusions were obtained, namely that an SWM support system is indispensable in water system management, leak detection, and water quality monitoring. Then, a smart water grid is used to develop control over water resources, infrastructure, and water discharge with high efficiency and sustainability. The flood early warning system must be utilized to minimize losses or victims caused by flood disasters. In addition, an online water quality monitoring system is needed to monitor water quality online and in real time. The integrated implementation of the smart city and SWM concepts described in the paper is expected to produce safe, healthy, and sustainable drinking water services.

Enhancing Water Resource Management Efficiency through Smart Technologies: A Case Study in Kashkadarya Region, Uzbekistan

This article presents a comprehensive study on the implementation of "Smart Water" devices in water management facilities in the Kashkadarya region of Uzbekistan. The goals of this initiative were to achieve efficient water control, real-time monitoring, and rapid water management. Through the installation of smart devices, including "Smart Water" devices and "DIVER" devices, water resources were effectively managed, resulting in online water control and significant water resource savings. The automation of large hydrotechnical facilities, such as the Karshi hydroelectric plant, enabled remote management and reduced technical water losses. Additionally, the pilot project on automation, based on solutions provided by the Australian company "Rubicon Water Pty. Ltd," showcased the potential for digitalization and automation in water resources management. The project demonstrated successful outcomes, including increased accuracy in land reclamation assessment, reduced water shortage, enhanced water resource utilization, and significant cost savings. The findings highlight the effectiveness and advantages of smart technologies in water management, underscoring the need for their widespread adoption in addressing water scarcity and sustainable water resource management globally.
 
 
 
 Highlights:
 
 
 
 
 
 Improved Water Control: Implementation of "Smart Water" devices enables real-time monitoring and online water control, enhancing efficient water resource management.
 
 
 Cost Savings and Resource Efficiency: Automation and smart technologies lead to significant water savings, reduced technical losses, and improved water utilization, resulting in economic benefits.
 
 
 Scalable Solutions for Global Water Management: The successful pilot project by "Rubicon Water Pty. Ltd" demonstrates the potential for widespread adoption of smart technologies in addressing water scarcity and promoting sustainable water resource management globally.
 
 
 Keywords: water resources, smart water, efficient technologies, water management, automation.
 
 
 
 
 

Developing a Modified Online Water Quality Index: A Case Study for Brazilian Reservoirs

Online approaches for monitoring water quality can be an alternative aid to rapid decision-making in watershed management, especially reservoirs, given their vulnerability to the process of eutrophication. In this study, a modified water quality index (WQI) was developed using parameters that are easily measured with sensors, which would allow for the online monitoring of reservoirs. The modified WQI was based on WQICETESB and we used regression models to obtain values for the parameters: total phosphorus (TP), total nitrogen (TN), biochemical oxygen demand (BOD) and total solids (TS). Water quality data from reservoirs from 2003 to 2020 were used, which were provided by the Environmental Company of the State of São Paulo (CETESB), Brazil. The adjusted modified WQI employing weight redistribution (WQIRWAdj or WQISOL) presented the most promising results, with a Pearson correlation of 0.92 and a success rate of 72.6% and 97.0% for the CETESB and simplified classifications, respectively. WQISOL, which was proposed in the present study, exhibited a satisfactory performance, allowing the water quality of reservoirs to be monitored remotely and in real-time.

Research on The Impact of Ciliwung River Water on The Surrounding Environment in The DKI Jakarta Area

Ciliwung River flows through central Jakarta, passing through many residential and densely populated areas that cause heavy pollution in the Ciliwung River resulting from wastewater and solid waste. This research aims to know the water quality and pollution level in the Ciliwung river and determine the locations to install water quality online monitoring stations. The research method used in this study is to analyze the quality of water samples taken from several points of the Ciliwung River. The analysis results show that the Ciliwung river is highly polluted, especially in the areas going downstream of the river. In addition, the analysis results have determined 8 (eight) points/locations where the online water quality monitoring devices will be installed. Due to limited funds, however, only 3 (three) online monitoring devices are currently installed on site, representing upstream, middle, and downstream areas of the Ciliwung river.

A water fraction measurement method of gas-water flow in a wide conductivity range

The water fraction of gas-water flow can be measured by a microwave-based approach, but the measurement is unstable if water conductivity is changed. To overcome this, the present study proposes an on-line water fraction measurement method taking advantages of a microwave transmission line sensor. An equivalent circuit and a two-port network are established to reveal that, in a high conductivity environment, the sensor’s S21 phase is linearly related to phase constant and can measure the water fraction. Considering an air-water stratified flow, we conduct experimental and numerical investigations on the variation of the phase against conductivity and water fraction. It is found that 3.31–8.52 S/m conductivity has no effect on the phase. Subsequently, experimental and numerical models are developed for predicting 0–100% water fraction. Both absolute errors are within 2.2% at 95% confidence probability. The simulation method is a low-cost alternative of traditional gas-water flow testing.