Water Quality Monitoring System Research Articles

IOT Based Waterproof System for Water Quality and Level Monitoring

Abstract: The Internet of Things (IoT) represents a significant advancement in the monitoring and management of environmental resources. This review explores the design and implementation of a waterproof IoT-based system for the remote monitoring of water quality and levels [2]. Water pollution, particularly in developing regions such as India, poses a substantial environmental threat, primarily driven by factors like untreated sewage, agricultural runoff, and industrial waste. Conventional approaches to monitoring water quality, which rely on physical sampling and laboratory testing, tend to be less efficient and often fail to deliver the prompt information required for effective resource management. To overcome these limitations, the proposed system utilizes waterproof IoT devices equipped with various sensors to continuously assess critical water quality parameters, including pH, turbidity [3], and pollutant levels. The collected data is transmitted in real time to a central monitoring unit, allowing for immediate assessment and timely interventions when quality thresholds are exceeded. This review emphasizes the innovations in sensor technology and communication protocols that enhance the efficiency and reliability of the monitoring system. Additionally, the paper addresses challenges such as sensor calibration, durability, and data transmission reliability in different environmental conditions. Solutions to these challenges are discussed to provide a comprehensive understanding of how to develop and maintain effective water quality monitoring systems. The review concludes by outlining future research directions aimed at improving system robustness and expanding its application across various water bodies, thereby contributing to enhanced water resource management and environmental sustainability [1].

Advancing aquaculture: fuzzy logic-based water quality monitoring and maintenance system for precision aquaculture

AbstractAquaculture plays a vital role in global food production, and maintaining optimal water quality is essential for the health and growth of aquatic species. This research addresses the need for an efficient, adaptive solution by introducing an innovative water quality monitoring and maintenance system for aquaculture ponds. Unlike conventional systems, our approach uniquely integrates fuzzy logic with IoT technologies to optimise the precision and adaptability of pond management. The system stands out with its continuous monitoring of critical parameters such as temperature, pH, dissolved oxygen (DO), weather conditions and salinity and its ability to autonomously adjust operational controls, such as aerators and water pumps, based on dynamic environmental changes. This ensures ideal water conditions without manual intervention, providing a reliable and effective solution for aquaculture pond management. This work’s novelty lies in applying fuzzy Logic to handle the complexity and variability of aquaculture environments, allowing for nuanced control decisions that improve water quality management. The system’s efficiency was demonstrated through a 72-h operational test, where it maintained optimal DO and salinity levels, showcasing its reliability and effectiveness in real-world conditions. The fuzzy logic model has demonstrated a commendable accuracy rate of 98%. These results validate the system’s performance and underscore its practical benefits, meeting the demands of aquaculture production and significantly enhancing operational efficiency by enabling remote monitoring and rapid issue identification. This research contributes a robust technological solution for aquafarmers, offering a promising advancement in aquaculture management by improving productivity and ensuring the health and growth of aquatic species.

AI-Aided Robotic Wide-Range Water Quality Monitoring System

AI-Aided Robotic Wide-Range Water Quality Monitoring System

Real-time water quality monitoring along the lower stretch of River Ganges and its connected drains using sensors and Water Quality Index

ABSTRACT In this study, water quality is measured using a Real-Time Water Quality Monitoring System for a total of 84 times at 14 locations, in which four drains discharged sewage directly to river Ganges at Bihar, five locations on river Ganges at West Bengal, two tributaries of river Ganges at West Bengal and three drains discharged wastewater directly to river Ganges at West Bengal. In Bihar, four major drains discharged 377.98 MLD wastewater into the river Ganges which in turn released 8985.17 kg of BOD per day. In West Bengal, three major drains had a total flow of 1630.88 MLD that discharged 11563.77 kg of BOD per day to the River Ganges. The average Water Quality Index of the river Ganges was observed as 64.55 and that of drains of West Bengal and Bihar were recorded as 309.78 and 775.56, respectively. The average BOD to COD ratio of less than 0.6 indicated wastewater from drains of Bihar and West Bengal was nonbiodegradable. DO and pH values were recorded compliant with the bathing water quality standard at all river locations, however, the average BOD value at five monitoring points of the river Ganges and its tributaries was slightly above the bathing water quality standard.

DEVELOPMENT OF WATER QUALITY MONITORING SYSTEM IN SHRIMP PONDS BASED ON INTERNET OF THINGS (IOT)

This type of research is research and development (R&D) which uses the waterfall development model. The aim of this research is to produce a water quality monitoring system in shrimp ponds based on the Internet of Things (IoT) and to find out the results of testing the development of the system using the ISO 25010 standard. This development involves the application of ESP32, Relay, Toa Siren, DS18b20 Temperature Sensor, pH Sensor Module 4502C, Water Salinity and Turbidity Sensor which allows users to get real-time pond water quality monitoring information via the Telegram application. The test results for the reliability aspect have met the fault tolerance sub-characteristic standards because the percentage of measurement error obtained for the temperature sensor was 0.10%, pH 0.54%, salinity 1.19%, and turbidity 1.00%, still within the permitted tolerance limits. Functionality suitability testing shows that this tool obtains a percentage of 100%, indicating that the system is suitable for use. This tool also meets the portability aspect because it can be used on various devices with different operating systems. Apart from that, the usability evaluation results from user responses are in the very good category with an average of >4.2.

A Study the Water Quality Measuring Equipment with Automatic Sensor System

This paper studied the water quality in Dong Khoai Village, Xaythani District, Vientiane Capital, which is the primary water pumping location for agriculture. The area has a population of over 1735 citizens and cover more than 450 hectares. Common issue found in Markhiaw river. Stream is its toxicity to certain rice field, causing it inability to grow rice despite using fertilizer. Furthermore, this stream gives dissatisfaction odor, negatively affects people who get into the water for fishing and occasionally found death fish in the areas. In order to assess the water quality and disseminate the results to the public. Therefore, this study aims to investigate the creating of an automated sensor system for high precision water quality monitoring in the water. Four sensors; pH (hydrogen potential), EC (electro conductivity), TDS (total dissolved solid), and temperature are used in this procedure. The sensor system was tested, and build a small station uses solar energy and saves information into the SD card and available online by Blynk, and compare the test results with national environmental standards. The research revealed that, in comparison to the standard instrument S5 pro parameter, the measuring stations for the water quality values of all four sensors are accuracy value of 92.91% to 99.34%. Based on the information gathered over the course of two months, from February 13 and March 18, the pH value will be low, at 5. The pH value is high, at 11, between 13, 17 and 18 February 2024. These numbers are considered to be in the standard ranks. Regarding the dissolved solids in the water vary from 3 to 100 mg/L, and the electrical conductivity is between 100 and 180 µS/cm. They are also fall upon the National Environmental Standard. The Temperature value is high, at 40.81 ºC on 4 February 2024 and from 02 March 2024, the Temperature value will be low, at 24.4ºC.

Water Quality Monitoring and Control System for Tilapia Cultivation Based on Internet of Things

This research analyzes the quality of water for tilapia habitat which is a type of brackish water fish that is currently widely cultivated by pond farmers. This fish is the choice because of its flexibility regarding habitat. However, despite having flexibility in terms of habitat, each harvest of tilapia that lives in a different habitat will produce tilapia with different quantity and quality. Currently, many tilapia farmers still carry out the cultivation process using traditional methods using ponds. Kuala Kerto Village, Lapang District, North Aceh is one of the locations where many tilapia fish farmers use ponds as a habitat for this fish. Not infrequently, changes in natural conditions such as rain and floods have an impact on tilapia fish ponds in this village. Thus, crop yields are very varied, often even resulting in losses. One of the reasons for this is that there is still minimal use of technology in tilapia cultivation in this village. The design of a water quality monitoring and control system for IoT-based tilapia cultivation in this research was carried out to help the problems of tilapia pond farmers. Through this research, a tool was produced in the form of a prototype IoT device that can be used to monitor and control water quality in tilapia fish ponds. This device utilizes several sensors such as turbidity sensors, ammonia sensors, salinity sensors, pH sensors, and several other sensors as data takers which will later be transmitted and displayed via a web application. Research and development of this device uses the RD method, namely research and development.

Real-Time Monitoring of Seawater Quality Parameters in Ayia Napa, Cyprus

Real-time monitoring systems are crucial for the comprehensive management of operations and processes, as well as for assessing the impacts of coastal infrastructures on the marine environment. These systems not only support environmental protection and data-driven decision-making but also enable the early detection of adverse events and the issuance of timely warnings for prompt responses. Although water quality is a critical parameter in this monitoring framework, there are currently limited permanent systems in place dedicated to maintaining these objectives. Even fewer systems leverage their data for research purposes, leading to a gap in the literature regarding effective processing approaches for real-time water quality data. In this context, this study presents a real-time water quality monitoring system integrated into a broader in-field laboratory installed at a coastal area off the coast of Ayia Napa, Cyprus, as well as an initial measured data set of different qualitative quantities. It proposes a holistic approach for post-processing real-time seawater quality data, employing both time and frequency domain analyses, alongside filtering techniques. The study discusses the advantages of each method and emphasizes the importance of their combined use. Utilizing data collected from a three-month operational period, the study assesses the current state of marine seawater quality and examines both temporal and cyclic variations in various seawater quality parameters. The findings reveal that the examined seawater parameters are within reasonable values, indicating that the construction and operation of a nearby marina and the necessary infrastructures (e.g., breakwater) did not affect the seawater quality in the area. Additionally, the study identifies pronounced daily cyclic responses in different seawater quality parameters, including temperature, density, pH, dissolved oxygen, and turbidity. Finally, notable correlations are observed between temperature and dissolved oxygen, temperature and conductivity, oxidation–reduction potential (ORP) and salinity, ORP and dissolved oxygen, and ORP and TDS.

Review on Evaluation and Analysis of Water Quality Monitoring Systems Using IoT

Review on Evaluation and Analysis of Water Quality Monitoring Systems Using IoT

Integrating deep learning techniques for effective river water quality monitoring and management

Effective river water quality monitoring is essential for sustainable water resource management. In this study, we established a comprehensive monitoring system along the Kaveri River, capturing real-time data on multiple critical water quality parameters. The parameters collected encompassed water contamination levels, turbidity, pH measurements, temperature, and total dissolved solids (TDS), providing a holistic view of river water quality. The monitoring system was meticulously set up with strategically positioned sensors at various river locations, ensuring data collection at regular 5-min intervals. This data was then transmitted to a cloud-based web portal, facilitating storage and analysis. To assess water quality, we introduced a novel hybrid approach, combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The proposed CNN-LSTM model achieved a validation accuracy of 98.40%, surpassing the performance of other state-of-the-art methods. Notably, the practical application of this system includes real-time alerts, promptly notifying stakeholders when water quality parameters exceed predefined thresholds. This feature aids in making informed decisions in water resource management. The study's contributions lie in its effective river water quality monitoring system, which encompassing various parameters, and its potential to positively impact environmental conservation efforts by providing a valuable tool for informed decision-making and timely interventions.

Perceptions and knowledge about the use of biological indicators in freshwater ecosystem monitoring in Rwanda

The use of biological monitoring (biomonitoring) to assess water quality is recognised alongside the use of chemical and physicochemical parameters due to its ability and efficiency in providing information about both current and long-term changes. Indeed, biomonitoring is applied in several developed and developing countries. Nevertheless, some developing countries, such as Rwanda, are yet to adopt such a monitoring system. This paper presents results of an assessment of the knowledge and perceptions by water resource managers about the use of biomonitoring and bioindicators, and challenges to their integration into existing routine water quality monitoring systems in Rwanda. Qualitative research, using semistructured interviews, was conducted with nine water resource practitioners from six water governance institutions between August and November 2021. The results show that participants are aware that macroinvertebrates, algae and fish are bioindicators that have potential applications in Rwanda to complement the chemical and physicochemical parameters already being collected in the country’s water monitoring system. The main challenges for integration of biological indicators, as indicated by participants, include the need for equipment, funding, technical skills and taxonomic knowledge. Training in the use of bioindicators and taxonomy are needed to raise the awareness and skills of staff from the institutions involved in freshwater ecosystem management in Rwanda, and to encourage integration of biomonitoring results into national water monitoring frameworks.

Design and realization of a water quality monitoring system based on the Internet of Things

ABSTRACT Water quality monitoring is a key means of ensuring the safety of water resources, protecting the ecological environment and maintaining public health. This paper describes a water quality monitoring system based on the Internet of Things (IoT), which supports real-time monitoring, remote control and intelligent early warnings. It is concluded that the system can effectively collect, process and display data on water quality. To further improve the level of intelligence of the water quality monitoring system, the ability of machine learning algorithm to analyze and predict the water quality data has also been separately tested. In this paper, the hardware design, software design, test results and algorithm evaluation of the system are described. The actual performance and reliability of the system in practical application under various field conditions have not yet been verified. This new system is compared with other water quality monitoring systems.

An Effective Smart Water Quality Monitoring and Management System Using IoT and Machine Learning

An Effective Smart Water Quality Monitoring and Management System Using IoT and Machine Learning

Enhanced Hydroponic Farming with Crop Suitability Prediction

Hydroponics, a soil-less cultivation technique, offers efficient resource utilization and enhanced crop yields. However, precise water quality control is essential to ensure optimal nutrient absorption and successful crop growth. This research paper presents a comprehensive IoT-based water quality monitoring system specifically designed for hydroponics, incorporating pH, turbidity, and temperature sensors. The system can acquire data from sensors, data processing, and data visualization in real time. The sensor data is seamlessly transmitted from Arduino to ESP8266 and securely stored on the ThingSpeak cloud platform. Utilizing web scraping techniques, the collected data is extracted from ThingSpeak and seamlessly integrated into a user-friendly website. On the website, a machine learning (ML) model, trained on required data, automatically processes the realtime sensor readings and performs crop suitability predictions. The system enables continuous and automated monitoring of key water quality parameters, ensuring optimal conditions for hydroponic crop growth. The deployed ML model demonstrates remarkable performance in predicting water suitability for different crops. The model provides real-time predictions on the website by utilizing the data, empowering hydroponic farmers with data-driven insights to optimize crop selection based on water quality. To evaluate the system’s efficacy, extensive experimentation, and validation have been conducted using hydroponic setups with various crop types.

Real-time water quality monitoring using AI-enabled sensors: Detection of contaminants and UV disinfection analysis in smart urban water systems

This study introduces a novel method for assessing water quality, employing a cutting-edge sensor system integrated with artificial intelligence (AI) technologies. Addressing the global challenge of water scarcity and pollution, the research focuses on the innovative use of spectroscopic analysis for real-time water quality monitoring. The study evaluates the effectiveness of this system in distinguishing between clean, contaminated, and UV-disinfected water samples, highlighting its precision in detecting variations in water quality. Central to the research is the deployment of advanced machine learning algorithms, including Random Forest, Support Vector Machines (SVM), and Neural Networks, to process and classify spectral data. These models demonstrate remarkable accuracy in real-time classification, underscoring the synergy between AI and environmental science in addressing critical public health issues. Significantly, the study showcases the potential of UV disinfection in water treatment, as evidenced by the spectral changes observed in disinfected water samples. This aspect of the research emphasizes the role of spectral analysis in verifying the efficacy of water treatment processes. Overall, this study paves the way for more sophisticated and accessible water quality monitoring systems, offering a promising solution to one of the most pressing environmental challenges. The integration of AI and spectral analysis in this research offers a breakthrough in ensuring a safe water supply and effective water resource management. This study utilizes advanced machine learning algorithms, including Random Forest, Support Vector Machines (SVM), and Neural Networks, for water quality assessment. These models process and classify spectral data with high precision, highlighting variations in water quality.

Prediction of Physico-Chemical Parameters of Surface Waters Using Autoregressive Moving Average Models: A Case Study of Kis-Balaton Water Protection System, Hungary

In this work, the authors provide a case study of time series regression techniques for water quality forecasting. With the constant striving to achieve the Sustainable Development Goals (SDG), the need for sensitive and reliable water management tools has become critical. Continuous online surface water quality monitoring systems that record time series data about surface water parameters are essential for the supervision of water conditions and proper water management practices. The time series data obtained from these systems can be used to develop mathematical models for the prediction of the temporal evolution of water quality parameters. Using these mathematical models, predictions can be made about future trends in water quality to pinpoint irregular behaviours in measured data and identify the presence of anomalous events. We compared the performance of regression models with different structures for the forecasting of water parameters by utilizing a data set collected from the Kis-Balaton Water Protection System (KBWPS) wetland region of Hungary over an observation period of eleven months as a case study. In our study, autoregressive integrated moving average (ARIMA) regression models with different structures have been compared based on forecasting performance. Using the resulting models, trends of the oxygen saturation, pH level, electrical conductivity, and redox potential of the water could be accurately forecast (validation data residual standard deviation between 0.09 and 20.8) while in the case of turbidity, only averages of future values could be predicted (validation data residual standard deviation of 56.3).

Evaluation of LoRa Network Performance for Water Quality Monitoring Systems

Conserving water resources from scarcity and pollution is the basis of water resource management and water quality monitoring programs. However, due to industrialization and population growth in Malaysia, which have resulted in poor water quality in many areas, this program needs to be improved. A smart water quality monitoring system based on the internet of things (IoT) paradigm was designed to analyze water conditions in real time and enable effective water management. Long-range (LoRa) application of the low-power, wide-area networking concept has become a phenomenon in IoT smart monitoring applications. This study proposes the implementation of a LoRa network in a water quality monitoring system-based IoT approach. The LoRa nodes were embedded with measuring sensors pH, turbidity, temperature, total dissolved solids, and dissolved oxygen, in the designated water stations. They operate at a transmission power of 14 dB and a bandwidth of 125 kHz. The network properties were tested with two different antenna gains of 2.1 dBi and 3 dBi, with three different spread factors of 7, 9, and 12. The water stations were located on the Sungai Pantai and Sungai Anak Air Batu rivers on the Universiti Malaya campus, Malaysia. Following a dashboard display and K-means analysis of the water quality data received by the LoRa gateway, it was determined that both rivers are Class II B rivers. The results from the evaluation of LoRa performance on the received strength signal indicator, signal noise ratio, loss packet, and path loss at best were −83 dBm, 7 dB, <0%, and 64.41 dB, respectively, with a minimum received sensitivity of −129.1 dBm. LoRa has demonstrated its efficiency in an urban environment for smart river monitoring purposes.

Study on environmental monitoring classification system based on improved bayesian algorithm

Abstract The commonly used methods for classifying water quality data are based on water quality parameters. The naive Bayesian classification method can be applied to substitute different evaluation criteria and selected water quality parameters for different water bodies. Compared to other water quality data classification methods, naive Bayesian classification methods have advantages such as simple calculation, high classification accuracy, and strong universality. However, this method overlooks the correlation between various water quality parameters and categories. To address the issues of poor universality, computational complexity, and low accuracy of traditional water quality data classification methods, a water quality data classification method based on weighted naive Bayes is proposed. This method comprehensively considers the impact of water quality attributes and their values on the classification results and replaces the original naive Bayes with weighted attribute conditional probabilities to make the classification results as close as possible to the actual category of the sample. The results indicate that this method exceeds 94% accuracy and can be directly utilized as a water quality classification module in water quality monitoring systems.

Real-time Water Quality Monitoring System using IoT: Application at Melaka River, Malaysia

The rapid development around the Malacca city has a huge impact on the environment. Among the effects that can be seen is water pollution along the Malacca River. To overcome the problem, Hydro Quality Monitoring System (HydroQS) has been built and is a co-developed product between Universiti Teknikal Malaysia Melaka (UTeM) and Perbadanan Pembangunan Sungai dan Pantai Melaka (PPSPM). This device acts as one of the solutions to continuous monitoring the river water quality in real time values such as pH, Dissolved Oxygen (DO), Total Dissolved Solid (TDS), Turbidity and Temperature via Arduino interface. Prior to conduct field test, the HydroQS will perform floating and sensors functionality test at laboratory environment. As a results, the HydroQS capable to float and the sensors able to capture the water quality data.

Prototype of Household Water Quality Monitoring System by Utilizing Internet of Things

This research aims to develop a household water quality monitoring system using the Internet of Things (IoT) by monitoring water quality parameters such as pH, Total Dissolved Solids (TDS), turbidity, and temperature in real time. The prototyping method in the development of this system allows continuous interaction between developers and users to meet the needs of the system. Data is collected through literature studies and observations to support system development. The results of this study are that every 1 minute, the sensor data is sent to the server, and then the sensor data will be displayed on the web platform; if the sensor data value is not at the predetermined value limit according to Permenkes number 2 of 2023, the displayed value will be red. The data collected by the sensor is sent to the server built with Node.js and stored in the PostgreSQL database. The monitoring results will also be displayed on a 20x4 I2C LCD. The system successfully monitors water quality parameters in real time, with results displayed on a web platform that is easily accessible to users. The implementation of IoT technology in this system provides an innovative solution for increasing public awareness of the importance of clean water quality. With this system, it is expected to create a healthier and safer household environment for families.