Water Quality Monitoring System Research Articles

Development of LoRaWAN-based IoT system for water quality monitoring in rural areas

This article delineates the design and deployment of an innovative real-time water quality monitoring system tailored for rural regions, focusing on monitoring the water resource quality parameters. We propose a solar-powered, waterproof, portable, and Internet of Things (IoT)-enabled solution that leverages Long Range Wide Area Network (LoRaWAN) technology. Central to this system is a sophisticated LoRa node outfitted with an array of sensors for capturing key water parameters, such as pH, total dissolved solids, turbidity and temperature. A conjunction of an Arduino microcontroller-based board and a LoRa shield facilitates real-time data capture and transmission to a LoRaWAN gateway. The acquired data is transmitted to The Things Network server, which is seamlessly integrated with a ThingSpeak web-based IoT server and ThingView mobile applications. We incorporate a solar cell with a solar shield to ensure sustainable energy provision for powering the entire system through a rechargeable battery. This allows users to access vital water quality information online simultaneously and continuously in real-time. As a testament to its robustness, the system was empirically tested at Gambang Lake to demonstrate its effectiveness, functionality, buoyancy, and waterproof capabilities. We further validated the results by comparing them with laboratory sample analysis findings. Experimental evaluations confirmed the system's reliability, as evidenced by the strong agreement between the water conditions measured using our solution and those obtained from laboratory instruments. Moreover, our system efficiently and remotely updated data across multiple IoT platforms using the LoRa radio interface over the LoRaWAN gateway.

Optimizing Brackishwater Shrimp Farming with IoT-Enabled Water Quality Monitoring and Decision Support System

Optimizing Brackishwater Shrimp Farming with IoT-Enabled Water Quality Monitoring and Decision Support System

Monitoring dan Klasifikasi Kualitas Air Kolam Ikan Gurami Berbasis Internet of Things Menggunakan Metode Naive Bayes

Ministry of Marine Affairs and Fisheries (KKP) noted that Indonesia produced 56,539 tons of gourami fish in the second quarter of 2022 High market demand and economical selling prices encourage farmers to cultivate gourami fish. In cultivating gourami fish there are several obstacles, for example, disease caused by poor water quality. Water quality is the main parameter in the success of gourami fish farming. This research aims to develop a water quality monitoring system based on the Internet of Things. The system prototype uses a temperature sensor (DS18B20), Ph sensor (dfrobot SEN0161), turbidity sensor (dfrobot SEN0189), flowmeter, and ultrasonic sensor (JSN-SR04) as input. The Arduino Mega R3 microcontroller is the processor and the Oled module (SSD1306) is the output. Thingboard is a cloud server that functions as sensor data monitoring. Temperature sensor testing results (DS18B20) average error 0.48%, Ph(dfrobot SEN0161) sensor testing average error 0.64%, ultrasonic sensor testing (JSN-SR04) average error 7.83%, testing Turbidity sensors can measure the level of water turbidity. Next, the water quality parameter data is processed using the Naïve Bayes algorithm method for classifying the water quality of gourami ponds. The results of this classification obtained an accuracy of 99.94% a Kappa Statistics value of 0.9989 and a Mean Absolute Error of 0.0003

Smart Water Quality Monitoring System In Progo River, Magelang Regency, Central Java

Traditionally, air quality detection is done manually by means of air samples obtained locally and sent for analysis to a laboratory, which takes a long time, is expensive, and requires human resources. This technique does not provide real-time data. Therefore, there is a need for innovation and new technology that makes it easier to monitor and analyze air quality data in real-time with a microcontroller approach and certain water quality sensor parameters, where the recorded data will be recorded and sent via wireless with the Internet of Things (IoT) approach. Parameter values such as temperature, humidity, water level, turbidity, and pH in general can be properly recorded by each sensor. The goal of this research is to create measuring equipment for aquatic characteristics such as pH, water turbidity, water level, ambient air temperature, and humidity using the Thingspeak website and telegram. System design, programming the ATmega2560-based Arduino Mega microcontroller and ESP8266-based NodeMCU using the Arduino IDE, and constructing and testing equipment on a prototype scale and directly in the field are the methods employed in this research. According to the results of field testing, the water temperature sensor is capable of capturing fluctuating values ranging from 32.4 to 34.1 oC. Meanwhile, the air humidity sensor measured 33.5-35.6% humidity. The ultrasonic sensor records the height of the river water level pretty well, with a value of 28-71 cm. A pH sensor measures the pH of the water and records data in the form of water voltage ranging from 3.34 to 5.38 volts. Based on IoT, the sensor recording results are subsequently sent online and received via the Telegram application.

Ethogram of the Chinese Giant Salamander during the Breeding Period Based on the PAE Coding System

The PAE (Posture-Act-Environment) coding system is a behavior coding system that divides the study of animal behavior into postures, actions, and the corresponding environmental factors, and they are coded correspondingly. It determines the analysis dimension to standardize the study of behavior. To investigate the behavior of A. davidianus during the breeding period, as well as their related postures, actions, and required environmental conditions, this study monitored the behavior of four pairs of A. davidianus in a simulated natural breeding pool using an infrared image monitoring system and recorded the changes in water quality during this process using a water quality monitoring system. The process of reproductive behaviors was observed and recorded with the random sampling method and the focal animal sampling method to classify and code the behaviors, and the ethogram of A. davidianus during the breeding period was constructed based on the PAE coding system. The result showed that 10 postures, 33 actions, 11 environments, and 45 behavioral patterns were differentiated and defined, which were classified into 9 categories of behaviors according to the behavioral function. Among these categories, five were distinguished as behaviors unique to the reproductive period, which include sand pushing, showering, courtship, oviposition, and parental care. The remaining four categories were daily behaviors: exercise, feeding, rest, and miscellaneous behaviors. The quantitative data on water quality and habitat factors that had a significant impact on the behavior of A. davidianus, such as water temperature (WT), pH, and dissolved oxygen (DO), were included in the coding framework, which more accurately expresses the environmental conditions and thresholds required for the breeding behavior.

Integrated intelligent system for water quality monitoring and theft detection

Abstract In India, water demands have risen inexorably over the last decade. The world's growing need for water has become a huge concern. The resource has been depleted due to wastewater, global warming, and industrialization. The importance of resource conservation and management must be prioritized. This work introduces an Internet of Things concept for water scanning and management in this work, which provides real-time feature extraction via the Internet. The suggested strategy addresses emerging needs within the aquatic sector, like gauging flow velocity and the imperative for scrutinizing water provision to minimize wastage and promote conservation. It also utilizes pH and conductivity sensors to examine the excellence of the aquatic that is distributed to every home. Traditional water metering methods necessitate consistent personal engagement for conservation, rendering them unwieldy and inefficient. To address the shortcomings of existing models, the proposed strategy employs wireless technology for smart quality sensing and information communication.

Resource Allocation Optimization in IoT-Enabled Water Quality Monitoring Systems.

Water quality monitoring systems that are enabled by the Internet of Things (IoT) and used in water applications to collect and transmit water data to data processing centers are often resource-constrained in terms of power, bandwidth, and computation resources. These limitations typically impact their performance in practice and often result in forwarding their data to remote stations where the collected water data are processed to predict the status of water quality, because of their limited computation resources. This often negates the goal of effectively monitoring the changes in water quality in a real-time manner. Consequently, this study proposes a new resource allocation method to optimize the available power and time resources as well as dynamically allocate hybrid access points (HAPs) to water quality sensors to improve the energy efficiency and data throughput of the system. The proposed system is also integrated with edge computing to enable data processing at the water site to guarantee real-time monitoring of any changes in water quality and ensure timely access to clean water by the public. The proposed method is compared with a related method to validate the system performance. The proposed system outperforms the existing system and performs well in different simulation experiments. The proposed method improved the baseline method by approximately 12.65% and 16.49% for two different configurations, demonstrating its effectiveness in improving the energy efficiency of a water quality monitoring system.

IMPLEMENTATION OF INTERNET OF THINGS (IoT) BASED ON GOOGLE SHEETS FOR WATER QUALITY MONITORING SYSTEM

Temperature, pH, and TDS are important indicators of water quality that affect various aspects. This research aims to develop a water quality monitoring system by implementing Internet of Things (IoT) technology based on Google Sheets. Based on the tests for each sensor - temperature, pH, and TDS - the results show that the system has an excellent level of measurement precision, with an average precision value of above 95%. The findings also indicate that Google Sheets can be utilized in IoT technology and can operate on more than one distinct computer device at the same time. The use of Google Sheets in IoT technology is highly effective, flexible, and user-friendly. The measured data can be automatically stored and can be visualized effectively.".

IoT Based Smart Betta Fish Monitoring system with fish fatality prediction.

This study enlightens the importance of rearing water quality to Betta fish health. A water quality monitoring system was developed based on water quality parameters namely water pH, temperature (°C) and TDS level (ppm). Fuzzy Logic Algorithm was applied to predict the possibility of the fish to get infected by the disease using combination of the water quality parameters value. Graphical User Interface (GUI) was developed to test the efficiency of the fish disease prediction system using fuzzy logic algorithm before the fuzzy rule been embedded to the IOT system. Arduino Uno Wi-Fi R2.0 and Blynk Apps used for enabling the system to update the aquarium water quality to owner in real-time. Hydroponic technology implemented in this project for recirculate rearing water inside the fish tank. Theoretically, the aquaponic system will help regulate the water tank parameters in optimum range and Betta Splendens should be free from all diseases.

IOT Based Water Quality Monitoring System

Abstract: Technologies are expanding and developing quickly across a wide range of disciplines as science continues to advance and improve. The improvement of human existence and modernization are the main goals of these developments. It is crucial to recognize that this advancement has come at the expense of making environmental issues worse. Pollution of water is one of the main threats in recent times as drinking water is getting contaminated and polluted. The polluted water can cause various diseases to humans and animals, which in turn affects the life cycle of the ecosystem [1]. The ESP32 microcontroller, along with DS18B20 Digital temperature sensor, turbidity sensor, and pH sensor, are used in the IoT-based water quality monitoring system presented in this project to continuously monitor and assess the water's quality. The ESP32 is programmed using the Arduino IDE software, and the Blynk IoT platform is used for data visualization and remote monitoring. Water temperature, turbidity levels, and pH values may be continuously monitored by the system, and users can access and analyze the data remotely using the Blynk mobile app or web dashboard. It is possible to study historical data patterns and set up alerts to inform users of any changes in the quality of the water. By enabling proactive actions to be performed to maintain and improve water quality while ensuring environmental protection, this system offers an efficient and effective method for managing water quality.

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.

Impact of Ecological Factors on Water quality goals

A deeper knowledge of the processes that influence environmental elements in areas such as water management, consumer services, water reuse acceptability, and risk communication, among others. Also, as a result of the unique climate, soil features, and agricultural idiosyncrasies, water quality and water management for human welfare and the natural environment. Also, various kinds of contamination may also have an impact on aquatic habitats across the world. Trace metals are one of the most major pollutants that have harmed marine environments. Its release into the marine environment is caused by both natural and human activities. Erosion and pollution are two of the negative repercussions of agricultural operations on soil quality. Pollution from nutrient leaching and intrusion is one of the consequences for water resources. To choose the best long-term solutions for mitigating these effects, researchers should focus on developing an accurate water and soil quality monitoring system at many scales based on a functional evaluation. On the other hand, the review focuses on the influence of metals and nutrients on water quality for water usage and its impact on agricultural output. Its mean water quality great impact on natural and human on the other hand effect on environment and ecosystem. finally this review indicated that many ecological factors positive effects on water quality goals.

STM32 BASED WATER QUALITY MONITORING SYSTEM

Water is a precious and essential resource for all life on Earth. Ensuring the quality of water is vital for human health, environmental sustainability, and various industrial applications. This project introduces a novel STM32-based Water Quality Monitoring System designed to address the need for continuous and real-time monitoring of water parameters in various settings like pH and electrical conductivity sensors. The STM32-based Water Quality Monitoring System offers several advantages, including early detection of water pollution, enabling prompt action to mitigate environmental damage and health risks. It provides historical data for trend analysis, aiding in the identification of long-term water quality patterns and trends. Additionally, it reduces the need for manual sampling and laboratory testing, thus lowering operational costs for water treatment facilities. Furthermore, the system is highly adaptable and can be integrated into a variety of water management scenarios, including municipal water supplies, industrial processes, aquaculture, and environmental monitoring. It promotes sustainable water resource management by ensuring that water quality remains within safe and regulated standards In summary, this project presents an innovative STM32-Based Water Quality Monitoring System that leverages advanced microcontroller technology to address the critical issue of water quality monitoring. Key Words: water quality, stm32, pH sensor, conductivity sensor, aquaculture

Internet of Things Enabled Smart Water Excellence Observing and Outflow Recognition System

Currently, global water demands are increasing at an alarming rate, largely due to the depletion of resources caused by factors such as water waste, climate change, and urbanization.Unfortunately, conventional water metering schemes are often performed manually by human resources, which can be both cumbersome and ineffective for conservation efforts. To address the shortcomings of the-state-of-the models, this paper introduces an autonomous water quality monitoring system based on IoT technology to optimize water utilization. The proposed system analyzes and monitors the usage of water in the aquatic industry such as circulation speed measurement and optimization of water supply to reduce water waste and encourage conservation. Also, the proposed system uses pH and conductivity sensors to monitor the quality of the water that is delivered to each residence.

Development of River Flow and Water Quality Using IOT-based Smart Buoys Environment Monitoring System

Efficient and accurate river water quality monitoring is needed to support laboratory testing based on on-site sampling. Therefore, we have developed a monitoring system for river flow and water quality using sensor-equipped buoys and the Internet of Things (IoT) concept. An ESP32 NodeMCU microcontroller integrated with WiFi and compatible with Arduino IDE is used in the system. The buoy is equipped with GPS to determine the position and flow speed and sensors to measure water quality parameters of pH and temperature. Data on position, flow velocity, and water quality parameters are transmitted over a WiFi network using the MQTT protocol. The data is recorded by the buoy and uploaded and displayed on the adafruit.io platform. Positioning was done by comparing the values displayed on the Neo-6M GPS with the Maps application on the smartphone. The results show that the GPS coordinate values are accurate. The water quality parameter values obtained have an error rate of 3.3% for the pH sensor and 1.02% for the temperature sensor. Thus, the system we have developed has the potential to be used as a substitute for field sampling-based river water quality monitoring systems.

Node Layout Optimization Strategy Based on Aquaculture Water Quality Monitoring System

Node Layout Optimization Strategy Based on Aquaculture Water Quality Monitoring System

Research Trends in Smart Cost-Effective Water Quality Monitoring and Modeling: Special Focus on Artificial Intelligence

Numerous conventional methods are available for analyzing various water quality parameters to determine the water quality index. However, ongoing surveillance is necessary for large bodies of water. A water quality monitoring system supports a robust surface and groundwater ecosystem. Various tactics are used to improve aquatic habitats: identification of the precise chemical pollutants released into the aquatic environment; advancements in assessing ecological effects; and working on ways to enhance water quality through informing the public, communities, businesses, etc. In order to save the marine ecosystem and those who entirely depend on these enormous bodies of water, it is also crucial to continuously handle many data sets of water quality metrics. To predict the water quality index, this review paper provides an overview of water quality monitoring, the modeling and numerous sensors employed, and various artificial intelligence approaches. Various water quality models were proposed to assess pH, a few components, and alkalinity. Additionally, handling raw information for surface and groundwater quality metrics was studied using artificial intelligence techniques like neural networks.

Pattern detection and prediction using deep learning for intelligent decision support to identify fish behaviour in aquaculture

The United Nations’ Sustainable Development Goals (SDGs) underscore aquaculture as a sustainable practice. However, many countries rely on ineffective conventional methods to monitor the aquaculture. Fish behaviour monitoring systems lack an integrated strategy, often focusing on underwater footage analysis or water quality data. Additionally, limited research exists on using learning algorithms to identify sensor data anomalies, limiting the effectiveness of such systems. This study proposes a comprehensive and effective aquaculture monitoring system to address these issues. The work introduces a novel method integrating fish motion analysis with water quality metrics. The study also presents a learning system that anticipates potential water quality parameters. The proposed model has the potential to significantly enhance aquaculture management procedures and contribute to the UN’s SDGs through its ability to forecast water quality parameters and detect abnormal behaviour without requiring training detection and tracking algorithms.The study employs a large aquarium to capture water quality parameters and fish movement patterns. The study develops an FPGA-based water quality monitoring system and a prediction model to forecast potential water quality metrics. Among multiple LSTM models, the bi-directional LSTM performs well, achieving an RMSE score of 1.01 for water quality prediction. Subsequently, an outlier detection system is applied to identify unusual water quality parameters, triggering the behaviour analysis model. The behaviour analysis model uses an auto-encoder-based reconstruction approach to detect the frantic patterns in fish. The suggested behaviour analysis model has a f1-score of 0.68, demonstrating its reliability in contrast to conventional methods. The developed system is also tested on aquaculture site videos and includes a novel dataset of Sillago sihama with annotations for frantic behaviour patterns. The system works effectively as a decision support system for aquaculture.

Automatic Water Pumping and Quality Monitoring System

This project presents an innovative solution for automatic water pumping and real-time quality monitoring using the Blynk platform. It's designed to enhance water quality and pumping efficiency, ideal for hydroponics, aquaponics, and irrigation. Key components include an Arduino Uno, ultrasonic sensor for liquid levels, a relay for pump control, and a pH sensor. The system maintains water levels within defined thresholds and monitors pH levels, activating the pump when needed. Keywords: Automatic water pumping; Quality monitoring; Arduino UNO; pH sensor; Blynk platform

IOT BASED WATER QUALITY MONITORING SYSTEM

Water is one of the most vital resources for life, and protecting its quality is crucial for both the environment's health and the well-being of people. Monitoring and preserving the quality of the water is now essential given the mounting problems caused by pollution, climate change, and expanding populations. In this environment, the Internet of Things (IoT) has emerged as a ground-breaking innovation that has the potential to revolutionize the way we manage and monitor water quality. An IoT-based water quality monitoring system's relevance, components, advantages, and prospective applications are highlighted. It offers a chance to proactively address problems with water quality, protecting both the environment and the general people. Sensors used in IoT-based water quality monitoring systems can measure a wide range of factors, including temperature, dissolved oxygen, turbidity, pH levels, and chemical concentrations. These sensors are interfaced with the microcontroller unit. The sensors are positioned in pipelines, treatment facilities, or water bodies. Through wireless communication protocols like Wi-Fi or cellular networks, the data gathered by sensors is sent to a central server or cloud platform. This makes it possible to access and analyses data in real time. Users can use online or mobile applications to get the data on the quality of the water. These intuitive user interfaces offer up-to-the-minute data, historical trends, and alarms if water quality measurements differ from predetermined criteria. Urban populations can have access to safe drinking water by using IoT systems at water treatment facilities to monitor and manage water quality indicators. Aquaculture farms' production is increased by IoT- based systems' assistance in maintaining ideal water conditions for fish and other aquatic species. Monitoring soil and water quality helps farmers improve irrigation operations, which increases agricultural yields and decreases water waste. IoT is an excellent instrument for preserving water resources, defending the environment, and delivering real-time data, affordable solutions, and data-driven decision-making. Key Words: Microcontroller, Pumping station, Sensor, IoT, data, aquaculture.