Real-time Water Quality Monitoring Research Articles

Water Quality Monitoring System: Literature Survey and Design

Abstract: Given the significant obstacles that water pollution poses to the principles of green globalisation, it is more important than ever to guarantee a reliable and safe source of drinking water. This research presents a practical and affordableapproach to meet this need by proposing a real-time waterquality monitoring system that makes use of the Internet ofThings (IoT). The proposed system effectively incorporates awide range of sensors that are specifically designed to monitor physical and chemical parameters that are critical for assessingthe quality of water. These parameters include temperature, pH,turbidity, and water flow.

Autonomous UAV-Mounted LoRaWAN System for Real-Time Monitoring of Harmful Algal Blooms (HABs) and Water Quality

Autonomous UAV-Mounted LoRaWAN System for Real-Time Monitoring of Harmful Algal Blooms (HABs) and Water Quality

Graph neural network-based anomaly detection for river network systems

Background Water is the lifeblood of river networks, and its quality plays a crucial role in sustaining both aquatic ecosystems and human societies. Real-time monitoring of water quality is increasingly reliant on in-situ sensor technology. Anomaly detection is crucial for identifying erroneous patterns in sensor data, but can be a challenging task due to the complexity and variability of the data, even under typical conditions. This paper presents a solution to the challenging task of anomaly detection for river network sensor data, which is essential for accurate and continuous monitoring. Methods We use a graph neural network model, the recently proposed Graph Deviation Network (GDN), which employs graph attention-based forecasting to capture the complex spatio-temporal relationships between sensors. We propose an alternate anomaly threshold criteria for the model, GDN+, based on the learned graph. To evaluate the model’s efficacy, we introduce new benchmarking simulation experiments with highly-sophisticated dependency structures and subsequence anomalies of various types. We also introduce software called gnnad. Results We further examine the strengths and weaknesses of this baseline approach, GDN, in comparison to other benchmarking methods on complex real-world river network data. Conclusions Findings suggest that GDN+ outperforms the baseline approach in high-dimensional data, while also providing improved interpretability.

Dissolved Oxygen Forecasting for Lake Erie’s Central Basin Using Hybrid Long Short-Term Memory and Gated Recurrent Unit Networks

Dissolved oxygen (DO) concentration is a pivotal determinant of water quality in freshwater lake ecosystems. However, rapid population growth and discharge of polluted wastewater, urban stormwater runoff, and agricultural non-point source pollution runoff have triggered a significant decline in DO levels in Lake Erie and other freshwater lakes located in populated temperate regions of the globe. Over eleven million people rely on Lake Erie, which has been adversely impacted by anthropogenic stressors resulting in deficient DO concentrations near the bottom of Lake Erie’s Central Basin for extended periods. In the past, hybrid long short-term memory (LSTM) models have been successfully used for the time-series forecasting of water quality in rivers and ponds. However, the prediction errors tend to grow significantly with the forecasting period. Therefore, this research aimed to improve the accuracy of DO forecasting models by taking advantage of Lake Erie’s real-time water quality (water temperature and DO concentration) monitoring network to establish temporal and spatial links between adjacent monitoring stations. We developed hybrid LSTM models that combine LSTM, convolutional neuron network LSTM (CNN-LSTM), hybrid CNN with gated recurrent unit (CNN-GRU) models, and convolutional LSTM (ConvLSTM) to forecast near-bottom DO concentrations in Lake Erie’s Central Basin. These hybrid LSTM models improve their capacity to handle complicated datasets with spatial and temporal variability. These models can serve as accurate and reliable tools for forecasting DO concentrations in freshwater lakes to help environmental protection agencies better access and manage the health of these vital ecosystems. Following analysis of a 21-site Lake Erie dataset for 2020 and 2021, the ConvLSTM model emerged as the most accurate and reliable, boasting an MSE of 0.51 mg/L, MAE of 0.42 mg/L, and an R-squared of 0.95 over the 12 h prediction range. The model foresees future hypoxia in Lake Erie. Notably, the temperature near site 713 holds significance for Central Basin DO forecasting in Lake Erie, as indicated by outcomes derived from the Shapley additive explanations (SHAP).

An IoT Real-Time Potable Water Quality Monitoring and Prediction Model Based on Cloud Computing Architecture.

In order to achieve the Sustainable Development Goals (SDG), it is imperative to ensure the safety of drinking water. The characteristics of each drinkable water, encompassing taste, aroma, and appearance, are unique. Inadequate water infrastructure and treatment can affect these features and may also threaten public health. This study utilizes the Internet of Things (IoT) in developing a monitoring system, particularly for water quality, to reduce the risk of contracting diseases. Water quality components data, such as water temperature, alkalinity or acidity, and contaminants, were obtained through a series of linked sensors. An Arduino microcontroller board acquired all the data and the Narrow Band-IoT (NB-IoT) transmitted them to the web server. Due to limited human resources to observe the water quality physically, the monitoring was complemented by real-time notifications alerts via a telephone text messaging application. The water quality data were monitored using Grafana in web mode, and the binary classifiers of machine learning techniques were applied to predict whether the water was drinkable or not based on the data collected, which were stored in a database. The non-decision tree, as well as the decision tree, were evaluated based on the improvements of the artificial intelligence framework. With a ratio of 60% for data training: at 20% for data validation, and 10% for data testing, the performance of the decision tree (DT) model was more prominent in comparison with the Gradient Boosting (GB), Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) modeling approaches. Through the monitoring and prediction of results, the authorities can sample the water sources every two weeks.

Biological-based techniques for real-time water-quality studies: Assessment of non-invasive (swimming consistency and respiration) and toxicity (antioxidants) biomarkers of zebrafish

Swimming consistency and respiration of fish are recognized as the non-invasive stress biomarkers. Their alterations could directly indicate the presence of pollutants in the water ecosystem. Since these biomarkers are a routine process for fish, it is difficult to monitor their activity manually. For this reason, experts employ engineering technologies to create sensors that can monitor the regular activities of fish. Knowing the importance of these non-invasive stress biomarkers, we developed online biological behavior monitoring system-OBBMS and online biological respiratory response monitoring system-OBRRMS to monitor real-time swimming consistency and respiratory response of fish, respectively. We continuously monitored the swimming consistency and respiration (OCR, CER and RQ) of zebrafish (control and atrazine-treatments) for 7 days using our homemade real-time biological response monitoring systems. Furthermore, we analyzed oxidative stress indicators (SOD, CAT and POD) within the vital tissues (gills, brain and muscle) of zebrafish during stipulated sampling periods. The differences in the swimming consistency and respiratory rate of zebrafish between the control and atrazine treatments could be precisely differentiated on the real-time datasets of OBBMS and OBRRMS. The zebrafish exposed to atrazine toxin showed a concentration-dependent effect (hypoactivity). The OCR and CER were increased in the atrazine treated zebrafish. Both Treatment I and II received a negative response for RQ. Atrazine toxicity let to a rise in the levels of SOD, CAT and POD in the vital tissues of zebrafish. The continuous acquisition of fish signals is achieved which is one of the main merits of our OBBMS and OBRRMS. Additionally, no special data processing was done, the real-time data sets were directly used on statistical tools and the differences between the factors (groups, photoperiods, exposure periods and their interactions) were identified precisely. Hence, our OBBMS and OBRRMS could be a promising tool for biological response-based real-time water quality monitoring studies.

Wireless Sensor Networks for Green Cities: A Comprehensive Review of Environmental Pollution Monitoring

This research paper investigates the design and implementation of a Wireless Sensor Network (WSN)-Based Data Acquisition System tailored for collecting environmental pollution factors with a specific focus on contributing to the realization of a Green City. Leveraging various literature surveys on WSN and its applications with different techniques, a comprehensive analysis of the existing body of knowledge in the field. The paper explores the deployment of WSN technology as a cost-effective and scalable solution for real-time monitoring of pollution-related parameters, including air and water quality, noise levels, and other relevant environmental factors.By synthesizing insights from diverse literature sources, propose an intelligent and adaptable WSN framework capable of capturing, analyzing, and transmitting real-time environmental data. The research highlights the importance of integrating advanced sensor technologies within the WSN infrastructure to ensure a holistic monitoring of pollution factors. This analysis also delves into various data acquisition methodologies and explores the potential for advanced data analytics and visualization techniques to derive meaningful insights

Real Time Water Quality Monitoring of River Pamba (India) using Internet of Things

— All of Earth’s living things depend on food and clean water to survive. River water monitoring is essential for gathering the data required to manage water quality in the present as well as the future. In this research, the water quality of the Pamba River is monitored and analyzed using the Internet of Things (IoT) during the monsoon and summer seasons. The Pamba River is the third-longest river in the Indian state of Kerala and one of the primary sources of drinking water in Kerala’s Pathanamthitta and Alappuzha districts. In conventional systems, the monitoring procedure entails manually collecting water samples from various places, followed by testing and analysis in a lab. This is costly and time-consuming. With developments in sensors, communication, and the Internet of Things (IoT), monitoring water contamination has become more and more crucial in recent years and provides accurate data. The system collects information from IoT sensors to evaluate the quality of the water, including pH, dissolved oxygen, temperature changes, and other parameters. The relevant officials are alerted in the case of any variation, and all of this data is stored in the cloud. The system also ensures real-time water quality monitoring.

Water quality assessment monitoring system using fuzzy logic and the internet of things

Water utilization has recently been at its highest level of demand. The water needed to be clean, healthy, and determined to be suitable for consumption. Therefore, it is necessary to have a system that can monitor the water quality so thatinformation related to wate r suitability can be received regularly and in real-time. This paper addresses the critical need for real-time water quality monitoring systems. This study proposed a novel approach integrating the Tsukamoto fuzzy algorithm into an internet of things (IoT)-based framework, forming part of the Fuzzy Inference System. Our system serves as a decision support tool, enabling continuous assessment of water quality. The method categorizes water quality into three levels: good, moderate, and unhealthy, providing timely and precise suitability information. The results demonstrate the effectiveness of the fuzzy logic method in delivering accurate output. Through remotely deployed IoT devices, water suitability and status can be monitored and analyzed in real-time over the internet. This research bridges the gap between traditional water quality assessment methods and the demands of our modern, technology-driven society, ensuring a reliable supply of safe and consumable water.

Monitoring the quality of ground water in pipelines using deep neural network model

Monitoring the water quality in pipelines is crucial to guaranteeing that consumers receive safe and hygienic water. Conventional monitoring techniques rely on labor-intensive, time-consuming laboratory testing and physical sampling. This study aims to introduce an integrated LSTM-BP neural network model for real-time water quality monitoring in pipelines, showcasing improved predictions for parameters like dissolved oxygen and pH. This paper investigated the results of real-time Water Quality (WQ) monitoring in pipelines using several Deep Neural Network (DNN) models and pre-processing techniques for the information being provided. This paper proposes an integrated long-short-term memory (LSTM) and backpropagation (BP) neural network to offer an I-LSTM-BP model for Monitoring Water Quality in Pipelines (MWQP). The performance of predictions has been enhanced through the integration of LSTM and BP neural networks, particularly for time-series forecasting applications like MWQP. The model may use the LSTM network’s memory and adaptability while still keeping the BP network’s capacity to simulate non-linear interactions and enhance performance during generalization. Dissolved oxygen (DO; mg/L) and hydrogen (pH) potential are the parameters considered during the MWQP process. Information collected from China’s autonomous water quality monitoring units has been used to examine the water quality characteristics thoroughly. The forecasting model has been trained and evaluated using data collected from monitoring stations from June 2018 to December 2022. Statistical measures such as the correlation coefficient, root mean square error (RMSE), mean absolute error (MAE), and their normalized equivalents have been used to evaluate the DNN model’s performance throughout the training and testing stages. The results of this work demonstrate the viability and efficiency of using the proposed I-LSTM-BP-based DNN for MWQP applications. These results show the I-LSTM-BP model’s significant performance benefit, which makes it the model of choice for precise MWQP parameter prediction and WQ evaluation.

New Graph-Based and Transformer Deep Learning Models for River Dissolved Oxygen Forecasting

Dissolved oxygen (DO) is a key indicator of water quality and the health of an aquatic ecosystem. Aspiring to reach a more accurate forecasting approach for DO levels of natural streams, the present work proposes new graph-based and transformer-based deep learning models. The models were trained and validated using a network of real-time hydrometric and water quality monitoring stations for the Credit River Watershed, Ontario, Canada, and the results were compared with both benchmarking and state-of-the-art approaches. The proposed new Graph Neural Network Sample and Aggregate (GNN-SAGE) model was the best-performing approach, reaching coefficient of determination (R2) and root mean squared error (RMSE) values of 97% and 0.34 mg/L, respectively, when compared with benchmarking models. The findings from the Shapley additive explanations (SHAP) indicated that the GNN-SAGE benefited from spatiotemporal information from the surrounding stations, improving the model’s results. Furthermore, temperature has been found to be a major input attribute for determining future DO levels. The results established that the proposed GNN-SAGE model outperforms the accuracy of existing models for DO forecasting, with great potential for real-time water quality management in urban watersheds.

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.

Design and development of a continuous water quality monitoring buoy for health monitoring of river Ganga

Real-time monitoring of water quality in the river Ganga and other Indian rivers is crucial to determining its suitability for drinking and other usages across the seasons and round the clock. For this, a structurally strong and hydrostatically stable floating observation center is required to house all the sensors and related equipment. This paper explains the design process for such a buoy platform that can house an array of water quality sensors powered by hybrid energy harvesting systems. Sensors are connected to a wireless sensor network (WSN) system that transfers data to a web-based platform, where we can monitor and analyze our data for the purpose of hazard prediction. Computational analysis has been carried out for the observatory body to ascertain its structural integrity and hydrostatic stability at small and large angles of inclination. The buoy design is based on various requirements specific to Indian rivers at different locations from the mid-course to the confluence. It is important that the system be modular and portable for use in a constantly changing river/water environment. A full-scale functional prototype has been developed, and field testing has been carried out to bring out the efficacy of the proposed system. Also, the WSN system collected real-time water quality data that have been validated with laboratory-based experiments. The establishment of a network of low-cost river/water health monitoring system will further initiate the large-scale data collection and help create digital twins of the Indian rivers.

Real-Time Anomaly Detection for Water Quality Sensor Monitoring Based on Multivariate Deep Learning Technique.

With the increased use of automated systems, the Internet of Things (IoT), and sensors for real-time water quality monitoring, there is a greater requirement for the timely detection of unexpected values. Technical faults can introduce anomalies, and a large incoming data rate might make the manual detection of erroneous data difficult. This research introduces and applies a pioneering technology, Multivariate Multiple Convolutional Networks with Long Short-Term Memory (MCN-LSTM), to real-time water quality monitoring. MCN-LSTM is a cutting-edge deep learning technology designed to address the difficulty of detecting anomalies in complicated time series data, particularly in monitoring water quality in a real-world setting. The growing reliance on automated systems, the Internet of Things (IoT), and sensor networks for continuous water quality monitoring is driving the development and deployment of the MCN-LSTM approach. As these technologies become more widely used, the rapid and precise identification of unexpected or aberrant data points becomes critical. Technical difficulties, inherent noise, and a high data influx pose significant hurdles to manual anomaly detection processes. The MCN-LSTM technique takes advantage of deep learning by integrating Multiple Convolutional Networks and Long Short-Term Memory networks. This combination of approaches offers efficient and effective anomaly detection in multivariate time series data, allowing for identifying and flagging unexpected patterns or values that may signal water quality issues. Water quality data anomalies can have far-reaching repercussions, influencing future analyses and leading to incorrect judgments. Anomaly identification must be precise to avoid inaccurate findings and ensure the integrity of water quality tests. Extensive tests were carried out to validate the MCN-LSTM technique utilizing real-world information obtained from sensors installed in water quality monitoring scenarios. The results of these studies proved MCN-LSTM's outstanding efficacy, with an impressive accuracy rate of 92.3%. This high level of precision demonstrates the technique's capacity to discriminate between normal and abnormal data instances in real time. The MCN-LSTM technique is a big step forward in water quality monitoring. It can improve decision-making processes and reduce adverse outcomes caused by undetected abnormalities. This unique technique has significant promise for defending human health and maintaining the environment in an era of increased reliance on automated monitoring systems and IoT technology by contributing to the safety and sustainability of water supplies.

Detection and mapping of water and chlorophyll-a spread using Sentinel-2 satellite imagery for water quality assessment of inland water bodies.

Water quality monitoring of reservoirs is currently a significant challenge in the tropical regions of the world due to limited monitoring stations and hydrological data. Remote sensing techniques have proven to be a powerful tool for continuous real-time monitoring and assessment of tropical reservoirs water quality. Although many studies have detected chlorophyll-a (Chl-a) concentrations as a proxy to represent nutrient contamination, using Sentinel 2 for eutrophic or hypereutrophic inland water bodies, mainly reservoirs, minimal efforts have been made for oligotrophic and mesotrophic reservoirs. The present study aimed to develop a modeling framework to map and estimate spatio-temporal variability of Chl-a levels and associated water spread using the Modified Normalized Difference Water Index (MNDWI) and Maximum Chlorophyll Index (MCI). Moreover, the impact of land use/land cover type of the contributing watershed in the oligo-mesotrophic reservoir, Bhadra (tropical reservoir), for 2018 and 2019 using Sentinel 2 satellite data was analyzed. The results show that the water spread area was higher in the post-monsoon months and lower in the summer months. This was further validated by the correlation with reservoir storage, which showed a strong relationship (R2 = 0.97, 2018; R2 = 0.93, 2019). The estimated Chl-a spread was higher in the winter season, because the reservoir catchment was dominated by deciduous forest, producing a large amount of leaf litter in tropical regions, which leads to an increase in the level of Chl-a. It was found that Chl-a spread in the reservoir, specifically at the inlet sources and near agricultural land practices (western parts of the Bhadra reservoir). Based on the findings of this study, the MCI spectral index derived from Sentinel 2 data can be used to accurately map the spread of Chl-a in diverse water bodies, thereby offering a robust scientific basis for effective reservoir management.

스마트도시의 안전성 향상을 위한 스마트기술 적용방안에 관한 연구

In order to propose ways to apply smart technology to improve the safety of smart cities, this study aims to propose smart technologies that are highly related to safety management in the areas of transportation, medical care, environment, nature, and crime prevention in smart cities, such as AR, BIM, big data, and cloud. , IoT, VR, robot automation, drones, and 3D printing were analyzed. For the study, the applicability of smart technology in each field was investigated by safety management experts using a matrix evaluation method, and the results can be summarized as follows. In the transportation field, big data technology for collecting and processing traffic information through GPS, GIS and smart phone apps, drone technology that monitors real-time traffic information and utilizes sensors such as vehicles, roads and signal devices, and various collected information The safety of smart cities can be secured by applying IoT technology that builds prediction models based on traffic patterns. In the medical field, robot automation technology that sets up predictive models for telemedicine, health management, drug processing and infectious diseases, drone technology that utilizes the movement of patients and medical staff, attachment of monitoring devices, and securing public health response data, etc. It is predicted that it will be possible to secure the safety of smart cities through 3D printing technology that can manufacture medical instruments, auxiliary devices, and various artificial bodies, as well as various medical models and educational tools. In the environmental field, drone technology enables real-time monitoring of water quality, air, and various environmental factors, robotic automation technology that uses robots in places to collect environmental data within the city, and environmental infrastructure that installs and collects buildings, vehicles, and various infrastructure facilities. IoT technology can be applied to improve the safety of smart cities. In the natural field, drone technology is capable of collecting and utilizing various data by attaching video, thermal images, LiDAR scans, radars and sensors, and integrated management linked to a central system focused on the installation of houses, buildings and various infrastructure facilities and environmental changes. The safety of smart cities can be improved by utilizing possible IoT technology, BIM technology that can establish response plans for storm and flood damage, cold waves, heatstroke, and earthquakes through 3D models, and big data technology that collects, integrates, and builds platforms for safety-related data. It can be promoted. In the crime prevention field, drone technology that can detect the occurrence of abnormal signals such as sound, heat, and optical sensors and link with the local police station, IoT technology that can collect and analyze voice recognition technology, and AI that strengthens facial and vehicle recognition in intelligent CCTV. The safety of smart cities can be secured through robot automation technology that combines the deployment of basic robots and collaboration with drones and IoT technologies.

Detection of Water Contaminants by Organic Transistors as Gas Sensors in a Bottom-Gate/Bottom-Contact Cross-Linked Structure.

Detecting volatile organic compounds is a fundamental step in water quality analysis. Methylisoborneol (MIB) provides a lousy odor to water, whereas geosmin (GEO) is responsible for its sour taste. A widely-used technique for their detection is gas-phase chromatography. On the other hand, an electronic nose from organic thin-film transistors is a cheaper and faster alternative. Poly(2,5-bis(3-tetradecyl-thiophen-2-yl)thieno[3,2-b]thiophene) (PBTTT-C14) features semiconducting properties suitable for organic electronics. However, in order to expose the active layer in a bottom-gate transistor structure with photolithographically patterned electrodes, a cross-linked dielectric such as poly(4-vinyl phenol) (PVP) is necessary. In this work, the cross-linking was demonstrated using FTIR and Raman spectroscopies, as well as high-k capacitors with a dielectric constant of 5.3. The presence of enhanced crystallinity with terrace formation in the semiconducting film was confirmed with UV-visible spectrophotometry, atomic force microscopy, and X-ray diffraction. Finally, for the first time, a PBTTT-C14 transistor on cross-linked PVP was shown to respond to isoborneol with a sensitivity of up to 6% change in mobility per ppm. Due to its similarity to MIB, a system comprising these sensors must be investigated in the future as a tool for sanitation companies in real-time water quality monitoring.

A Weak Electricigen-Based Bioelectrochemical Sensor for Real-Time Monitoring of Chemical Pollutants in Water.

Electroactive microorganisms are now understood to be abundant across nature, though many are categorized as "weak electricigens" not suitable for reasonable power generation. We report the use of weak electricigens from the natural environment for rapid, real-time water quality monitoring. Using a variety of pesticides as model chemical pollutants, the bioelectrochemical sensor was responsive within minutes at all concentrations tested (0.05-2 ppm) and could be repreatedly used long-term. Due to the prevalence of electroactive microorganisms in the natural environment, such sensors could work in tandem with conventional monitoring methods and may be useful for detecting emerging contaminants.

Water Quality Monitoring Using Physical Web Concept and Alert via Whatsapp Messenger

Abstract: Monitoring water quality plays a crucial role in safeguarding human health, Industrial equipment and aquatic life such as in aquarium. In this research paper we have present a low-cost and unique demonstration of real-time water quality monitoring using physical web concept and alert to user by WhatsApp if water quality is Low. Physical web concept is a broadcast and discovery service in that, smart object broadcasts relevant information that nearby mobile devices can discover to interact with that object. Using physical web user can digitally interact with object and service without much effort that user feels in conventional method of interaction with object. The TDS and Ph is the important factor for the water quality monitoring among the many other factors. This system utilizes the TDS and Ph sensors to measure and monitor the quality of water. The collected quality monitoring data is processed by the ESP32 and make the decision to sends low-quality alert via WhatsApp to designated users. The system aims to provide a reliable and user-friendly solution for monitoring water quality, enabling timely interventions and minimizing risks associated with it using the physical web concept and Internet of Things (IoT)

Intelligent monitoring of water quality based on image analytics

Owing to the limitations of the spatial arrangement of monitoring stations and time acquisition of satellite remote sensing images, the water quality monitoring of rivers, especially small- and medium-sized rivers, cannot be satisfied in terms of time and space continuity. In this study, we propose a standardization method for the camera images derived at different locations on different time considering the influences of light, shadows, reflections, ripples, leaves and so on. After this algorithm is established, an estimation algorithm based on the comprehensive appearance of water body is proposed, which has the potential of realising real-time, mobile, and continuous monitoring of water quality with low costs. The test results showed that the accuracy of the model was quite high compared to the results of the hydrological monitoring stations. Compared with the single-point detection of water quality monitoring stations, this method has advantages in terms of dynamic detection and small- andmedium-sized water body detection, which can serve as a supplement to traditional detection.