Search Results (95)

Benthic diatoms are key primary producers in aquatic ecosystems and sensitive bioindicators for water quality monitoring; for example, the Yellow River Basin exhibits high diatom species diversity. However, traditional microscopic identification of such species remains inefficient and inaccurate. To enable automated identification, we established a benthic diatom dataset containing 3157 SEM images of 32 genera/species from the Yellow River Basin and developed a novel identification method. Specifically, the knowledge extraction module distinguishes foreground features from background noise by guiding spatial attention to focus on mutually exclusive regions within the image. This mechanism allows the network to focus more on foreground features that are useful for the classification task while significantly reducing the interference of background noise. Furthermore, a dual knowledge guidance module is designed to enhance the discriminative representation of fine-grained diatom images. This module strengthens multi-region foreground features through grouped channel attention, supplemented with contextual information through convolution-refined background features assigned low weights. Finally, the proposed method integrates multi-granularity learning, knowledge distillation, and multi-scale training strategies, further improving the classification accuracy. The experimental results demonstrate that the proposed network outperforms comparative methods on both the self-built diatom dataset and a public diatom dataset. Ablation studies and visualization further validate the efficacy of each module. Full article

The Urban Drainage Network (UDN) is a type of underground municipal infrastructure responsible for transporting sewage and rainwater. To keep abreast of the hydraulic and water quality conditions of the pipes and to detect problems such as pipe clogging, pollution and leakage, real-time monitoring sensors have been widely adopted, accomplished with the development of IoT technologies. However, the intricate topology and numerous nodes of drainage pipes complicate IoT sensor placement strategies, especially in the selection of sensors and the location of monitoring points. This review examines application cases of IoT sensors in UDNs and some other hydraulic networks, evaluating the characteristics and applicability of various optimal placement methods and theories. A general framework was proposed applicable to the optimal placement of IoT sensors in the UDN, including object classification–method selection–quantitative evaluation. Currently, the quantitative evaluation of monitoring schemes lacks a systematic process, and existing layout methods may not be optimal. Future research can explore dynamic optimization strategies through phased deployment and feedback iteration, which can enhance the accuracy and objectivity of sensor layout design and evaluation. Full article

In the context of drinking water safety assurance, water quality prediction faces challenges due to temporal fluctuations, seasonal cycles, and the impacts of sudden events. To address the issue of cumulative prediction bias caused by the simplistic feature fusion of traditional methods, this study proposes a neural network architecture that integrates spatiotemporal features with a hierarchical cross-attention mechanism. Innovatively, the model constructs a parallel feature extraction framework, integrating BiGRUs (Bidirectional Gated Recurrent Units) and BiTCNs (Bidirectional Temporal Convolutional Networks). By incorporating a bidirectional spatiotemporal interaction mechanism, the model effectively captures long-term dependencies in time series and local associations in spatial topology. During the feature fusion phase, layer-by-layer weighting through learnable parameters enables adaptive spatiotemporal feature processing. A hierarchical cross-attention module is designed to achieve deep feature integration, enhancing the discriminative expression of spatial features while preserving the dynamics of time series. The experimental results demonstrate that when predicting water quality monitoring data from the Xidong Water Plant, this model excels in forecasting key indicators such as total phosphorus and total nitrogen. Compared to traditional hybrid models, it reduces the MSE (Mean Squared Error) by 33.35%, the MAE (Mean Absolute Error) by 38.05%, and the RMSE (Root Mean Square Error, RMSE) by 19.35%, and increases the R² (coefficient of determination, R²) by 2.15 percentage points. These achievements break the limitations of traditional methods’ rigid and simplistic feature fusion, fully demonstrating the model’s superiority in prediction accuracy and generalization capabilities. Full article

Leaky dams have become essential nature-based solutions for flood management, providing sustainable alternatives to traditional engineered flood control methods. This review delves into the mechanisms by which leaky dams operate, including the regulation of water flow through velocity reduction and distribution across floodplains, effective sediment trapping and soil quality enhancement, and the facilitation of groundwater recharge and water table stabilization. These structures not only mitigate peak flood flows and reduce erosion but also contribute to enhanced biodiversity by creating diverse aquatic habitats and maintaining ecological connectivity. The effectiveness of leaky dams is assessed through various performance metrics, demonstrating significant reductions in peak flows, improved sediment management, and increased groundwater levels, which collectively enhance ecosystem resilience and water quality. However, the implementation of leaky dams presents several technical challenges, such as design complexity, hydrological variability, maintenance requirements, and socio-economic factors like land use conflicts and economic viability. Additionally, while leaky dams offer numerous environmental benefits, potential negative impacts include habitat disruption, sediment accumulation, and alterations in water quality, which necessitate careful planning and adaptive management strategies. Emerging trends in leaky dam development focus on the integration of smart technologies, such as real-time monitoring systems and artificial intelligence, to optimize performance and resilience against climate-induced extreme weather events. Advances in modeling and monitoring technologies are facilitating the effective design and implementation of leaky dam networks, promoting their incorporation into comprehensive watershed management frameworks. This review highlights the significant potential of leaky dams as integral components of sustainable flood management systems, advocating for their broader adoption alongside conventional engineering solutions to achieve resilient and ecologically balanced water management. Full article

Chemical oxygen demand (COD) is a critical parameter employed to assess the level of organic pollution in water. Accurate COD detection is essential for effective environmental monitoring and water quality assessment. Ultraviolet–visible (UV-Vis) spectroscopy has become a widely applied method for COD detection due to its convenience and the absence of the need for chemical reagents. This non-destructive and reagent-free approach offers a rapid and reliable means of analyzing water. Recently, deep learning has emerged as a powerful tool for automating the process of spectral feature extraction and improving COD prediction accuracy. In this paper, we propose a novel multi-scale one-dimensional convolutional neural network (MS-1D-CNN) fusion model designed specifically for spectral feature extraction and COD prediction. The architecture of the proposed model involves inputting raw UV-Vis spectra into three parallel sub-1D-CNNs, which independently process the data. The outputs from the final convolution and pooling layers of each sub-CNN are then fused into a single layer, capturing a rich set of spectral features. This fused output is subsequently passed through a Flatten layer followed by fully connected layers to predict the COD value. Experimental results demonstrate the effectiveness of the proposed method, as it was compared with three traditional methods and three deep learning methods on the same dataset. The MS-1D-CNN model showed a significant improvement in the accuracy of COD prediction, highlighting its potential for more reliable and efficient water quality monitoring. Full article

Urban expansion leads to changes in land use, and the resulting habitat fragmentation increases the risk of species extinction. Therefore, strategies to connect fragmented habitats for wildlife conservation are required, but past research has focused mainly on large mammals and specific species, and there has been a lack of research on habitat connectivity in Korea. In the present study, we sought to design an ecological network for the conservation of endangered forest wildlife (leopard cat, yellow-throated marten, and Siberian flying squirrel) in Pyeongchang, Gangwon State, Korea. The InVEST habitat quality and MaxEnt models were used to predict forest areas with excellent habitat quality and a high probability of the occurrence of endangered wildlife. We then used Linkage Mapper to identify corridors and bottlenecks that connect fragmented habitats within the study area. The quality of these corridors and the environmental features of the pinch points were also analyzed. The results showed that the area outside of Pyeongchang is the most likely area for endangered forest wildlife habitats and occurrence. A total of seven core areas were identified, and 12 corridors connecting the core areas were identified. The highest quality corridors were those connecting forest areas outside of Pyeongchang because they had a high habitat quality with alternative paths of least resistance. We also identified sections with high pinch points in all corridors, and these points tended to have high elevation, a southern aspect, a long distance from agricultural land and water bodies, low traffic density, and low building density. ANOVA revealed that the environmental variables associated with high pinch points, least-cost paths, and Pyeongchang in general exhibited statistically significant differences. These results demonstrate that the proposed conservation planning model can be applied to multiple species using a corridor-integrated mapping approach and produces quantitative figures for the targeted improvement of ecological connectivity in forests according to local characteristics, including biodiversity. As such, this approach can be utilized as the basis for the selection and management of protected forest areas and for environmental impact assessment. However, because this study had data limitations, field surveys and the monitoring of target species are needed. Once these limitations are addressed, a quantitative conservation plan can be established based on the ecological characteristics of endangered forest wildlife. Full article

In the context of climate change, there is a growing need for accurate, real-time data on water quality in river waterways. This results in the development of advanced monitoring systems. This article presents a remote water quality monitoring system designed specifically for use in inland waterways, the basic elements of which are placed in a buoy with an IoT unit. The proposed system uses a network of sensors strategically placed along the waterway to continuously measure critical parameters: temperature, pH, dissolved oxygen, and conductivity. Various compatibility, efficiency, and ease-of-use tests have been conducted to verify each aspect of the monitoring system. It has been shown that the sensors operate within the intended accuracy ranges. The central unit equipped with a GSM (Global System for Mobile Communications) module can wirelessly transmit data to a main server, enabling remote access and analysis via a user-friendly interface of the developed application. The paper details the technical architecture of the system, the integration of GSM technology to ensure reliable data transmission, and the results of the monitoring studies of the proposed parameters. The remote monitoring system offers significant benefits in terms of early detection of pollution events, ensuring the safety of aquatic life, and supporting sustainable navigation practices. The research results highlight the potential of GSM-based remote monitoring systems to revolutionize water quality management in waterways in various regions. Full article

Waterborne illnesses lead to millions of fatalities worldwide each year, particularly in developing nations. In this paper, we introduce a comprehensive system designed for the autonomous early detection of viral outbreaks transmitted through water to ensure sustainable access to healthy water resources, especially in remote areas. The system utilizes an autonomous water quality monitoring setup consisting of an airborne water sample collector, an autonomous sample processor, and an artificial intelligence-aided microscopic detector for risk assessment. The proposed system replaces the time-consuming conventional monitoring protocol by automating sample collection, sample processing, and pathogen detection. Furthermore, it provides a safer processing method against the spillage of contaminated liquids and potential resultant aerosols during the heat fixation of specimens. A morphological image processing technique of light microscopic images is used to segment images, assisting in selecting a unified appropriate input segment size based on individual blob areas of different bacterial cultures. The dataset included harmful pathogenic bacteria (A. baumanii, E. coli, and P. aeruginosa) and harmless ones found in drinking water and wastewater (E. faecium, L. paracasei, and Micrococcus spp.). The segmented labeled dataset was used to train deep convolutional neural networks to automatically detect pathogens in microscopic images. To minimize prediction error, Bayesian optimization was applied to tune the hyperparameters of the networks’ architecture and training settings. Different convolutional networks were tested in accordance with different required output labels. The neural network used to classify bacterial cultures as harmful or harmless achieved an accuracy of 99.7%. The neural network used to identify the specific types of bacteria achieved a cumulative accuracy of 93.65%. Full article

Water is a critical resource for human survival worldwide, and its availability and quality in natural reservoirs such as lakes and rivers must be monitored. In that way, wireless dynamic sensor networks can help monitor water quality. These networks have significantly advanced across various sectors, including industrial automation and environmental monitoring. Moreover, the Internet of Things has emerged as a global technological marvel, garnering interest for its ability to facilitate information visualization and ease of deployment—the combination of wireless dynamic sensor networks and the Internet of Things improves water monitoring and helps to care for this vital resource. This article presents the design and deployment of a wireless dynamic sensor network comprising a mobile node outfitted with multiple sensors for remote aquatic navigation and a stationary node similarly equipped and linked to a server via the IoT. Both nodes can measure parameters like pH, temperature, and total dissolved solids (TDS), enabling real-time data monitoring through a user interface and generating a database for future reference. The integrated control system within the developed interface enhances the mobile node’s ability to survey various points of interest. The developed project enabled real-time monitoring of the aforementioned parameters, with the recorded data being stored in a database for subsequent graphing and analysis using the IoT. The system facilitated data collection at various points of interest, allowing for a graphical representation of parameter evolution. This included consistent temperature trends, neutral and alkaline zone data for pH levels, and variations in total dissolved solids (TDS) recorded by the mobile node, reaching up to 100 ppm. Full article

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. Full article

Water is an essential resource for both rural and agricultural areas; it can be wisely distributed and used in the field to protect daily life, production, the natural environment, and the safety and stability of regional drainage and flood control systems. Our research selected a typical plains rural river network area with agriculture as the main industry to investigate the most effective method of farmland diversion and drainage. We comprehensively planned and transformed the water system flow, water conservation engineering, and the ecological environment in the irrigation area through the reutilization system. The reutilization system’s operation and scheduling design is implemented for four specific periods: the water replenishment cycle, agricultural irrigation, agricultural drainage and the rainy period of the flood season. The research period ranges from 2020 to 2023 after the completion of the system. We used monitoring, the recording of hydraulic equipment parameters and data collection to evaluate the balance of water supply and demand in the study area. At the same time, we have tracked and evaluated the four aspects of water quality enhancement, water conservation and flood control, and agricultural irrigation. The results show that the total agricultural water consumption decreased by 2.9%, and the amount of water saved increased by 9.6%. The current segment creates the rivers’ embankment standards. With a 92% irrigation guarantee rate, the current section forms and the embankment standards of the rivers satisfy the design storage volume and the flood level of one in twenty years. The water quality of all the rivers in the area has decreased by 5~10% compared to the average concentration prior to establishment. This study verifies the comprehensive effect and the suitability of the system by comparing the before and after effects, and provides a scientific basis for the method of efficient recycling and utilization of water resources in the rural plains river network area; we also propose the guidance of increasing the digital twin control and long-term operation mechanism to ensure the long-term stable operation of the technology. Full article

Understanding fish distribution, behavior, and abundance is crucial for marine ecological research, fishery management, and environmental monitoring. However, the distinctive features of the underwater environment, including low visibility, light attenuation, water turbidity, and strong currents, significantly impact the quality of data gathered by underwater imaging systems, posing considerable challenges in accurately detecting fish objects. To address this challenge, our study proposes an innovative fish detection network based on prior knowledge of image degradation. In our research process, we first delved into the intrinsic relationship between visual image quality restoration and detection outcomes, elucidating the obstacles the underwater environment poses to object detection. Subsequently, we constructed a dataset optimized for object detection using image quality evaluation metrics. Building upon this foundation, we designed a fish object detection network that integrates a prompt-based degradation feature learning module and a two-stage training scheme, effectively incorporating prior knowledge of image degradation. To validate the efficacy of our approach, we develop a multi-scene Underwater Fish image Dataset (UFD2022). The experimental results demonstrate significant improvements of 2.4% and 2.5%, respectively, in the mAP index compared to the baseline methods ResNet50 and ResNetXT101. This outcome robustly confirms the effectiveness and superiority of our process in addressing the challenge of fish object detection in underwater environments. Full article

Traditional laboratory-based water quality monitoring and testing approaches are soon to be outdated, mainly because of the need for real-time feedback and immediate responses to emergencies. The more recent wireless sensor network (WSN)-based techniques are evolving to alleviate the problems of monitoring, coverage, and energy management, among others. The inclusion of the Internet of Things (IoT) in WSN techniques can further lead to their improvement in delivering, in real time, effective and efficient water-monitoring systems, reaping from the benefits of IoT wireless systems. However, they still suffer from the inability to deliver accurate real-time data, a lack of reconfigurability, the need to be deployed in ad hoc harsh environments, and their limited acceptability within industry. Electronic sensors are required for them to be effectively incorporated into the IoT WSN water-quality-monitoring system. Very few electronic sensors exist for parameter measurement. This necessitates the incorporation of artificial intelligence (AI) sensory techniques for smart water-quality-monitoring systems for indicators without actual electronic sensors by relating with available sensor data. This approach is in its infancy and is still not yet accepted nor standardized by the industry. This work presents a smart water-quality-monitoring framework featuring an intelligent IoT WSN monitoring system. The system uses AI sensors for indicators without electronic sensors, as the design of electronic sensors is lagging behind monitoring systems. In particular, machine learning algorithms are used to predict E. coli concentrations in water. Six different machine learning models (ridge regression, random forest regressor, stochastic gradient boosting, support vector machine, k-nearest neighbors, and AdaBoost regressor) are used on a sourced dataset. From the results, the best-performing model on average during testing was the AdaBoost regressor (a $\overline{MAE}$ of 14.37 counts/100 mL), and the worst-performing model was stochastic gradient boosting (a $\overline{MAE}$ of 42.27 counts/100 mL). The development and application of such a system is not trivial. The best-performing water parameter set (Set A) contained pH, conductivity, chloride, turbidity, nitrates, and chlorophyll. Full article

The relationship between estimating traffic flow and evaluating pollutant emissions lies in understanding how vehicular traffic patterns affect air quality. Traffic flow estimation is a complex field that involves a variety of analytical techniques to understand, predict, and manage the flow of vehicles on road networks. Different types of analyses commonly employed in this area are statistical analysis (e.g., descriptive statistics, inferential statistics, time series analysis), mathematical modeling (macroscopic models, microscopic models, mesoscopic models), computational methods (e.g., simulation modeling, machine learning, and AI techniques), geospatial analysis (e.g., geographic information systems (GISs), spatial data analysis), network analysis (e.g., graph theory and network flow models). In sensor network setups, the strategic placement of sensors is crucial, primarily due to the challenges posed by limited energy supplies, restricted storage capabilities, and the demands on processing and communication, all of which significantly impact maintenance costs and hardware limitations. To mitigate the burden on processing and communication, it is essential to deploy a limited number of sensors strategically. In practical applications, achieving an optimal layout of physical sensors (i.e., placing sensors within the network in such a way as to meet a specific optimality criterion, such as identifying the minimum number of sensors required to ensure the ability to design reliable state observers capable of reconstructing the network’s state based on the available data) is essential for the accurate monitoring of large-scale systems, including traffic flow or the distribution networks of water and gas. In the context of traffic systems, addressing the challenge of full link flow observability, that is, the ability to accurately monitor and assess the flow of entities (i.e., vehicles) across all the links or pathways within a network, entails selecting the smallest number of traffic sensors from a larger set to install. The goal is to choose a subset of p sensors, which may include redundancies, from a pool of $n>>p$ potential sensors. This is conducted to maintain the structural observability of the entire traffic network. This concept pertains to deducing the complete internal state (traffic volume on each road link in the network) from external outputs and inputs (measurements from sensors). The traditional concept of system observability serves as a criterion for sensor placement. This article presents the development of a simulated annealing heuristic to address the selection problem. The selected sensors are then applied to construct a Luenberger observer, a mathematical construct used in control theory to accurately estimate the internal state of a dynamic system based on its inputs and outputs. Numerical simulations are carried out to demonstrate the effectiveness of this method, and a performance analysis using a digital twin of a transport network, designed using the Aimsun Next software, are also carried out to assess traffic flow and associated pollutant emissions. In particular, we examine a traffic network comprising 21 roads. We address the sensor selection problem by identifying an optimal set of six sensors, which facilitates the design of a Luenberger observer. This observer enables the reconstruction of traffic flow across the network with minimal estimation error. Furthermore, by integrating this observer with data from the Aimsun Next software, we assess the pollutant emissions related to traffic flow. The results indicate a high accuracy in estimating pollutant levels. Full article

This paper designed and developed an online digital imaging excitation sensor for wind power gearbox wear condition monitoring based on an adaptive deep learning method. A digital imaging excitation sensing image information collection architecture for magnetic particles in lubricating oil was established to characterize the wear condition of mechanical equipment, achieving the real-time online collection of wear particles in lubricating oil. On this basis, a mechanical equipment wear condition diagnosis method based on online wear particle images is proposed, obtaining data from an engineering test platform based on a wind power gearbox. Firstly, a foreground segmentation preprocessing method based on the U-Net network can effectively eliminate the interference of bubbles and dark fields in online wear particle images, providing high-quality segmentation results for subsequent image processing, A total of 1960 wear particle images were collected in the experiment, the average intersection union ratio of the validation set is 0.9299, and the accuracy of the validation set is 0.9799. Secondly, based on the foreground segmentation preprocessing of wear particle images, by using the watered algorithm to obtain the number of particles in each size segment, we obtained the number of magnetic particle grades in three different ranges: 4–38 µm, 39–70 µm, and >70 µm. Thirdly, we proposed a method named multidimensional transformer (MTF) network. Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) are used to obtain the error, and the maintenance strategy is formulated according to the predicted trend. The experimental results show that the predictive performance of our proposed model is better than that of LSTM and TCN. Finally, the online real-time monitoring system triggered three alarms, and at the same time, our offline sampling data analysis was conducted, the accuracy of online real-time monitoring alarms was verified, and the gearbox of the wind turbine was shut down for maintenance and repair. Full article