A novel framework to predict water turbidity using Bayesian modeling

Enhanced river suspended sediment concentration identification via multimodal video image, optical flow, and water temperature data fusion

Monitoring suspended sediment concentration (SSC) in rivers is pivotal for water quality management and sustainable river ecosystem development. However, achieving continuous and precise SSC monitoring is fraught with challenges, including low automation, lengthy measurement processes, and high cost. This study proposes an innovative approach for SSC identification in rivers using multimodal data fusion. We developed a robust model by harnessing colour features from video images, motion characteristics from the Lucas-Kanade (LK) optical flow method, and temperature data. By integrating ResNet with a mixed density network (MDN), our method fused the image and optical flow fields, and temperature data to enhance accuracy and reliability. Validated at a hydropower station in the Xinjiang Uygur Autonomous Region, China, the results demonstrated that while the image field alone offers a baseline level of SSC identification, it experiences local errors under specific conditions. The incorporation of optical flow and water temperature information enhanced model robustness, particularly when coupling the image and optical flow fields, yielding a Nash-Sutcliffe efficiency (NSE) of 0.91. Further enhancement was observed with the combined use of all three data types, attaining an NSE of 0.93. This integrated approach offers a more accurate SSC identification solution, enabling non-contact, low-cost measurements, facilitating remote online monitoring, and supporting water resource management and river water-sediment element monitoring.

Machine learning integrated high quantum yield blue light carbon dots for real-time and on-site detection of Cr(VI) in groundwater and drinking water

The safety of groundwater and drinking water is directly related to the well-being of human beings and ecosystems. On-site monitoring and timely response to heavy metals in these water sources are crucial for water supply security. Fluorescent probes combined with machine learning technology have been applied to on-site detection of heavy metals. However, they were primarily focused on industrial-level detection and lacked the sensitivity required for detecting Cr(VI) in groundwater and drinking water. In this study, we developed an machine learning-integrated approach using high-quantum-yield (QY) N-doped blue-light carbon dots (N-BCDs) for instant detection of Cr(VI) in groundwater and drinking water. N-BCDs were synthesized within 3 min using a household microwave oven with citric acid and 1,2-diaminobenzene, resulting in a QY of approximately 90 %. The fluorescence of N-BCDs was quenched via the internal filter effect (IFE), enabling the detection of Cr(VI) within 1 min, with a detection limit of 0.1574 μg L-1 for Cr(VI) concentrations ranging from 0 to 60 μg L-1. We employed machine learning methods to determine Cr(VI) concentrations from simple shots, based on the red-green-blue (RGB) feature and Kmeans feature extraction. These features were input into four models (Ridge, XGB, SVR, and Linear), achieving a fitness of 95.2 %. Furthermore, the accuracies for Cr(VI) concentration identification in actual groundwater and drinking water were as high as 95.71 % and 96.81 %, respectively. Our work successfully extended the detection range of Cr(VI) to the μg level, significantly improving the practical applicability of the method and providing a new approach for on-site detection of Cr(VI) in groundwater and drinking water.

Quantifying uncertainty of marine water quality forecasts for environmental management using a dynamic multi-factor analysis and multi-resolution ensemble approach

Unpredictable climate change and human activities pose enormous challenges to assessing the water quality components in the marine environment. Accurately quantifying the uncertainty of water quality forecasts can help decision-makers implement more scientific water pollution management strategies. This work introduces a new method of uncertainty quantification driven by point prediction for solving the engineering problem of water quality forecasting under the influence of complex environmental factors. The constructed multi-factor correlation analysis system can dynamically adjust the combined weight of environmental indicators according to the performance, thereby increasing the interpretability of data fusion. The designed singular spectrum analysis is utilized to reduce the volatility of the original water quality data. The real-time decomposition technique cleverly avoids the problem of data leakage. The multi-resolution-multi-objective optimization ensemble method is adopted to absorb the characteristics of different resolution data, so as to mine deeper potential information. Experimental studies are conducted using 6 actual water quality high-resolution signals with 21,600 sampling points from the Pacific islands and corresponding low-resolution signals with 900 sampling points, including temperature, salinity, turbidity, chlorophyll, dissolved oxygen, and oxygen saturation. The results illustrate that the model is superior to the existing model in quantifying the uncertainty of water quality prediction.