Smart Monitoring Method for Land-Based Sources of Marine Outfalls Based on an Improved YOLOv8 Model DOI Open Access
Shiyu Zhao,

Haolan Zhou,

Haiyan Yang

и другие.

Water, Год журнала: 2024, Номер 16(22), С. 3285 - 3285

Опубликована: Ноя. 15, 2024

Land-based sources of marine outfalls are a major source pollution. The monitoring land-based is an important means for environmental protection and governance. Traditional on-site manual methods inefficient, expensive, constrained by geographic conditions. Satellite remote sensing spectral analysis can only identify pollutant plumes affected discharge timing cloud/fog interference. Therefore, we propose smart method based on improved YOLOv8 model, using unmanned aerial vehicles (UAVs). This accurately classify outfalls, offering high practical application value. Inspired the sparse sampling in compressed sensing, incorporated multi-scale dilated attention mechanism into model integrated dynamic snake convolutions C2f module. approach enhanced model’s detection capability occluded complex-feature targets while constraining increase computational load. Additionally, proposed new loss calculation combining Inner-IoU (Intersection over Union) MPDIoU (IoU with Minimum Points Distance), which further regression speed its ability to predict targets. final experimental results show that achieved mAP50 (mean Average Precision at 50) 87.0%, representing 3.4% from original effectively enabling outlets.

Язык: Английский

Inversion of Water Quality Parameters from UAV Hyperspectral Data Based on Intelligent Algorithm Optimized Backpropagation Neural Networks of a Small Rural River DOI Creative Commons
Manqi Wang, Chunyi Zhou, Jiaqi Shi

и другие.

Remote Sensing, Год журнала: 2025, Номер 17(1), С. 119 - 119

Опубликована: Янв. 2, 2025

The continuous and effective monitoring of the water quality small rural rivers is crucial for sustainable development. In this work, machine learning models were established to predict a typical river based on quantity measured data UAV hyperspectral images. Firstly, spectral preprocessed using fractional order derivation (FOD), standard normal variate (SNV), normalization (Norm) enhance response characteristics parameters. Second, method combining Pearson’s correlation coefficient variance inflation factor (PCC–VIF) was utilized decrease dimensionality features improve input data. Again, screened features, back-propagation neural network (BPNN) model optimized mixture genetic algorithm (GA) particle swarm optimization (PSO) as means estimating parameter concentrations. To intuitively evaluate performance hybrid algorithm, its prediction accuracy compared with that conventional algorithms (Random Forest, CatBoost, XGBoost, BPNN, GA–BPNN PSO–BPNN). results show GA–PSO–BPNN turbidity (TUB), ammonia nitrogen (NH3-N), total (TN), phosphorus (TP) exhibited optimal coefficients determination (R2) 0.770, 0.804, 0.754, 0.808, respectively. Meanwhile, also demonstrated good robustness generalization ability from different periods. addition, we used visualize parameters in study area. This work provides new approach refined rivers.

Язык: Английский

Процитировано

0
Optical properties of CDOM and assessing eutrophication by remote sensing of CDOM in the Zhanjiang Bay, China DOI Creative Commons

Yafeng Zhong,

Guo Yu, Dongyang Fu

и другие.

Frontiers in Marine Science, Год журнала: 2025, Номер 12

Опубликована: Май 23, 2025

Based on the field survey data collected in winter (January, 2018) and spring (April, 2017), characteristic variability of chromophoric dissolved organic matter (CDOM) different seasons Zhanjiang Bay was analyzed. The results demonstrated that CDOM absorption coefficient at 280 nm ( a g (280)) spectral slope from 275 to 295 (S 275-295 ) representing molecular weight could both maintain good correlations with salinity spring, indicating more likely exist as conserved substance during its migration Bay. characteristics weak correlation between Chlorophyll (Chl a) revealed influence algal activity limited. In addition, this study also suggested idea using track eutrophication bay. acceptable (280) reflectance band ratio (R rs (704)/R (492)) recorded in-situ , index (EI), series empirical models were developed categorize retrieve through then used Sentinel-2. assessment examined by remote sensing. This provided fresh approach measuring help regional environmental quality management organizations make informed decisions.

Язык: Английский

Процитировано

0
Smart Monitoring Method for Land-Based Sources of Marine Outfalls Based on an Improved YOLOv8 Model DOI Open Access
Shiyu Zhao,

Haolan Zhou,

Haiyan Yang

и другие.

Water, Год журнала: 2024, Номер 16(22), С. 3285 - 3285

Опубликована: Ноя. 15, 2024

Land-based sources of marine outfalls are a major source pollution. The monitoring land-based is an important means for environmental protection and governance. Traditional on-site manual methods inefficient, expensive, constrained by geographic conditions. Satellite remote sensing spectral analysis can only identify pollutant plumes affected discharge timing cloud/fog interference. Therefore, we propose smart method based on improved YOLOv8 model, using unmanned aerial vehicles (UAVs). This accurately classify outfalls, offering high practical application value. Inspired the sparse sampling in compressed sensing, incorporated multi-scale dilated attention mechanism into model integrated dynamic snake convolutions C2f module. approach enhanced model’s detection capability occluded complex-feature targets while constraining increase computational load. Additionally, proposed new loss calculation combining Inner-IoU (Intersection over Union) MPDIoU (IoU with Minimum Points Distance), which further regression speed its ability to predict targets. final experimental results show that achieved mAP50 (mean Average Precision at 50) 87.0%, representing 3.4% from original effectively enabling outlets.

Язык: Английский

Процитировано

0