Marine water quality detection based on neural network

Liu, Jiangping; Xue, Heru; Pan, Xin Xiang; Gao, Jing; Jiang, Xinhua

doi:10.5004/dwt.2021.26873

Cited by 4 publications

(3 citation statements)

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“…Semantic segmentation is pixel-level classification, and lake water body extraction is the scope of semantic segmentation, and the commonly used performance evaluation metrics include pixel accuracy (PA), mean pixel accuracy (MPA), and mean intersection over union (MIoU), and these metrics are calculated based on the confusion matrix, which is used to count the number of samples categorized into right and wrong categories [32,33]. The higher the values of PA, MPA and MIoU, the better the model is for the extraction of the predicted targets, and the three metrics are calculated as follows.…”

Section: Model Performance Evaluation Metricsmentioning

confidence: 99%

A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images

Liu,

2024

Applied Sciences

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Extraction of lake water bodies from remote sensing images provides reliable data support for water resource management, environmental protection, natural disaster early warning, and scientific research, and helps to promote sustainable development, protect the ecological environment and human health. With reference to the classical encoding-decoding semantic segmentation network, we propose the network model R50A3-LWBENet for lake water body extraction from remote sensing images based on ResNet50 and three attention mechanisms. R50A3-LWBENet model uses ResNet50 for feature extraction, also known as encoding, and squeeze and excitation (SE) block is added to the residual module, which highlights the deeper features of the water body part of the feature map during the down-sampling process, and also takes into account the importance of the feature map channels, which can better capture the multiscale relationship between pixels. After the feature extraction is completed, the convolutional block attention module (CBAM) is added to give the model a global adaptive perception capability and pay more attention to the water body part of the image. The feature map is up-sampled using bilinear interpolation, and the features at different levels are fused, a process also known as decoding, to finalize the extraction of the lake water body. Compared with U-Net, AU-Net, RU-Net, ARU-Net, SER34AUNet, and MU-Net, the R50A3-LWBENet model has the fastest convergence speed and the highest MIoU accuracy with a value of 97.6%, which is able to better combine global and local information, refine the edge contours of the lake’s water body, and have stronger feature extraction capability and segmentation performance.

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Section: Model Performance Evaluation Metricsmentioning

confidence: 99%

A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images

Liu,

2024

Applied Sciences

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“…The number of nodes in the input and output layers needs to be determined in conjunction with the actual problem. Too many nodes in the hidden layer are easy to over-fit and weakly generalize the model, while too few nodes are easy to under-fit the model, and in practice, it is often calculated using Equation (12), with c taking values in the range of 1 to 10 [15].…”

Section: ()mentioning

confidence: 99%

Construction of Air Quality Level Prediction Model Based on STEPDISC-PCA-BP

Liu

Zhang

et al. 2023

Applied Sciences

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Air quality level has a complex nonlinear relationship with air pollutant and meteorological conditions, including multiple factors, overlapping information, and difficulty solving equations. In order to identify significant factors, remove correlations, reduce data dimensionality, and simplify the model structure, a BP neural network model for air quality level prediction optimized by stepwise discriminant analysis (STEPDISC) and principal component analysis (PCA) is proposed with 12 factors of historical daily meteorology and air pollutants in Bayannur city as samples. The results showed that, at the significance level of 0.01, the STEPDISC method retained 9 significant impact factors. The PCA method made an orthogonal linear combination of the 9 factors to form the principal components, and the contribution of the top 5 principal components were 37.6%, 19.2%, 15.3%, 8.8%, and 7.7%. At a contribution threshold of 0.85, the top 5 principal component scores were used as input nodes to construct the STEPDISC-PCA-BP model, which had a prediction accuracy of 85.5%.Compared with the PCA-BP and BP model, which had a prediction accuracy of 61.8% and 56.7%, respectively, the STEPDISC-PCA-BP model has a higher prediction accuracy, shorter time, and lower complexity of structure and data dimensionality, and can provide the necessary technical support for the local air quality improvement.

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“…The artificial neural network (ANN) algorithm is an algorithmic mathematical model that mimics the behavioral characteristics of animal neural networks for distributed parallel information processing. The use of ANN algorithms allows the establishment of relationships between remotely sensed product data and ground parameters in a non-linear manner and has been widely used for the inversion and estimation of various parameters (Almeida et al, 2009;Lei et al, 2020;Liu et al, 2021).…”

mentioning

confidence: 99%

Sustainable application of GF-6 WFV satellite data in desert steppe: A village-scale grazing study in China

Zhang

Wang²,

Xinguo³

et al. 2023

Front. Environ. Sci.

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This study investigated the vegetation growth and forage-livestock balance of desert grasslands at the village scale. This study was chosen to be conducted in the Ningxia Hui Autonomous Region, where desert grasslands are widely distributed and fragility of the environment. This paper establishes a model for estimating time series changes of above-ground biomass (AGB) in the study area based on remote sensing images from the Gaofen-6 satellite, combined with canopy spectra and AGB data of vegetated desert steppe areas. Next, the ecological carrying capacity and load of the desert steppe area were analyzed based on the number of sheep raised in different enclosures and the grazing methods employed. The status of the forage-livestock balance in the study area was assessed after measuring the crop yield of farmland. Finally, data and case support for grazing management in desert steppe were provided in combination with the research results. The study concluded that Gaofen-6 satellite images could quickly and accurately obtain information on vegetation growth and changes in desert grassland areas at a village scale. The available grazing time in a single year in the study area is inadequate to support current stocking levels. When ecological stability is the primary consideration, natural forage obtained through natural conditions cannot meet the needs of farmers for normal grazing and production activities. The suitable grazing time in the study area is limited in any year. The suitable maximum grazing intensity among all plots ranged from 0.334 to 2.541 sheep/ha. In the case of the balance between grass and livestock, the forage obtained from natural pastures could only satisfy 41.71% of the current stocking levels for sheep. Rotational grazing and subsidies for farmers help desert steppe to recover and achieve a forage-livestock balance.

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Marine water quality detection based on neural network

Cited by 4 publications

References 0 publications

A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images

A Novel Deep Learning Network Model for Extracting Lake Water Bodies from Remote Sensing Images

Construction of Air Quality Level Prediction Model Based on STEPDISC-PCA-BP

Sustainable application of GF-6 WFV satellite data in desert steppe: A village-scale grazing study in China

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