Underwater object detection, as the principal means of underwater environmental sensing, plays a significant part in the marine economic, military, and ecological fields. Due to the degradation problems of underwater images caused by color cast, blurring, and low contrast, we proposed a model for underwater object detection based on YOLO v7. In the presented detection model, an enhanced image branch was constructed to expand the feature extraction branch of YOLOv7, which could mitigate the feature degradation issues existing in the original underwater images. The contextual transfer block was introduced to the enhanced image branch, following the underwater image enhancement module, which could extract the domain features of the enhanced image, and the features of the original images and the enhanced images were fused before being fed into the detector. Focal EIOU was adopted as a new model bounding box regression loss, aiming to alleviate the performance degradation caused by mutual occlusion and overlapping of underwater objects. Taking URPC2020 and UTDAC2020 (Underwater Target Detection Algorithm Competition 2020) datasets as experimental datasets, the performance of our proposed model was compared against with other models, including YOLOF, YOLOv6 v3.0, DETR, Swin Transformer, and InternImage. The results show that our proposed model presents a competitive performance, achieving 80.71% and 86.32% in mAP@0.5 on URPC2020 and UTDAC2020, respectively. Comprehensively, the proposed model is capable of effectively mitigating the problems encountered in the task of object detection in underwater images with degraded features and exhibits great advancement.

Segmentation of island waterline contributes to shoreline movement analyzing, environmental monitoring, and integrated coastal zone management. To achieve high efficiency and high accuracy of island waterline segmentation in remote sensing images, we proposed a model for island waterline segmentation based on DANet (DANet-SMIW). In DANet-SMIW model, different indexes (Normalized Difference Water Index and OTSU) were taken as new channels added to input dataset, which enhanced waterline's spectral information. DANet backbone network was improved by dense connection of DenseNet. Loss function, consisting of binary cross entropy loss and Dice loss, was used to resolve the sample imbalance problem, and then, rough results of island waterline segmentation were refined by boundary refinement module (BRM). In total, 2042 island images were taken as experiment dataset, which were cropped from Landsat-8 images and divided into 1634 images for training, 100 images for testing, and 308 images for validation, and DANet-SMIW model was compared against other models, including FCN-32s, DeepLabv3+, PSPNet, Dense-ASPP, PSANet, ICNet, DuNet, and PIDNet. Results demonstrated that DANet-SMIW model achieved the highest values with pixel accuracy and Mean Intersection over Union and possessed higher segmentation efficiency than most other models. Collectively, DANet-SMIW model was an integrated accurate and efficient model for island waterline segmentation in remote sensing images.

Synthetic aperture radar (SAR) is an advanced microwave sensor widely used in ocean monitoring because of its resilience to light and weather conditions. However, SAR ship detection tends to have relatively low accuracy due to the prevalence of complex backgrounds and small targets in the detection process. To address these issues, we proposed ECF-YOLO, an improved ship detection algorithm based on YOLOv8. The algorithm enhanced the feature extraction ability of the model and reduced the number of parameters and computational cost by developing a novel C2f-EMSCP module, which replaced the original C2f module in the backbone network. Additionally, we proposed the CGFM module in the neck network, which was designed to improve the detection accuracy of small ship targets by selecting features after combining shallow and deep feature maps. Furthermore, the Inner-SIoU loss function was introduced to replace the CIoU, providing a more precise overlap calculation between the target and anchor boxes, thus further improving detection accuracy. The experimental results for the SAR ship detection dataset showed that compared to YOLOv8n, ECF-YOLO improved $ \mathrm{AP_{75}} $ by 2.8% and $ \mathrm{AP_{50:95}} $ by 0.9%. Compared to other mainstream algorithms like YOLOv9t, YOLOv10n, and YOLO11n, ECF-YOLO achieved improvements of 3.4%, 4.6%, and 4.9% for $ \mathrm{AP_{75}} $, and 3.4%, 1.9%, 3.0% for $ \mathrm{AP_{50:95}} $, respectively, demonstrating its effectiveness for detecting small targets.

Accurate and reliable water level prediction plays a crucial role in the optimal management of water resources and reservoir scheduling. Water level data have the characteristics of volatility and temporality; a single water level prediction model can only be applied to specific hydrological conditions and reservoirs. Therefore, in this paper, we present a robust lightweight model for water level prediction, namely WLP-VBL, by using a combination of VMD, BA, and LSTM. The proposed WLP-VBL model consists of three steps: first, the water level dataset is decomposed by EMD to obtain a number of decomposition layers K, and then VMD is used to decompose the original water level dataset into K intrinsic modal functions (IMFs) to produce a clearer signal. Next, the IMF data are sent to an LSTM neural network optimized by BA for prediction, and finally each component is superimposed to obtain the predicted value. In order to evaluate the effectiveness of the model, experiments were carried out on water level data for the Gan River. The results indicate that: (1) Compared with state-of-the art methods, e.g., LSTM, VMD-LSTM, and EMD-LSTM, WLP-VBL exhibited the best performance. The MSE and MAE of WLP-VBL decreased by 69.6~74.7% and 45~98.5%, respectively. (2) The proposed model showed stronger robustness for water level prediction, and was able to handle highly volatile and noisy data.

The journal retracts the article “WLP-VBL: A Robust Lightweight Model for Water Level Prediction”

To address the issue of relatively simple features and methods used in methane concentration inversion, which leads to low overall accuracy, this study proposes a methane concentration inversion method based on multi-feature fusion and Stacking ensemble learning. The method leverages the series-parallel cascade structure between multiple base models and meta-models to learn different feature representations and patterns in the original data, fully exploring the intrinsic relationships between various feature factors and methane concentration. This approach improves inversion accuracy and generalization capability. Finally, the research team conducted experimental validation in the eastern region of Xinjiang. The experimental results show that, compared with other typical methods, the Stacking ensemble model proposed in this study achieves the best inversion performance, with R², RMSE, and MAE values of 0.9747, 2.8294, and 1.5299, respectively. In terms of seasonal distribution, methane concentration in eastern Xinjiang typically shows lower average values in the spring and autumn and higher average values in the summer and winter.