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Water Quality Prediction Based on the KF-LSTM encoder-decoder Network: A Case Study with Missing Data Collection

WATER 2023年第14期15卷 2542页

作者： Cai, Hao Zhang, Chen Xu, Jianlong Wang, Fei Xiao, Lianghong Huang, Shanxing Zhang, Yufeng Shantou Univ Dept Comp Sci Shantou Peoples R China Guangdong Prov Shantou Ecol Environm Monitoring Ce Shantou 515057 Peoples R China

This paper focuses on water quality prediction in the presence of a large number of missing values in water quality monitoring data. Current water quality monitoring data mostly come from different monitoring stations in different water bodies. As the duration of water quality monitoring increases, the complexity of water quality data also increases, and missing data is a common and difficult to avoid problem in water quality monitoring. In order to fully exploit the valuable features of the monitored data and improve the accuracy of water quality prediction models, we propose a long short-term memory (LSTM) encoder-decoder model that combines a Kalman filter (KF) with an attention mechanism. The Kalman filter in the model can quickly complete the reconstruction and pre-processing of hydrological data. The attention mechanism is added between the decoder and the encoder to solve the problem that traditional recursive neural network models lose long-range information and fully exploit the interaction information among high-dimensional covariate data. Using original data from the Haimen Bay water quality monitoring station in the Lianjiang River Basin for analysis, we trained and tested our model using detection data from 1 January 2019 to 30 June 2020 to predict future water quality. The results show that compared with traditional LSTM models, KF-LSTM models reduce the average absolute error (MAE) by 10%, the mean square error (MSE) by 21.2%, the root mean square error (RMSE) by 13.2%, while increasing the coefficient of determination (R2) by 4.5%. This model is more suitable for situations where there are many missing values in water quality data, while providing new solutions for real-time management of urban aquatic environments.

关键词： Kalman filter LSTM encoder-decoder time series prediction water quality prediction attention mechanism

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Cloud Detection Method Based on Spatial-Spectral Features and encoder-decoder Feature Fusion

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1页

作者： Zhang, Jing Shi, Xinlong Wu, Jun Song, Liangnong Li, Yunsong Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Xidian Univ Sch Telecommun Engn Xian 710071 Peoples R China Xidian Univ Hangzhou Inst Technol Hangzhou 311231 Peoples R China Xidian Univ Sch Telecommun Engn Xian 710071 Peoples R China

Cloud obscuration in remote sensing images affects Earth observation tasks by causing blurred and incomplete surface observation information. Regarding this, cloud detection is crucial in the processing of remote sensing images. However, existing cloud detection methods present some challenges, such as missed detection of thin cloud areas and false detection caused by confusing clouds with highlighted areas such as snow and ice. To address these problems, in this article, we proposed a cloud detection network that incorporates spectral feature enhancement and spatial-spectral feature fusion. Based on the difference in reflectivity of clouds and ground objects in the atmosphere, we proposed a short-wave infrared cloud index (SWIR-Index) and designed a feature-guided module to incorporate the spectral feature into the network and guide the training of the network to enhance the network's ability to learn differential features of snow, ice, and clouds. To fully utilize the spectral band information and spatial features of remote sensing images, we developed a spatial-spectral feature fusion module (SSFFM) that extracts spatial features at different scales and performs inter-spectral information fusion of spectral bands. Furthermore, we proposed a encoder-decoder feature fusion module that automatically calculates pixel weights by using a weight extraction block. The ablation study proves that our method can improve the feature extraction ability, reduce the leakage and misdetection, and improve the detection accuracy. Experimental results on Sentinel-2A images demonstrate the superior performance of the method, reaching 98.65(%) overall accuracy (OA) on the WHUS2-CD dataset, 97.50(%) on the S2-CMC dataset, and 92.36(%) on the CloudSEN12 dataset, which outperforms other algorithms.

关键词： Cloud detection encoder-decoder feature fusion short-wave infrared cloud index (SWIR-Index) spatial-spectral information

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A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting

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JOURNAL OF HYDROLOGY 2023年第1期619卷

作者： Jahangir, Mohammad Sina You, John Quilty, John Univ Waterloo Dept Civil & Environm Engn Waterloo ON Canada

Deep neural network (DNN) models have become increasingly popular in the hydrology community. However, most studies are related to (rainfall-) runoff simulation and comparatively fewer studies have focused on runoff forecasting. In this study, quantile-based (q = 0.05, 0.5, 0.95) encoder-decoder (ED) models that use long short -term memory network (LSTM) and dense network (DN) blocks were developed for three and five days ahead runoff forecasting. Through linear (LW) and non-linear (NLW) wavelet selection, hybrid models LSTM-DN, LSTM-DN-LW, LSTM-DN-NLW, ED, ED-LW, and ED-NLW were developed. For each lead time (LT = 3, 5) and value of q, different model configurations were created using different input lag lengths (IL = 15, 45, 180). The developed models were tested for runoff forecasting using three basins (with different characteristics) from the Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) dataset. The models were compared using deterministic (e.g., the Kling-Gupta efficiency [KGE] metric) and probabilistic (e.g., reliability) statistical metrics. While the models showed high variability in performance across the three basins (KGE = 0.308-0.979 for the q = 0.5 models), very high accuracy (up to KGE = 0.979) was achieved for one of the basins with high snowmelt. The ED-NLW model was found to generally outperform the other models. Although the LSTM-DN model had the highest median KGE (0.434 across all configurations), the ED and ED-NLW models had higher reliability than LSTM-DN (90% and 91%, respectively, considering a 90% confidence level). Models coupled with NLW performed superior to those that used LW. All ED models had high reliability despite two of the basins achieving median KGE values of similar to 0.390, highlighting that quantile-based models can generate reliable forecast intervals even when the KGE of the median forecast (q = 0.5) is low. An additional experiment generated synthetic precipitation forecasts with varying degrees

关键词： encoder-decoder Deep learning LSTM Wavelet decomposition Runoff forecasting Hydrological forecasting

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The trade-off between topology and content in community detection: An adaptive encoder-decoder-based NMF approach

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 209卷

作者： Zhao, Zhili Ke, Zhengyou Gou, Zhuoyue Guo, Hao Jiang, Kunyuan Zhang, Ruisheng Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China

Community detection is an important research field of complex network analysis and focuses on the study of networks' aggregation behaviours. Different from traditional methods that only consider network structural topology, many efforts have been put into combining network structural topology with node content attributes to achieve better community detection performance. However, it is critical to make an appropriate trade-off between structural topology and node content. In this paper, we propose an adaptive trade-off approach, called ANMF, which not only considers both structural topology and node content, but also provides a flexible parameter to balance their contribution. Compared with other related approaches, ANMF is a kind of non-negative matrix factorization (NMF)-based community detection method, but it imposes more constraints on the network reconstruction. More precisely, ANMF simultaneously employs a decoder that reconstructs a network from its community membership space and an encoder that transforms the network into the community membership space. Moreover, compared with the most related state-of-the-art effort adaptive semantic community detection (ASCD), which considers the topology part always has more contribution if there is a mismatch, ANMF considers the mismatch in two different situations, i.e., the topology part contributes more than the node content part and the node content part contributes more than the topology part. Based on the intensive evaluation on both real and artificial networks, ANMF provides higher normalized mutual information (NMI) values of 4.95%similar to 126.41% than the models without considering node content information on 13 out of 14 experimental networks. ANMF also presents higher NMI values of 7.38%similar to 201.01% than ASCD on 13 out of 14 experimental networks. Moreover, ANMF shows good convergence performance, and it can converge after 100 iterations on all of the networks. ANMF also provides stability alike to s

关键词： Non-negative matrix factorization Community detection encoder-decoder Network topology Node attributes

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Efficient MRI Brain Tumor Segmentation Using Multi-resolution encoder-decoder Networks 1

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6th International MICCAI Brain-Lesion Workshop (BrainLes)

作者： Soltaninejad, Mohammadreza Pridmore, Tony Pound, Michael Univ Nottingham Sch Comp Sci Comp Vis Lab Nottingham England

ISBN: (数字)9783030720872

ISBN: (纸本)9783030720865;9783030720872

In this paper, we propose an automated three dimensional (3D) deep learning approach for the segmentation of gliomas in pre-operative brain MRI scans. We introduce a state-of-the-art multi-resolution architecture based on encoder-decoder which comprise of separate branches to incorporate local high-resolution image features and wider low-resolution contextual information. We also used a unified multi-task loss function to provide end-to-end segmentation training. For the task of survival prediction, we propose a regression algorithm based on random forests to predict the survival days for the patients. Our proposed network is fully automated and designed to take input as patches that can work on input images of any arbitrary size. We trained our proposed network on the BraTS 2020 challenge dataset that consists of 369 training cases, and then validated on 125 unseen validation datasets, and tested on 166 unseen cases from the testing dataset using a blind testing approach. The quantitative and qualitative results demonstrate that our proposed network provides efficient segmentation of brain tumors. The mean Dice overlap measures for automatic brain tumor segmentation of the validation dataset against ground truth are 0.87, 0.80, and 0.66 for the whole tumor, core, and enhancing tumor, respectively. The corresponding results for the testing dataset are 0.78, 0.70, and 0.66, respectively. The accuracy measures of the proposed model for the survival prediction tasks are 0.45 and 0.505 for the validation and testing datasets, respectively.

关键词： Convolutional neural network encoder-decoder Deep learning MRI Brain tumor segmentation

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EDLAD: AN encoder-decoder LONG SHORT-TERM MEMORY NETWORK-BASED ANOMALY DETECTOR FOR HYPERSPECTRAL IMAGES

EDLAD: AN ENCODER-DECODER LONG SHORT-TERM MEMORY NETWORK-BAS...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Zhu, Dehui Du, Bo Zhang, Liangpei Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China Wuhan Univ Sch Comp Wuhan 430072 Peoples R China

ISBN: (纸本)9781665403696

In this paper, an encoder-decoder long short-term memory network-based anomaly detector (denoted as EDLAD) is proposed for hyperspectral images. The proposed EDLAD aims to simultaneously alleviate anomaly contamination and build a stable background component for anomaly detection. To reduce anomaly contamination, the EDLAD first utilizes a well-designed encoder-decoder LSTM to reconstruct the hyperspectral image. Based on the concept that the anomaly pixels occupy an extremely small fraction of the image, the well-designed encoder-decoder LSTM network tends to maintain the background and alleviate anomaly during the reconstruction process since the whole image is employed for training the network. Then the dimension reduction is used to further alleviate the anomaly contamination and build a stable background component. Finally, the EDLAD applies the Mahalanobis distance differences to detect the probable anomalies. The experiments on two benchmark hyperspectral images demonstrate the superiority of the EDLAD in anomaly detection.

关键词： Hyperspectral imagery (HSI) anomaly detection deep learning encoder-decoder long short-term memory (LSTM)

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ATTENTION-BASED encoder-decoder NETWORK FOR SINGLE IMAGE DEHAZING

ATTENTION-BASED ENCODER-DECODER NETWORK FOR SINGLE IMAGE DEH...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Gao, Shunan Zhu, Jinghua Xi, Heran Heilongjiang Univ Sch Comp Sci & Technol Harbin Peoples R China

ISBN: (纸本)9781665449892

Single image dehazing is a challenging problem in computer vision due to it is highly ill-posed. Although recent research has made great progress, the dehazed images produced by existing models still have residual haze and lose too much detail information. To solve the above problems, we propose an end-to-end Attention-based encoder-decoder Network (AEDNet) which is capable to effectively remove haze while preserving image details well. AEDNet employs a novel channel shuffle attention mechanism to adaptively adjust the weight of each channel-wise feature. This attention mechanism is integrated in residual block which is the core feature extraction module of encoder-decoder. Extensive experiments on synthetic datasets and real-hazy images demonstrate that our AEDNet achieves better performance compared with previous state-of-the-art methods.

关键词： Single image dehazing encoder-decoder channel shuffle attention image detail restoration

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Adding visual attention into encoder-decoder model for multi-modal machine translation

JOURNAL OF ENGINEERING RESEARCH

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JOURNAL OF ENGINEERING RESEARCH 2023年第2期11卷

作者： Xu, Chun Yu, Zhengqing Shi, Xiayang Chen, Fang Xinjiang Univ Finance & Econ Coll Informat Management Urumqi Peoples R China Zhengzhou Univ Light Ind Zhenghzou Peoples R China Henan Univ Tradit Chinese Med Med Dept Zhengzhou Peoples R China

Multi-modal neural machine translation (MNMT) aims to integrate visual and textual information to translate source sentences into target and attracts a lot of attentions. Existing methods contribute a lot for capturing interactions between visual and textual features to improve the performance of neural machine translation (NMT). However, most of them don't consider the multi-modal consistency for MNMT. In fact, the image provides the semantic global consistency between different languages. We believe that adding bilingual-visual agreement into the encoder and decoder simultaneously can obtain bilingual representations and is useful for NMT. In this paper, we propose to simultaneously integrate visual information in encoder and decoder to learn the interactions between visual and textual features, in this paper called the model VMNMT. As the visual information provide global context, the encoder and decoder can learn the bilingual representations. Besides, we introduce a new bilingual-visual agreement decoder to learn to better representations of corresponding imagesentence pairs. In the experiment, the improvement was 2.02 BLEU on the English-German16 dataset and 1.9 BLEU on the English-German17 dataset. The results show that our method can outperform baselines on several widely-used datasets in terms of various metrics.

关键词： Natural language processing Multi-modal machine translation Bilingual-visual encoder-decoder Bilingual representations

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基于encoder-decoder框架的城镇污水厂出水水质预测

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中国农村水利水电 2023年第11期 93-99页

作者：史红伟陈祺王云龙李鹏程长春理工大学电子信息工程学院吉林长春130022 长春水务投资发展集团有限公司吉林长春130022

由于污水厂的出水水质指标繁多、污水处理过程中反应复杂、时序非线性程度高,基于机理模型的预测方法无法取得理想效果。针对此问题,提出基于深度学习的污水厂出水水质预测方法,并以吉林省某污水厂监测水质为来源数据,利用多种结合encod... 详细信息

由于污水厂的出水水质指标繁多、污水处理过程中反应复杂、时序非线性程度高,基于机理模型的预测方法无法取得理想效果。针对此问题,提出基于深度学习的污水厂出水水质预测方法,并以吉林省某污水厂监测水质为来源数据,利用多种结合encoder-decoder结构的神经网络预测水质。结果显示,所提结构对LSTM和GRU网络预测能力都有一定提升,对长期预测能力提升更加显著,ED-GRU模型效果最佳,短期预测中的4个出水水质指标均方根误差(RMSE)为0.7551、0.2197、0.0734、0.3146,拟合优度(R2)为0.9013、0.9332、0.9167、0.9532,可以预测出水质局部变化,而长期预测中的4个指标RMSE为1.7204、1.7689、0.4478、0.8316,R2为0.4849、0.5507、0.4502、0.7595,可以预测出水质变化趋势,与顺序结构相比,短期预测RMSE降低10%以上,R2增加2%以上,长期预测RMSE降低25%以上,R2增加15%以上。研究结果表明,基于encoder-decoder结构的神经网络可以对污水厂出水水质进行准确预测,为污水处理工艺改进提供技术支撑。

关键词：污水厂出水 encoder-decoder 多指标水质预测 GRU模型

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Efficient encoder-decoder Network With Estimated Direction for SAR Ship Detection

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷

作者： Niu, Yuzhen Li, Yuezhou Huang, Jiangyi Chen, Yuzhong Fuzhou Univ Coll Comp & Data Sci Fujian Prov Key Lab Networking Comp & Intelligent Fuzhou 350116 Peoples R China Minist Educ Key Lab Spatial Data Min & Informat Sharing Fuzhou 350108 Fujian Peoples R China

Synthetic aperture radar (SAR) image ship detection has important applications in marine surveillance. There are two limitations when applying advanced detection methods naively for SAR ship detection. First, most detectors construct the model as an encoder and rely on the feature pyramid network (FPN) head for accurate prediction, which may lead to high computational costs. Second, the background noises in the ground truth (annotated as rectangular bounding boxes) of angular ships bring difficulties for model training. To meet these challenges, we propose an efficient encoder-decoder network with estimated direction for ship detection in SAR images. First, we present an anchor-free encoder-decoder model that can efficiently extract multiple-level features. Second, we formulate ship detection as a multitask learning problem, including a bounding box prediction and a ship direction regression. The estimated ship direction can weakly supervise and benefit ship detection. Furthermore, we develop a center-weighted labeling method for overlapped annotations. Comprehensive experiments on SAR-Ship-Detection and SSDD datasets show that our method achieves state-of-the-art performance with a high running speed.

关键词： Marine vehicles Radar polarimetry Synthetic aperture radar Decoding Feature extraction Task analysis Background noise encoder-decoder multitask learning ship detection in SAR image synthetic aperture radar (SAR) image

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