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A probabilistic forecast for multi-year ENSO using bayesian convolutional neural network

ENVIRONMENTAL RESEARCH LETTERS 2024年第12期19卷 124023页

作者： Sreeraj, P. Balaji, B. Paul, Arya Francis, P. A. Minist Earth Sci MoES Indian Natl Ctr Ocean Informat Serv INCOIS Hyderabad India Minist Earth Sci MoES Indian Inst Trop Meteorol Pune India

A robust El Ni & ntilde;o Southern Oscillation (ENSO) prediction is essential for monitoring the global climate, regional monsoons, and weather extremes. Despite dedicated efforts spanning decades, the precise prediction of ENSO events through numerical modeling beyond a couple of seasonal lead times remains a daunting challenge. The advent of deep learning-based approaches marks a transformative era in climate and weather prediction. However, many machine learning-based studies attempting ENSO prediction are confined to singular estimates, lacking adequate quantification of uncertainty in learned parameters and overlooking the crucial need for a nuanced understanding of ENSO prediction confidence. Here, we introduce a deep learning-based bayesian convolutional neural network model that provides robust probabilistic predictions for ENSO with a lead time of up to 9-10 months across all seasons. The bayesian layers within the convolutional neural network maintain the capability to predict a distribution of learned parameters. Augmented with bias correction, our model reproduces the amplitude of the Ni & ntilde;o 3.4 index with fidelity for lead up to 9-10 months. The inherent capacity for uncertainty modeling enhances the reliability of bayesian neural networks (BNNs), making them particularly valuable in operational services. This research holds substantial socio-economic implications as it enhances our forecasting capabilities and rigorously quantifies forecast uncertainties, providing valuable insights for planning and policymaking.

关键词： bayesian convolutional neural network ENSO forecast uncertainty estimation seasonal predictions

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A feature map of frequency response functions based model updating method using the bayesian convolutional neural network

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2023年 204卷

作者： Wang, Zhenyu Liang, Deli Ding, Sheng Zhang, Wenliang He, Huan Nanjing Univ Aeronaut & Astronaut State Key Lab Mech & Control Aerosp Struct Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut Inst Vibrat Engn Res Nanjing 210016 Peoples R China Beijing Inst Astronaut Syst Engn Beijing 100076 Peoples R China MIIT Key Lab Multifunct Lightweight Mat & Struct Nanjing 210016 Peoples R China

Combined with the application of deep learning is a hot topic in the current research of finite element (FE) model updating. In this paper, A feature map of frequency response functions (FMFRF) based model updating method using the bayesian convolutional neural network (BCNN) is proposed. This proposed method is based on frequency response functions (FRFs), which eliminates the steps of modal identification and modal matching and can adjust the structural parameters and damping parameters of the model at the same time, thus improving efficiency. The FRFs of multiple sensors are combined and transformed into the feature map, which is used as the input to the BCNN. The output of BCNN is the vector composed of uncertain model parameters. The FMFRF method solves the model updating problems as positive problems with the help of BCNN, and can accurately build the complex mapping between the FRFs and the model parameters. The BCNN introduces probability distributions over the weights, which helps avoid overfitting when training on small datasets and achieves high accuracy of the network. The benchmark multi-plate structure is used as an example to verify the validity and accuracy of the FMFRF method, and the correlation and sensitivity of the model parameters to the feature maps of FRFs are systematically analyzed. Finally, the robustness of the model updating results is investigated, and it proves that the FMFRF method shows good performance.

关键词： Finite element model updating bayesian convolutional neural network Frequency response function Feature maps of FRFs Correlation analysis Robustness

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Probabilistic SAR-based water segmentation with adapted bayesian convolutional neural network

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REMOTE SENSING OF ENVIRONMENT 2023年 285卷

作者： Hertel, Victor Chow, Candace Wani, Omar Wieland, Marc Martinis, Sandro German Aerosp Ctr DLR German Remote Sensing Data Ctr DFD Munchener Str 20 D-82234 Wessling Oberpfaffenhofen Germany NYU Dept Civil & Urban Engn Brooklyn NY 11201 USA Univ Calif Berkeley Environm Syst Dynam Lab Berkeley CA 94720 USA

Geospatial resources, including satellite-based synthetic aperture radar (SAR) and optical data, have been instrumental in providing time-sensitive information about the extent and impact of natural hazard events, such as floods, to support emergency response and hazard management efforts. In effect, finite resources can be better optimized to support the needs of often extensively affected areas. However, the derivation of SAR-based flood information is not without its challenges and inaccurate flood detection can result in poor decision-making and non-trivial, adverse consequences. Reliable uncertainty quantification of flood extent estimates addresses this risk. In this context, our study presents the results of two probabilistic convolutional neural networks (CNNs) adapted for SAR-based water segmentation with freely available Sentinel-1 interferometric wide (IW) swath ground range detected (GRD) data. In particular, the performance of a variational inference-based bayesian convolutional neural network (BCNN) is evaluated against that of a Monte Carlo dropout network (MCDN). In previous studies where uncertainty information has been generated along with segmentation results, MCDN has been more commonly applied as an approximation of bayesian deep learning. Differences between the two approaches are highlighted with the application of a set of extended performance metrics. Both segmentation and uncertainty outputs are evaluated at data-, model-, tile-and scene-levels. The methods are demonstrated on the binary segmentation of a reference water dataset. We show that while different probabilistic techniques return comparable segmentation accuracies, they are differentiated based on their performance in assigning reliable probabilities. In particular, MCDNs characterized by more restrictive architectures generally lead to over-confident prediction intervals, whereas BCNNs have greater flexibility to learn the mean and the spread of the parameter posterior. Furthermo

关键词： Semantic segmentation bayesian convolutional neural network MCDN Uncertainty quantification SAR Sentinel-1 Crisis information management

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Medicine-Shelf matching strategy based on bayesian convolutional neural network with fuzzy analytic hierarchy process

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期231卷

作者： Tian, Ran Yang, Saisai Wang, Chu Ma, Zhongyu Kang, Chunming Northwest Normal Univ Dept Comp Sci & Engn Lanzhou 730070 Peoples R China

In pharmaceutical warehousing operations, a scientific and reasonable medicine and shelf matching strategy can improve medicine shelving efficiency and manual work efficiency. However, the traditional matching strategy has the problems of static matching and low matching efficiency, so this paper proposes the matching algorithm for drugs and shelves based on the fuzzy analytic hierarchy process and bayesian convolutional neural network (FAHP-BCNN). First, we propose a drug-shelf matching degree model and discuss the influence of the dynamic matching process on the matching results by studying two parts: the attribute matching degree and the dynamic matching influence degree. Secondly, this paper quantitatively assessed the importance of the two matches by means of the fuzzy analytic hierarchy process. Finally, we mapped the massive matching information onto bayesian convolutional neural network nodes, constructed a dynamic matching network model, and obtained competitive matching strategies and results. The experimental results show that the FAHP-BCNN algorithm exhibits better matching results in all three scenarios with different quantity ratios of drugs and shelf matching. Compared with the fixed cargo matching strategy of the ABC classification method, the FAHP-BCNN algorithm shows excellent performance in both the manual walking distance index and fatigue index. In summary, the drug-shelf matching algorithm based on FAHP-BCNN is effective and can provide theoretical support for pharmaceutical warehousing enterprises to perform drug-shelf matching.

关键词： Shelf and drug matching Fuzzy analytic hierarchy process Attribute matching degree Dynamic matching influence degree bayesian convolutional neural network

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Geochemical anomaly identification and uncertainty quantification using a bayesian convolutional neural network model

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APPLIED GEOCHEMISTRY 2022年第0期146卷

作者： Huang, Dazheng Zuo, Renguang Wang, Jian China Univ Geosci State Key Lab Geol Proc & Mineral Resources Wuhan 430074 Peoples R China Chengdu Univ Technol Coll Earth Sci Chengdu 610059 Peoples R China

Geochemical prospecting plays an important role in mineral exploration. In recent years, deep learning algo-rithms (DLAs) have been applied in mapping geochemical anomalies associated with mineralization. However, few of them evaluated the effects of data and model uncertainty on geochemical anomaly identification, which can introduce bias and risk in geochemical prospecting. In this study, a bayesian convolutional neural network model (BCNN), which applies Bayes by Backprop to a convolutional neural network, was employed to extract geochemical anomalies associated with mineralization and quantify the two types of uncertainties, i.e., aleatoric (data-related) and epistemic (model-related) uncertainty. A case study on recognizing geochemical anomalies related to gold polymetallic mineralization in northern Sichuan Province of China was conducted. The results show that the geochemical anomalies extracted by BCNN are highly spatially correlated with known gold de-posits. Meanwhile, both aleatoric and epistemic uncertainty linked to the identified geochemical anomalies were quantified. Areas with high uncertainty are mainly distributed at the boundaries of the geochemical high anomalous zones. Therefore, future mineral exploration should focus on regions with high anomalies but low uncertainty. The obtained results provide an important decision basis for the prospecting of gold polymetallic deposits in the study area.

关键词： Geochemical anomalies Uncertainty quantification bayesian convolutional neural network Deep learning

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Feedback-Assisted Automatic Target and Clutter Discrimination Using a bayesian convolutional neural network for Improved Explainability in SAR Applications

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REMOTE SENSING 2022年第23期14卷 6096-6096页

作者： Blomerus, Nicholas Cilliers, Jacques Nel, Willie Blasch, Erik de Villiers, Pieter Univ Pretoria Dept Elect Elect & Comp Engn ZA-0028 Pretoria South Africa Council Ind & Sci Res ZA-0184 Pretoria South Africa Air Force Res Lab Dayton OH 45324 USA

In this paper, a feedback training approach for efficiently dealing with distribution shift in synthetic aperture radar target detection using a bayesian convolutional neural network is proposed. After training the network on in-distribution data, it is tested on out-of-distribution data. Samples that are classified incorrectly with high certainty are fed back for a second round of training. This results in the reduction of false positives in the out-of-distribution dataset. False positive target detections challenge human attention, sensor resource management, and mission engagement. In these types of applications, a reduction in false positives thus often takes precedence over target detection and classification performance. The classifier is used to discriminate the targets from the clutter and to classify the target type in a single step as opposed to the traditional approach of having a sequential chain of functions for target detection and localisation before the machine learning algorithm. Another aspect of automated synthetic aperture radar detection and recognition problems addressed here is the fact that human users of the output of traditional classification systems are presented with decisions made by "black box" algorithms. Consequently, the decisions are not explainable, even to an expert in the sensor domain. This paper makes use of the concept of explainable artificial intelligence via uncertainty heat maps that are overlaid onto synthetic aperture radar imagery to furnish the user with additional information about classification decisions. These uncertainty heat maps facilitate trust in the machine learning algorithm and are derived from the uncertainty estimates of the classifications from the bayesian convolutional neural network. These uncertainty overlays further enhance the users' ability to interpret the reasons why certain decisions were made by the algorithm. Further, it is demonstrated that feeding back the high-certainty, incorrectly class

关键词： synthetic aperture radar automatic target recognition bayesian convolutional neural network feedback-assisted bayesian convolutional neural network explainable artificial intelligence deep machine learning epistemic uncertainty uncertainty estimation

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Prediction of urban flood inundation using bayesian convolutional neural networks

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2024年第11期38卷 4485-4500页

作者： Zheng, Xiang Zheng, Minling Nantaihu Design Inst Water Conservancy & Hydropowe Huzhou 313000 Zhejiang Peoples R China Huzhou Univ Sch Sci Huzhou 313000 Zhejiang Peoples R China

Urban flood risk management has been a hot issue worldwide due to the increased frequency and severity of floods occurring in cities. In this paper, an innovative modelling approach based on the bayesian convolutional neural network (BCNN) was proposed to simulate the urban flood inundation, and to provide a reliable prediction of specific water depth. To develop the model, a series of historical rainfall data during the last 20 years were collected in Rushan China and the responding flood events were reproduced using physically based hydraulic model. The flood condition factors used in modeling include spacial factors and precipitation factors. The results showed that the BCNN model not only inherits the powerful ability of aggregating spacial information from CNNs to perform high level of accuracy and computational efficiency in predicting 2D urban flood inundation maps, but also offers a measure of uncertainty in the form of predictive variance, providing insights into the confidence and reliability of its predictions. The proposed BCNN method offered a new perspective for the analysis of surrogate model regarding real-time forecasting of flood inundation.

关键词： Deep learning bayesian convolutional neural network Data-driven simulation Rapid flood modelling Hydrodynamic modelling

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bayesian convolutional neural networks for predicting the terrestrial water storage anomalies during GRACE and GRACE-FO gap

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JOURNAL OF HYDROLOGY 2022年 604卷 127244-127244页

作者： Mo, Shaoxing Zhong, Yulong Forootan, Ehsan Mehrnegar, Nooshin Yin, Xin Wu, Jichun Feng, Wei Shi, Xiaoqing Nanjing Univ Sch Earth Sci & Engn Minist Educ Key Lab Surficial Geochem Nanjing Peoples R China China Univ Geosci Wuhan Sch Geog & Informat Engn Wuhan Peoples R China Aalborg Univ Dept Planning Geodesy Grp Aalborg Denmark Nanjing Hydraul Res Inst State Key Lab Hydrol Water Resources & Hydraul En Nanjing Peoples R China Sun Yat Sen Univ Sch Geospatial Engn & Sci Zhuhai Peoples R China Chinese Acad Sci Innovat Acad Precis Measurement Sci & Technol Inst Geodesy & Geophys State Key Lab Geodesy & Earths Dynam Wuhan Peoples R China

The monthly terrestrial water storage anomaly (TWSA) observations during the gap period between the Gravity Recovery and Climate Experiment (GRACE) satellite and its Follow-On (GRACE-FO) are missing, leading to discontinuity in the time series, and thus, impeding full utilization and analysis of the data. Despite previous efforts undertaken to tackle this issue, a gap-filling TWSA product with desirable accuracy at a global scale is still lacking. In this study, a straightforward and hydroclimatic data-driven bayesian convolutional neural network (BCNN) is proposed to bridge this gap. Benefiting from the excellent capability of BCNN in handling image data and the integration of recent deep learning advances (including residual-skip connections and spatial-channel attentions), the proposed method can automatically extract informative features for TWSA predictions from multiple predictor data. The BCNN predictions are compared with reanalyzed/simulated TWSA, Swarm solution, and the TWSA prediction products generated by three recent studies, using commonly used accuracy metrics. Results demonstrate BCNN's superior performance to obtain higher-quality TWSA predictions, particularly in relatively arid regions. Additionally, a comparison with two independent datasets at the basin scale further suggests that the BCNN-infilled TWSA is reliable to bridge the gap and enhance data consistency. Our gap-filling product can ultimately contribute to correcting the bias in long-term trend estimates, maintaining the continuity of TWSA time series and thus benefiting subsequent applications desiring continuous data records.

关键词： GRACE bayesian convolutional neural network Gap filling ERA5 Deep learning

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Near-Real-Time Monitoring of Global Terrestrial Water Storage Anomalies and Hydrological Droughts

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GEOPHYSICAL RESEARCH LETTERS 2025年第7期52卷

作者： Mo, Shaoxing Schumacher, Maike van Dijk, Albert I. J. M. Shi, Xiaoqing Wu, Jichun Forootan, Ehsan Nanjing Univ Sch Earth Sci & Engn Key Lab Surficial Geochem Minist Educ Nanjing Peoples R China Aalborg Univ Dept Sustainabil & Planning Geodesy Grp Aalborg Denmark Australian Natl Univ Fenner Sch Environm & Soc Canberra ACT Australia

Global terrestrial water storage anomaly (TWSA) products from the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On mission (GRACE/FO) have an approximately three-month latency, significantly limiting their operational use in water management and drought monitoring. To address this challenge, we develop a bayesian convolutional neural network (BCNN) to predict TWSA fields with uncertainty estimates during the latency period. The results demonstrate that BCNN provides near-real-time TWSA estimates that closely match GRACE/FO observations, with median correlation coefficients of 0.92-0.95, Nash-Sutcliffe efficiencies of 0.81-0.89, and root mean squared errors of 1.79-2.26 cm for one- to three-month ahead predictions. More importantly, the model advances global hydrological drought monitoring by enabling detection up to three months before GRACE/FO data availability, with median characterization mismatches below 16.4%. This breakthrough in early warning capability addresses a fundamental constraint in satellite-based hydrological monitoring and offers water resource managers critical lead time to implement drought mitigation strategies.

关键词： GRACE bayesian convolutional neural network hydrological drought terrestrial water storage anomaly data latency deep learning

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An Uncertainty Quantization-Based Method for Anti-UAV Detection in Infrared Images

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Computers, Materials & Continua 2025年第4期83卷 1439-1458页

作者： Can Wu Wenyi Tang Yunbo Rao Yinjie Chen Hui Ding Shuzhen Zhu Yuanyuan Wang School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610054China State Key Laboratory of Air Traffic Management System Nanjing210000China National Airspace Management Center Beijing100094China

Infrared unmanned aerial vehicle(UAV)target detection presents significant challenges due to the inter-play between small targets and complex *** methods,while effective in controlled environments,often fail in scenarios involving long-range targets,high noise levels,or intricate backgrounds,highlighting the need for more robust *** address these challenges,we propose a novel three-stage UAV segmentation framework that leverages uncertainty quantification to enhance target *** framework incorporates a bayesian convolutional neural network capable of generating both segmentation maps and probabilistic uncertainty *** utilizing uncer-tainty predictions,our method refines segmentation outcomes,achieving superior detection ***,this marks the first application of uncertainty modeling within the context of infrared UAV target *** evaluations on three publicly available infrared UAV datasets demonstrate the effectiveness of the proposed *** results reveal significant improvements in both detection precision and robustness when compared to state-of-the-art deep learning *** approach also extends the capabilities of encoder-decoder convolutional neural networks by introducing uncertainty modeling,enabling the network to better handle the challenges posed by small targets and complex environmental *** bridging the gap between theoretical uncertainty modeling and practical detection tasks,our work offers a new perspective on enhancing model interpretability and *** codes of this work are available openly at https://***/general-learner/UQ_Anti_UAV(acceessed on 11 November 2024).

关键词： Object segmentation uncertainty quantification bayesian convolutional neural network

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