Search

Acoustic tomography is a widely used non-contact method for visualizing temperature distribution. A temperature distribution reconstruction algorithm based on an improved sparse reconstruction model and multi-scale feature fusion network is proposed. First, the acoustic temperature measurement sparse reconstruction model is improved by combining the error function (ERF) and the iterative reweighting algorithm, and the alternating direction method of multipliers algorithm (ADMM) is used to solve the model to obtain the initial temperature distribution. Then the feature extraction network is constructed to extract multi-scale features of acoustic time of flight (TOF) as prior information. Finally, the feature fusion reconstruction network is constructed to fuse and reconstruct the initial temperature distribution and multi-scale features to obtain a high-precision temperature distribution. Simulation and experimental tests were conducted respectively, and compared with other algorithms. The results show that the average relative error and root mean square error of the simulated temperature distribution reconstruction are 0.073% and 0.1% respectively, the average reconstruction error of the temperature points set in the experimental test is 0.38%, and the reconstruction errors are lower than other algorithms. The proposed method effectively utilizes prior information to correct sparse reconstruction temperature distribution reconstruction results, significantly improving the quality of temperature distribution reconstruction. [Display omitted] • Acoustic tomography technology is used for temperature distribution reconstruction. • The improved sparse reconstruction model can improve the accuracy of initial temperature distribution reconstruction. • The feature extraction network can perform multi-scale feature extraction on the time-of-flight of acoustic waves. • The feature fusion reconstruction network can improve the utilization of prior information. [ABSTRACT FROM AUTHOR]

Acoustic tomography is a widely used non-contact method for visualizing temperature distribution. A temperature distribution reconstruction algorithm based on an improved sparse reconstruction model and multi-scale feature fusion network is proposed. First, the acoustic temperature measurement sparse reconstruction model is improved by combining the error function (ERF) and the iterative reweighting algorithm, and the alternating direction method of multipliers algorithm (ADMM) is used to solve the model to obtain the initial temperature distribution. Then the feature extraction network is constructed to extract multi-scale features of acoustic time of flight (TOF) as prior information. Finally, the feature fusion reconstruction network is constructed to fuse and reconstruct the initial temperature distribution and multi-scale features to obtain a high-precision temperature distribution. Simulation and experimental tests were conducted respectively, and compared with other algorithms. The results show that the average relative error and root mean square error of the simulated temperature distribution reconstruction are 0.073% and 0.1% respectively, the average reconstruction error of the temperature points set in the experimental test is 0.38%, and the reconstruction errors are lower than other algorithms. The proposed method effectively utilizes prior information to correct sparse reconstruction temperature distribution reconstruction results, significantly improving the quality of temperature distribution reconstruction. [Display omitted] • Acoustic tomography technology is used for temperature distribution reconstruction. • The improved sparse reconstruction model can improve the accuracy of initial temperature distribution reconstruction. • The feature extraction network can perform multi-scale feature extraction on the time-of-flight of acoustic waves. • The feature fusion reconstruction network can improve the utilization of prior information. [ABSTRACT FROM AUTHOR]

Accurate landslide extraction is significant for landslide disaster prevention and control. Remote sensing images have been widely used in landslide investigation, and landslide extraction methods based on deep learning combined with remote sensing images (such as U-Net) have received a lot of attention. However, because of the variable shape and texture features of landslides in remote sensing images, the rich spectral features, and the complexity of their surrounding features, landslide extraction using U-Net can lead to problems such as false detection and missed detection. Therefore, this study introduces the channel attention mechanism called the squeeze-and-excitation network (SENet) in the feature fusion part of U-Net; the study also constructs an attention U-Net landside extraction model combining SENet and U-Net, and uses Sentinel-2A remote sensing images for model training and validation. The extraction results are evaluated through different evaluation metrics and compared with those of two models: U-Net and U-Net Backbone (U-Net Without Skip Connection). The results show that proposed the model can effectively extract landslides based on Sentinel-2A remote sensing images with an F1 value of 87.94%, which is about 2% and 3% higher than U-Net and U-Net Backbone, respectively, with less false detection and more accurate extraction results. [ABSTRACT FROM AUTHOR]

Accurate rainfall measurement with a precise spatial and temporal resolution is essential for making informed decisions during disasters and conducting scientific studies, particularly in regions characterized by intricate terrain and limited coverage of automated weather stations. Retrieval of precipitation with satellite is currently the most effective means to obtain precipitation over large‐scale areas. The key to enhancing the accuracy of precipitation estimation and forecasting in regions with complex terrain lies in effectively integrating satellite data with topographic information. This paper introduces a deep learning approach called AttUnet_R_SFT that utilizes high temporal, spatial, and spectral resolution data obtained from the Fengyun 4A satellite, and incorporates the Deep Spatial Feature Transform (SFT) layer to incorporate geographical data for estimating half‐hourly precipitation in northeastern China. We assess it by compared to operational near‐real‐time satellite precipitation products demonstrated to be successful in estimating precipitation and baseline deep learning models. According to the experimental findings, the AttUnet_R_SFT model outperforms practical precipitation products and baseline deep learning models in both identifying and estimating precipitation. The main enhancement of the model performance is shown in the windward slope of the Greater Khingan Mountains as a result of the successful incorporation of geographical data. Hence, the suggested framework holds the capability to function as a superior and dependable satellite‐derived precipitation estimation solution in regions characterized by intricate terrain and infrequent rainfall. The findings of this study indicate that the utilization of deep learning algorithms for satellite precipitation estimation shows potential as a fruitful avenue for further research. Plain Language Summary: A deep learning model named AttUnet_R_SFT is proposed, which use high temporal, spatial and spectral resolution data from the Fengyun 4A satellite, and combines with the Deep Spatial Feature Transform (SFT) layer to input geographic information for half‐hourly precipitation estimation in the complex terrain region represented by northeast China. The model can provide a reference for improving the performance of precipitation estimation in areas with complex topography. Key Points: A deep‐learning model is proposed to effectively fuse satellite multispectral data of the Fengyun 4A satellite, with topographic informationHalf‐hourly precipitation is estimated with higher temporal resolution, which is closer to the operational needs of weather forecastingAs precipitation in the study area is a non‐high‐frequency event, data enhancement is attempted to use and obtain effective results [ABSTRACT FROM AUTHOR]

Accurate and timely precipitation forecasts are critical in modern society, influencing both economic activity and daily life. While deep learning methods leveraging remotely sensed radar data have become prevalent for precipitation nowcasting, longer-term forecasting remains challenging. This is due to accumulated errors in deep learning models and insufficient information about precipitation systems over longer time horizons. To address these challenges, we introduce the Short-Term Precipitation Forecast Network (STPF-Net), a recurrent neural network designed for longer-term precipitation prediction. STPF-Net uses a multi-tier structure with varying temporal resolutions to mitigate the accumulated errors during longer forecasts. Additionally, its transformer-based module incorporates larger spatial contexts, providing more complete information about precipitation systems. We evaluated STPF-Net on radar data from southeastern China, training separate models for 6 and 12 h forecasts. Quantitative results demonstrate STPF-Net achieved superior accuracy and lower errors compared to benchmark deep learning and numerical weather prediction models. Visualized case studies indicate reasonably coherent 6 h predictions from STPF-Net versus other methods. For 12 h forecasts, while STPF-Net outperformed other models, it still struggled with storm initiation over longer forecasting time. [ABSTRACT FROM AUTHOR]

Deep learning models based on radar echo extrapolation have been widely used in precipitation nowcasting. However, they face the challenge of insufficient input information when extending the forecast lead time, requiring the incorporation of physics‐based numerical weather prediction (NWP). Given that the strengths of radar and NWP data vary depending on the forecast time, effectively fusing these two data sources in a unified deep learning model remains an open research problem. In this study, we propose a Time‐aware Adaptive Feature Fusion Network (TAFFNet) for very short‐term precipitation forecasts up to 12 hr. TAFFNet fuses features adaptively according to their relative contributions to forecast skill at different times. Experimental results demonstrate that TAFFNet performs the best for very short‐term precipitation forecasts. The case studies show that adaptively fusing NWP with radar can improve the accuracy of precipitation forecasts, especially for predicting the initiation and dissipation of storms at longer lead times. Plain Language Summary: Very short‐term precipitation forecasting aims to predict precipitation for a couple of hours, which is crucial for public safety and emergency management in cities. Recently, deep learning models have been widely used for precipitation nowcasting (0–2 hr) based on observation extrapolation. However, as the forecast time increases, extrapolating historical observations alone is inadequate without incorporating physical constraints. On the contrary, traditional NWP can provide essential physical background, but the prediction accuracy still needs improvement for early forecasting periods. In this study, we proposed a novel deep learning model named TAFFNet to combine both advantages of radar and NWP for very short‐term precipitation forecasts up to 12 hr. The features of radar and NWP are first extracted, respectively. Their features are fused adaptively in the forecasting part through a time‐aware adaptive feature fusion (TAFF) module, according to their relative contributions to forecast skill at different lead times. The feature fusion approach can be optimized adaptively through model training. The proposed model is evaluated on the radar and NWP model data set covering southeast China. Results demonstrate that incorporating NWP into a radar‐based deep learning model can improve very short‐term precipitation forecasts. TAFFNet can better capture the storm's initiation and dissipation during forecasting. Key Points: A novel deep learning model termed Time‐aware Adaptive Feature Fusion Network (TAFFNet) is proposed for very short‐term precipitation forecasts up to 12 hrFeatures from radar and numerical weather prediction are fused adaptively in deep levels based on their relative contributions to forecast at different lead timesTAFFNet can improve very short‐term precipitation forecasts, especially for predicting storms initiation and dissipation at longer times [ABSTRACT FROM AUTHOR]

• MLP, GRU, CNN and MSCNN for landslide susceptibility mapping were compared. • CNN combined with multi-scale technique can improve feature utilization. • The joint evaluation method of ROC curve and PR curve for LSM was proposed. Landslide susceptibility mapping (LSM) can be used to determine the spatial probability of landslide occurrence. There are many methods for LSM, including statistical methods, traditional machine learning methods and deep learning methods, etc. However, the difference comparison of these methods has been not perfect, especially the comparison of different neural network models for LSM and their application prospects were rarely studied. In this paper, the classical neural net-work multi-layer perceptron (MLP), convolutional neural network (CNN), gated recurrent unit (GRU) and multi-scale convolutional neural network (MSCNN) four models are selected for comparison. Taking Lanzhou city, Gansu Province, China as an example, eight landslide-related influencing factors and historical landslide and non-landslide locations were selected, and the training set and validation set were divided according to 7:3. Through training the four models, four landslide susceptibility maps were generated. The experimental results were verified and compared by the confusion matrix, Kappa coefficient, F1-score and other statistical indicators. The receiver operating characteristic (ROC) curve and Precision-Recall (PR) curve were plotted to evaluate the classification effect and generalization capability of four models. The results show that the constructed MSCNN is the optimal model, which has the best performance both in the training process and in the mapping results. MSCNN model has the highest value of Recall (99.93%), Kappa (0.96) and F1-score (0.98) in the confusion matrix. In addition, ROC curve and PR curve of MSCNN model maintain the maximum area under curve (AUC) on different data sets. In the comparison, MLP and GRU accept sequence features, while CNN and MSCNN accept neighborhood features. In general, the prediction model considering neighborhood features contains more information in the limited input data and is better than the prediction model considering sequence features in all evaluation indicators. Therefore, we think that the neighborhood features can better represent the landslide occurrence characteristics. In the future model design process for LSM, more attention should be paid to the neighborhood features of landslide influencing factors. [ABSTRACT FROM AUTHOR]

The spatial resolution of precipitation predicted by general circulation models is too coarse to meet current research and operational needs. Downscaling is one way to provide finer resolution data at local scales. The single-image super-resolution method in the computer vision field has made great strides lately and has been applied in various fields. In this article, we propose a novel reference-based and gradient-guided deep learning model (RBGGM) to downscale daily precipitation considering the discontinuity of precipitation and ill-posed nature of downscaling. Global Precipitation Measurement Mission (GPM) precipitation data, variables in ERA5 re-analysis data, and topographic data are selected to perform the downscaling, and a residual dense attention block is constructed to extract features of them. By exploring the discontinuous feature of precipitation, we introduce gradient feature to reconstruct precipitation distribution. We also extract the feature of high-resolution monthly precipitation as a reference feature to resolve the ill-posed nature of downscaling. Extensive experimental results on benchmark data sets demonstrate that our proposed model performs better than other baseline methods. Furthermore, we construct a daily precipitation downscaling data set based on GPM precipitation data, ERA5 re-analysis data and topographic data. [ABSTRACT FROM AUTHOR]