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Landslide Risk Prediction Model Using an Attention-based Temporal Convolutional Network Connected to a Recurrent Neural Network
2022
IEEE Access
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In the second model, the encoder uses a neural network with an RNN architecture, and the decoder uses a TCN. ...
The encoder in the first model uses the temporal convolutional network (TCN), and the decoder uses a neural network with an RNN architecture, including long shortterm memory (LSTM) networks, gated recurrent ...
a novel deep learning landslide risk prediction model. ...
doi:10.1109/access.2022.3165051
fatcat:ky2ay5wzsrasvesyroipomjy6y
GRU–Transformer: A Novel Hybrid Model for Predicting Soil Moisture Content in Root Zones
2024
Agronomy
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advantage in predicting soil moisture content with enhanced precision for a five-day forecast. ...
Remarkably, with a streamlined set of just six soil moisture content parameters, the model predicts an average MSE of 0.59% and an R2 of 98.86% for a three-day forecast, highlighting its resilience to ...
Conclusions This research introduces a novel hybrid modeling approach that synergizes GRU and Transformer architectures, aiming to predict soil moisture content at varying depths, specifically within the ...
doi:10.3390/agronomy14030432
fatcat:cxc2yo2i7vh7tmvfm2n2opfl7q
DenseResUNet: An Architecture to Assess Water-Stressed Sugarcane Crops from Sentinel-2 Satellite Imagery
2021
Traitement du signal
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Here an architecture 'DenseResUNet' is proposed for water-stressed sugarcane crops using segmentation based on encoder-decoder approach. ...
The layers of a dense block are residual modules with a dense connection. The proposed model achieved 61.91% mIoU, and 80.53% accuracy on segmenting the water-stressed sugarcane fields. ...
The road extraction networks are improved with the extraction of fine details of the roads by He et al. [21] applying the atrous spatial pyramid pool (ASPP) in the encoder-decoder structure. ...
doi:10.18280/ts.380424
fatcat:2esirp5ndjhdvh7oiknnjmguhq
DeepVeg: Deep Learning Model for Segmentation of Weed, Canola, and Canola Flea Beetle Damage
2021
IEEE Access
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We propose, DeepVeg, a deep learning segmentation model that focuses on the smallest (damage) class without affecting other classes to solve the class imbalance issue. ...
The model also shows robustness in detecting unlabelled, newly grown weeds and canola and is also able to distinguish the similar rounded structured canola plant and weed with small amounts of data for ...
In a broader context, the obtained results support the quantitative analysis and depict that the proposed model with pyramid pooling and residual blocks connected encoder decoder combined with weighted ...
doi:10.1109/access.2021.3108003
fatcat:4jpdjyo2zbfvfbizo3ius3uwxq
A Hybrid Method with Adaptive Sub-series Clustering and Attention-Based Stacked Residual LSTMs for Multivariate Time Series Forecasting
2020
IEEE Access
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Besides, a multi-level attention mechanism (MLAttn), which makes full use of the encoding information of the encoder, has been introduced to further improve the prediction performance of the model. ...
The model is based on the encoder-decoder architecture with stacked residual LSTMs as the encoder, which can effectively capture the dependencies among multi variables and the temporal features from multivariate ...
As shown in Figure 5 , the model is based on the encoder-decoder architecture, which uses stacked residual LSTMs (SRLSTMs) as the encoder and single-layer LSTM as the decoder. ...
doi:10.1109/access.2020.2981506
fatcat:xcbtnlcysjgevfcutchykieo4a
Three Dimensional Root CT Segmentation using Multi-Resolution Encoder-Decoder Networks
2020
IEEE Transactions on Image Processing
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While previous work in encoder-decoders implies the use of multiple resolutions simply by downsampling and upsampling images, we make this process explicit, with branches of the network tasked separately ...
We then further improve performance using an incremental learning approach, in which failures in the original network are used to generate harder negative training examples. ...
ACKNOWLEDGMENT The work reported here was funded by the US Dept of Energy via the ARPA-e ROOTS project Low Cost X-Ray CT System for in-situ Imaging of Roots. ...
doi:10.1109/tip.2020.2992893
pmid:32406835
fatcat:ivqqxptm4bfn5bkkj4b2iywffi
Research on the Corn Stover Image Segmentation Method via an Unmanned Aerial Vehicle (UAV) and Improved U-Net Network
2024
Agriculture
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The model utilizes transfer learning by replacing the encoder with the first five layers of the VGG19 network to extract essential features from stalk images. ...
This method combines semantic segmentation principles with image detection techniques to form an encoder–decoder network structure. ...
Both Our algorithm and ResNet use the migration learning method to encoder part of U-Net with improved VGG19 and ResNet50, respectively, and models are encoder-decoder structures. ...
doi:10.3390/agriculture14020217
fatcat:kc6af56yingfvh5j22qs5mg4ju
Learning to Remove Clutter in Real-World GPR Images Using Hybrid Data
[article]
2022
arXiv
pre-print
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To tackle the challenge of clutter removal in real scenarios, a clutter-removal neural network (CR-Net) trained on a large-scale hybrid dataset is presented in this study. ...
The CLT-GPR dataset significantly improves the generalizability of the network to remove clutter in real-world GPR radargrams. ...
It is composed of an encoder, a decoder, and skip connections between them. ...
arXiv:2205.08135v1
fatcat:neraqrxxvnc73pu4xo4dqkuqsm
Deep Learning Based Burnt Area Mapping Using Sentinel 1 for the Santa Cruz Mountains Lightning Complex (CZU) and Creek Fires 2020
2022
Fire
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of images) combined with ResNet50 (Residual Networks used as a backbone for many computer vision tasks) encoder architecture used with SAR, Digital Elevation Model, and land cover data for burnt area ...
The results showed a maximum burnt area segmentation F1-Score of 0.671 in the CZU, which outperforms current models estimating burnt area with SAR data for the specific event studied models in the literature ...
., 2015 [28] propose a novel encoder-decoder framework applying some of the similar principles. ...
doi:10.3390/fire5050163
fatcat:mt2ggsb4kjdxlbhwpyrrghicly
Deep learning-based multi-spectral satellite image segmentation for water body detection
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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In this paper, we propose a novel deep convolutional neural network model -Multi-Channel Water Body Detection Network (MC-WBDN) -that incorporates three innovative components, a multi-channel fusion module ...
bodies compared to other DCNN models. ...
We train each deep learning model for a minimum of 100 and a maximum of 300 epochs with an early-stopping mechanism that terminates learning when performance on the validation set does not improve for ...
doi:10.1109/jstars.2021.3098678
fatcat:aafah7pmgjhkpepq7hdc7o34zm
Towards hybrid modeling of the global hydrological cycle
2022
Hydrology and Earth System Sciences
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The neural-network-learned hydrological responses of evapotranspiration and grid cell runoff to antecedent soil moisture states are qualitatively consistent with our understanding and theory. ...
H2M identifies a somewhat stronger role of soil moisture for TWS variations in transitional and tropical regions compared to GHMs. ...
Due to the high dimensionality of the static variables, the data were compressed in a preprocessing step using a simple convolutional auto-encoder, consisting of an encoder, a bottleneck layer, and a decoder ...
doi:10.5194/hess-26-1579-2022
fatcat:itdo2ovgxnbgtksgdzovi36wbi
A Review of Machine Learning for Near-Infrared Spectroscopy
2022
Sensors
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The analysis of infrared spectroscopy of substances is a non-invasive measurement technique that can be used in analytics. ...
Finally, we conclude that developing the integration of a variety of machine learning algorithms in an efficient and lightweight manner is a significant future research direction. ...
A single-layer autoencoder (AE) learns the encoder-decoder weights for each layer in the greedy layer-wise learning step before fine-tuning the SAE. ...
doi:10.3390/s22249764
pmid:36560133
pmcid:PMC9784128
fatcat:dyufzyqzkjhk3hxd4nprypch6a
Fast and scalable neuroevolution deep learning architecture search for multivariate anomaly detection
[article]
2022
arXiv
pre-print
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Other Versions
Neuroevolution is one of the methodologies that can be used for learning optimal architecture during training. ...
The presented framework can be used as an efficient learning network architecture method for any different unsupervised task where autoencoder architectures can be used. ...
The decoder is a mirror image of the encoder with tanh activation. The resulting ensemble model outperforms many other deep learning architectures. ...
arXiv:2112.05640v6
fatcat:ylhpba7jebhv5bswht5mixp36e
2021 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 14
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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., +, JSTARS 2021 4595-4606 Ensemble Encoder-Decoder Models for Predicting Land Transformation. ...
., +, JSTARS 2021 12514-12523 Ensemble Encoder-Decoder Models for Predicting Land Transformation. ...
., A Saturated ...
doi:10.1109/jstars.2022.3143012
fatcat:dnetkulbyvdyne7zxlblmek2qy
Graph Networks with Physics-aware Knowledge Informed in Latent Space
2021
AAAI Spring Symposia
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We demonstrate that climate prediction tasks are significantly improved and validate the effectiveness and importance of the proposed model. ...
In this work, we present a novel architecture to incorporate physics or domain knowledge given as a form of partial differential equations (PDEs) on sparse observations by utilizing graph structure. ...
While the accuracy and efficiency of datadriven deep learning models can be improved with ad-hoc architectural changes for specific tasks, we are confronted with many challenging learning scenarios in ...
dblp:conf/aaaiss/SeoL21
fatcat:wsvpozcxnnca3dv7djmzuhm4ia
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