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In this paper, we forecasted the ionospheric total electron content (TEC) over the region of China using the bidirectional convolutional gated recurrent unit (BiConvGRU) model. We first generated the China Regional Ionospheric Maps (CRIMs) using GNSS observations provide by the Crustal Movement Observation Network of China (CMONOC). We then used gridded TEC maps from 2015 to 2018 with a 1 h interval from the CRIMs as the dataset, including quiet periods and storm periods of ionospheric TEC. The BiConvGRU model was then utilized to forecast the ionospheric TEC across China for the year 2018. The forecasted TEC was compared with the TEC from the International Reference Ionosphere (IRI-2016), Convolutional Long Short-Term Memory (ConvLSTM), Convolutional Gated Recurrent Unit (ConvGRU), Bidirectional Convolutional Long Short-Term Memory (BiConvLSTM), and the 1-day Predicted Global Ionospheric Map (C1PG) provided by the Center for Orbit Determination in Europe (CODE). In addition, indices including Kp, ap, Dst and F10.7 were added to the training dataset to improve the forecasting accuracy of the model (-A indicates no indices, while -B indicates with indices). The results verified that the prediction accuracies of the models integrated with these indices were significantly improved, especially during geomagnetic storms. The BiConvGRU-B model presented a decrease of 41.5%, 22.3%, and 13.2% in the root mean square error (RMSE) compared to the IRI-2016, ConvGRU, and BiConvLSTM-B models during geomagnetic storm days. Furthermore, at a specific grid point, the BiConvGRU-B model showed a decrease of 42.6%, 49.1%, and 31.9% in RMSE during geomagnetic quiet days and 30.6%, 34.1%, and 15.1% during geomagnetic storm days compared to the IRI-2016, C1PG, and BiConvLSTM-B models, respectively. In the cumulative percentage analysis, the BiConvGRU-B model had a significantly higher percentage of mean absolute error (MAE) within the range of 0–1 TECU in all seasons compared to the BiConvLSTM-B model. Meanwhile, the BiConvGRU-B model outperformed the BiConvLSTM-B model with lower RMSE for each month of 2018. [ABSTRACT FROM AUTHOR]

The electrification of transport has proved to be a breakthrough to uplift the sustainable and eco-friendly platform in the global sector in which electric vehicles (EVs) are considered indispensable. In particular, creating intelligent energy management in the power distribution system integrated with electric vehicle charging stations (EVCS) as a new entity is one of the most important challenging tasks. The implementation of the EVCS network infrastructure should facilitate the adoption of the spatiotemporal electricity demand for EVs. The intelligent decision for the transmission, distribution, energy allocation and charging station placement by the control center or central aggregator is only possible by correctly forecasting its usage, occupancy, and energy or charging demand. Techniques like data analytics have enabled to extract data from the EVCS on a daily basis to store and process all the recorded data. To overcome the above-mentioned challenges related to energy demand forecasting for EVCS network, this work proposes two encoder-decoder models based on convolutional long short-term memory networks (ConvLSTM) and bidirectional ConvLSTM (BiConvLSTM) in combination with the standard long short-term memory (LSTM) network. Data on energy demand from EVCS located in four different cities is used in the proposed models. All datasets are preprocessed to make them suitable for the multi-step time-series learning models in order to make the framework data-centric. The suggested architectures are built on the ConvLSTM and BiConvLSTM to extract the key features from the spatiotemporal data of the energy demand data of the EVCS distributed over the time and space. The predicted outcomes generated by the suggested strategy are compared with conventional deep learning models and traditional machine learning techniques.

In this paper, we forecasted the ionospheric total electron content (TEC) over the region of China using the bidirectional convolutional gated recurrent unit (BiConvGRU) model. We first generated the China Regional Ionospheric Maps (CRIMs) using GNSS observations provide by the Crustal Movement Observation Network of China (CMONOC). We then used gridded TEC maps from 2015 to 2018 with a 1 h interval from the CRIMs as the dataset, including quiet periods and storm periods of ionospheric TEC. The BiConvGRU model was then utilized to forecast the ionospheric TEC across China for the year 2018. The forecasted TEC was compared with the TEC from the International Reference Ionosphere (IRI-2016), Convolutional Long Short-Term Memory (ConvLSTM), Convolutional Gated Recurrent Unit (ConvGRU), Bidirectional Convolutional Long Short-Term Memory (BiConvLSTM), and the 1-day Predicted Global Ionospheric Map (C1PG) provided by the Center for Orbit Determination in Europe (CODE). In addition, indices including Kp, ap, Dst and F10.7 were added to the training dataset to improve the forecasting accuracy of the model (-A indicates no indices, while -B indicates with indices). The results verified that the prediction accuracies of the models integrated with these indices were significantly improved, especially during geomagnetic storms. The BiConvGRU-B model presented a decrease of 41.5%, 22.3%, and 13.2% in the root mean square error (RMSE) compared to the IRI-2016, ConvGRU, and BiConvLSTM-B models during geomagnetic storm days. Furthermore, at a specific grid point, the BiConvGRU-B model showed a decrease of 42.6%, 49.1%, and 31.9% in RMSE during geomagnetic quiet days and 30.6%, 34.1%, and 15.1% during geomagnetic storm days compared to the IRI-2016, C1PG, and BiConvLSTM-B models, respectively. In the cumulative percentage analysis, the BiConvGRU-B model had a significantly higher percentage of mean absolute error (MAE) within the range of 0–1 TECU in all seasons compared to the BiConvLSTM-B model. Meanwhile, the BiConvGRU-B model outperformed the BiConvLSTM-B model with lower RMSE for each month of 2018.

Single-image super-resolution (SR) is an effective approach to enhance spatial resolution for numerous applications such as object detection and classification when the resolution of sensors is limited. Although deep convolutional neural networks (CNNs) proposed for this purpose in recent years have outperformed relatively shallow models, enormous parameters bring the risk of overfitting. In addition, due to the different scale of objects in images, the hierarchical features of deep CNN contain additional information for SR tasks, while most CNN models have not fully utilized these features. In this paper, we proposed a deep yet concise network to address these problems. Our network consists of two main structures: (1) recursive inference block based on dense connection reuse of local low-level features, and recursive learning is applied to control the model parameters while increasing the receptive fields; (2) a bidirectional convolutional LSTM (BiConvLSTM) layer is introduced to learn the correlations of features from each recursion and adaptively select the complementary information for the reconstruction layer. Experiments on multispectral satellite images, panchromatic satellite images, and nature high-resolution remote-sensing images showed that our proposed model outperformed state-of-the-art methods while utilizing fewer parameters, and ablation studies demonstrated the effectiveness of a BiConvLSTM layer for an image SR task.

• A novel early crop prediction system to produce crop maps early in the growing season. • Bidirectional ConvLSTM and Decoder end-to-end neural network modules were used. • Deep neural network shown to learn historical trends better than Markov Chain method. • The approach has potential to predict early crop type maps with acceptable accuracy. Reliable crop type maps are needed early in the growing season to retrieve crop type information from satellite imagery in order to produce in-season crop condition and yield outlooks. However, the inability to have early crop maps due to limited earth observations available during the beginning of the season is a major challenge for developing reliable satellite-based early warning systems. This paper introduces a novel crop type prediction modeling system based on deep Neural Networks (NN) to produce preseason crop type maps at the field scale resolution using historical crop maps. The architecture of this modeling system comprises of two end-to-end NN based modules that form an autoencoder configuration: a spatio-temporal Encoder, built off of the Bidirectional ConvLSTM network, and a Decoder, which can learn both spatial and temporal patterns necessary to accurately predict the crop sequences. To build this system, we used USDA-NASS historical Cropland Data Layer (CDL) data of Nebraska and trained nine different neural network models using a combination of three rotation cycles (3-, 4- and 5-year rotations) and three sets of historical CDL data with varying durations (2010–2016; 2006–2016; and 2002–2016). All trained models performed well when applied to predict 2017 land cover, and achieved maximum accuracy of 88%. However, the amount of training time taken to reach 88% accuracy varies for each model. Models with more temporal information were found to learn faster and achieve maximum accuracy quickly. Further, we compared the NN model with a Markov Chain (MC) based approach, a common approach used for crop pattern recognition, by applying them to predict 2018 and 2019 land cover maps using 2006–2016 CDL data. Results showed that the NN model outperformed the MC model with higher overall accuracy (0.77) and kappa coefficient (0.57) compared to that of the MC model (overall accuracy = 0.67 and kappa coefficient = 0.44). In summary, our novel system based on deep neural networks showed that it can learn complex spatial and temporal patterns from the historical land cover data and produce reasonable early crop maps, which can be used in satellite-based early warning systems. [ABSTRACT FROM AUTHOR]

Single-image super-resolution (SR) is an effective approach to enhance spatial resolution for numerous applications such as object detection and classification when the resolution of sensors is limited. Although deep convolutional neural networks (CNNs) proposed for this purpose in recent years have outperformed relatively shallow models, enormous parameters bring the risk of overfitting. In addition, due to the different scale of objects in images, the hierarchical features of deep CNN contain additional information for SR tasks, while most CNN models have not fully utilized these features. In this paper, we proposed a deep yet concise network to address these problems. Our network consists of two main structures: (1) recursive inference block based on dense connection reuse of local low-level features, and recursive learning is applied to control the model parameters while increasing the receptive fields; (2) a bidirectional convolutional LSTM (BiConvLSTM) layer is introduced to learn the correlations of features from each recursion and adaptively select the complementary information for the reconstruction layer. Experiments on multispectral satellite images, panchromatic satellite images, and nature high-resolution remote-sensing images showed that our proposed model outperformed state-of-the-art methods while utilizing fewer parameters, and ablation studies demonstrated the effectiveness of a BiConvLSTM layer for an image SR task. [ABSTRACT FROM AUTHOR]