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Deformation can effectively reflect the structural state of concrete dams and, thus, establishing na accurate concrete dam deformation prediction model is important for dam health monitoring and early warning strategies. To address the problem that the spatial coordinates introduced in the traditional multi-point deformation prediction model of dams not being able to accurately and efficiently reflect the spatial correlation of multiple-measuring points, a 2D-1D-CNN model is proposed which expresses the spatial correlation between each measuring point through spatial feature vectors, replacing the spatial coordinates in the traditional multi-point model. First, the spatial feature vector is extracted from the historical spatio-temporal panel series of deformation values of measuring points via a Two-Dimensional Convolutional Neural Network (2D-CNN); second, the vector is combined with the environmental impact factor of dam deformation to form the final input factor of fused spatial features; and, thirdly, this vector is combined with the environmental impact factors of dam deformation to form the final input factor of fused spatial features, and the non-linear linkage between the factors and the measured displacement values is constructed by the efficient feature processing capability of a One-Dimensional Convolutional Neural Network (1D-CNN) to obtain the prediction results. Finally, the actual monitoring data of a concrete dam in China are used as an example to verify the validity of the model. The results show that the proposed model outperforms the other models in most cases, respectively, which verifies the effectiveness of the proposed model in this paper. [ABSTRACT FROM AUTHOR]

In recent years, with the development of geological prospecting from shallow ore to deep and hidden ore, the difficulty of prospecting is increasing day by day, so the application of computer technology and new methods of geological and mineral exploration is paid more and more attention. The mining and prediction of geological prospecting information based on deep learning have become the frontier field of earth science. However, as a deep artificial intelligence algorithm, deep learning still has many problems to be solved in the big data mining and prediction of geological prospecting, such as the small number of training samples of geological and mineral images, the difficulty of building deep learning network models, and the universal applicability of deep learning models. In this paper, the training samples and convolutional neural network models suitable for geochemical element data mining are constructed to solve the above problems, and the model is successfully applied to the prediction research of gold, silver, lead and zinc polymetallic metallogenic areas in South China. Taking the Pangxidong research area in the west of Guangdong Province as an example, this paper carries out prospecting target prediction research based on a 1:50000 stream sediment survey original data. Firstly, the support vector machine (SVM) model and statistical method were used to determine the ore-related geochemical element assemblage. Secondly, the experimental data of geochemical elements were augmented and a dataset was established. Finally, ResNet-50 neural network model is used for data training and prediction research. The experimental results show that the areas numbered 9, 29, 38, 40, 95, 111, 114, 124, 144 have great metallogenic potential, and this method would be a promising tool for metallogenic prediction. By applying the ResNet-50 neural network in metallogenic prediction, it can provide a new idea for the future exploration of mineral resources. In order to verify the generality of the research method in this paper, we conducted experimental tests on the geochemical dataset of B area, another deposit research area in South China. The results show that 100% of the prediction area obtained by using the proposed method covers the known ore deposit area. This model also provides method support for further delineating the prospecting target area in study area B. [ABSTRACT FROM AUTHOR]

Power dispatching systems currently receive massive, complicated, and irregular monitoring alarms during their operation, which prevents the controllers from making accurate judgments on the alarm events that occur within a short period of time. In view of the current situation with the low efficiency of monitoring alarm information, this paper proposes a method based on natural language processing (NLP) and a hybrid model that combines long short-term memory (LSTM) and convolutional neural network (CNN) for the identification of grid monitoring alarm events. Firstly, the characteristics of the alarm information text were analyzed and induced and then preprocessed. Then, the monitoring alarm information was vectorized based on the Word2vec model. Finally, a monitoring alarm event identification model based on a combination of LSTM and CNN was established for the characteristics of the alarm information. The feasibility and effectiveness of the method in this paper were verified by comparison with multiple identification models. [ABSTRACT FROM AUTHOR]

Forest fires have a significant impact on terrestrial ecosystems, leading to harm to biodiversity and environment. To mitigate the ecological damage caused by forest fires, it was necessary to develop prediction models of fire risk. In this study, by evolving the optimal architecture and parameters using the particle swarm optimization (PSO) algorithm, a convolutional neural network (CNN) deep learning model was proposed to predict forest fire risk on a national scale. Utilizing fire data and fire risk factors from 2001 to 2020 in China, the PSO-CNN-based deep learning model (PSO-CNN) was utilized and tested. Compared to logistic regression, random forest, support vector machine, k-nearest neighbors, and CNN models, the PSO-CNN model exhibited superior performance with an accuracy of 82.2% and an AUC value of 0.92. These results clearly highlighted the effectiveness of the PSO-CNN model in enhancing the accuracy of forest fire prediction. Furthermore, the forest fire risk prediction level estimated by the proposed model on a national scale for the entire country was mostly consistent with actual fire data distribution, indicating its potential to be used as an important direction for deep learning in forest fire prediction research. [ABSTRACT FROM AUTHOR]

Winter jujube (Ziziphus jujuba Mill. cv. Dongzao) has been cultivated in China for a long time and has a richly abundant history, whose maturity grade determined different postharvest qualities. Traditional methods for identifying the fundamental quality of winter jujube are known to be time-consuming and labor-intensive, resulting in significant difficulties for winter jujube resource management. The applications of deep learning in this regard will help manufacturers and orchard workers quickly identify fundamental quality information. In our study, the best fundamental quality of winter jujube from the correlation between maturity and fundamental quality was determined by testing three simple physicochemical indexes: total soluble solids (TSS), total acid (TA) and puncture force of fruit at five maturity stages which classified by the color and appearance. The results showed that the fully red fruits (the 4th grade) had the optimal eating quality parameter. Additionally, five different maturity grades of winter jujube were photographed as datasets and used the ResNet-50 model and the iResNet-50 model for training. And the iResNet-50 model was improved to overlap double residuals in the first Main Stage, with an accuracy of 98.35%, a precision of 98.40%, a recall of 98.35%, and a F1 score of 98.36%, which provided an important basis for automatic fundamental quality detection of winter jujube. This study provided ideas for fundamental quality classification of winter jujube during harvesting, fundamental quality screening of winter jujube in assembly line production, and real-time monitoring of winter jujube during transportation and storage. [ABSTRACT FROM AUTHOR]

Simple Summary: Spinal metastases are a common occurrence, and many patients do not have a clear history of primary tumors when diagnosed with spinal metastases. Patients with cancer of unknown primary often undergo comprehensive and invasive diagnostic work-ups, which can be expensive and time-consuming. To narrow down the search for primary tumor sites in spinal metastases patients with cancer of unknown primary, an artificial intelligence model that can assess tumor origin on MRI may be beneficial. Our work builds upon the considerable interest in investigating the feasibility of using a ResNet-50 convolutional neural network model based on MRI in predicting primary tumor sites in spinal metastases. In this preliminary study, for the 5-class classification of spinal metastases originating from the lung, kidney, prostate, and mammary or thyroid glands, the AUC-ROC and top-1 accuracy of the ResNet-50 model were 0.77 and 52.97%, respectively. Therefore, we believe that the ResNet-50 CNN model for predicting primary tumor sites in spinal metastases using MRI has the potential to help prioritize examinations and treatments in cases of unknown primary for radiologists and oncologists. We aim to investigate the feasibility and evaluate the performance of a ResNet-50 convolutional neural network (CNN) based on magnetic resonance imaging (MRI) in predicting primary tumor sites in spinal metastases. Conventional sequences (T1-weighted, T2-weighted, and fat-suppressed T2-weighted sequences) MRIs of spinal metastases patients confirmed by pathology from August 2006 to August 2019 were retrospectively analyzed. Patients were partitioned into non-overlapping sets of 90% for training and 10% for testing. A deep learning model using ResNet-50 CNN was trained to classify primary tumor sites. Top-1 accuracy, precision, sensitivity, area under the curve for the receiver-operating characteristic (AUC-ROC), and F1 score were considered as the evaluation metrics. A total of 295 spinal metastases patients (mean age ± standard deviation, 59.9 years ± 10.9; 154 men) were evaluated. Included metastases originated from lung cancer (n = 142), kidney cancer (n = 50), mammary cancer (n = 41), thyroid cancer (n = 34), and prostate cancer (n = 28). For 5-class classification, AUC-ROC and top-1 accuracy were 0.77 and 52.97%, respectively. Additionally, AUC-ROC for different sequence subsets ranged between 0.70 (for T2-weighted) and 0.74 (for fat-suppressed T2-weighted). Our developed ResNet-50 CNN model for predicting primary tumor sites in spinal metastases at MRI has the potential to help prioritize the examinations and treatments in case of unknown primary for radiologists and oncologists. [ABSTRACT FROM AUTHOR]

How to acquire landslide disaster information quickly and accurately has become the focus and difficulty of disaster prevention and relief by remote sensing. Landslide disasters are generally featured by sudden occurrence, proposing high demand for emergency data acquisition. The low-altitude Unmanned Aerial Vehicle (UAV) remote sensing technology is widely applied to acquire landslide disaster data, due to its convenience, high efficiency, and ability to fly at low altitude under cloud. However, the spectrum information of UAV images is generally deficient and manual interpretation is difficult for meeting the need of quick acquisition of emergency data. Based on this, UAV images of high-occurrence areas of landslide disaster in Wenchuan County and Baoxing County in Sichuan Province, China were selected for research in the paper. Firstly, the acquired UAV images were pre-processed to generate orthoimages. Subsequently, multi-resolution segmentation was carried out to obtain image objects, and the barycenter of each object was calculated to generate a landslide sample database (including positive and negative samples) for deep learning. Next, four landslide feature models of deep learning and transfer learning, namely Histograms of Oriented Gradients (HOG), Bag of Visual Word (BOVW), Convolutional Neural Network (CNN), and Transfer Learning (TL) were compared, and it was found that the TL model possesses the best feature extraction effect, so a landslide extraction method based on the TL model and object-oriented image analysis (TLOEL) was proposed; finally, the TLOEL method was compared with the object-oriented nearest neighbor classification (NNC) method. The research results show that the accuracy of the TLOEL method is higher than the NNC method, which can not only achieve the edge extraction of large landslides, but also detect and extract middle and small landslides accurately that are scatteredly distributed. [ABSTRACT FROM AUTHOR]

Having a high-quality historical air pollutant dataset is critical for environmental and epidemiological research. In this study, a novel deep learning model based on convolutional neural network architecture was developed to estimate ground-level ozone concentrations across eastern China. A high-resolution maximum daily average 8-h (MDA8) surface ground ozone concentration dataset was generated with the support of the total ozone column from the satellite Tropospheric Monitoring Instrument, meteorological data from the China Meteorological Administration Land Data Assimilation System, and simulations of the WRF-Chem model. The modeled results were compared with in situ measurements in five cities that were not involved in model training, and the mean R2 of predicted ozone with observed values was 0.9, indicating the good robustness of our model. In addition, we compared the model results with some widely used machine learning techniques (e.g., random forest) and recently published ozone datasets, showing that the accuracy of our model is higher and that the spatial distributions of predicted ozone are more coherent. This study provides an efficient and exact method to estimate ground-level ozone and offers a new perspective for modeling spatiotemporal air pollutants. [ABSTRACT FROM AUTHOR]

Heavy rain associated with landfalling typhoons often leads to disasters in South China, which can be reduced by improving the accuracy of radar quantitative precipitation estimation (QPE). At present, raindrop size distribution (DSD)-based nonlinear fitting (QPEDSD) and traditional neural networks are the main radar QPE algorithms. The former is not sufficient to represent the spatiotemporal variability of DSDs through the generalized Z–R or polarimetric radar rainfall relations that are established using statistical methods since such parametric methods do not consider the spatial distribution of radar observables, and the latter is limited by the number of network layers and availability of data for training the model. In this paper, we propose an alternative approach to dual-polarization radar QPE based on deep learning (QPENet). Three datasets of "dual-polarization radar observations—surface rainfall (DPO—SR)" were constructed using radar observations and corresponding measurements from automatic weather stations (AWS) and used for QPENetV1, QPENetV2, and QPENetV3. In particular, 13 × 13, 25 × 25, and 41 × 41 radar range bins surrounding each AWS location were used in constructing the datasets for QPENetV1, QPENetV2, and QPENetV3, respectively. For training the QPENet models, the radar data and AWS measurements from eleven landfalling typhoons in South China during 2017–2019 were used. For demonstration, an independent typhoon event was randomly selected (i.e., Merbok) to implement the three trained models to produce rainfall estimates. The evaluation results and comparison with traditional QPEDSD algorithms show that the QPENet model has a better performance than the traditional parametric relations. Only when the hourly rainfall intensity is less than 5 mm (R < 5 mm·h−1), the QPEDSD model shows a comparable performance to QPENet. Comparing the three versions of the QPENet model, QPENetV2 has the best overall performance. Only when the hourly rainfall intensity is less than 5 mm (R < 5 mm·h−1), QPENetV3 performs the best. [ABSTRACT FROM AUTHOR]

Predictive modelling of mineral prospectivity, a critical, but challenging procedure for delineation of undiscovered prospective targets in mineral exploration, has been spurred by recent advancements of spatial modelling techniques and machine learning algorithms. In this study, a set of machine learning methods, including random forest (RF), support vector machine (SVM), artificial neural network (ANN), and a deep learning convolutional neural network (CNN), were employed to conduct a data-driven W prospectivity modelling of the southern Jiangxi Province, China. A total of 118 known W occurrences derived from long-term exploration of this brownfield area and eight evidential layers of multi-source geoscience information related to W mineralization constituted the input datasets. This provided a data-rich foundation for training machine learning models. The optimal configuration of model parameters was trained by a grid search procedure and validated by 10-fold cross-validation. The resulting predictive models were comprehensively assessed by a confusion matrix, receiver operating characteristic curve, and success-rate curve. The modelling results indicate that the CNN model achieves the best classification performance with an accuracy of 92.38%, followed by the RF model (87.62%). In contrast, the RF model outperforms the rest of ML models in overall predictive performance and predictive efficiency. This is characterized by the highest value of area under the curve and the steepest slope of success-rate curve. The RF model was chosen as the optimal model for mineral prospectivity in this region as it is the best predictor. The prospective zones delineated by the prospectivity map occupy 9% of the study area and capture 66.95% of the known mineral occurrences. The geological interpretation of the model reveals that previously neglected Mn anomalies are significant indicators. This implies that enrichment of ore-forming material in the host rocks may play an important role in the formation process of wolframite and can represent an innovative exploration criterion for further exploration in this area. [ABSTRACT FROM AUTHOR]

The accurate characterization of tree species distribution in forest areas can help significantly reduce uncertainties in the estimation of ecosystem parameters and forest resources. Deep learning algorithms have become a hot topic in recent years, but they have so far not been applied to tree species classification. In this study, one-dimensional convolutional neural network (Conv1D), a popular deep learning algorithm, was proposed to automatically identify tree species using OHS-1 hyperspectral images. Additionally, the random forest (RF) classifier was applied to compare to the algorithm of deep learning. Based on our experiments, we drew three main conclusions: First, the OHS-1 hyperspectral images used in this study have high spatial resolution (10 m), which reduces the influence of mixed pixel effect and greatly improves the classification accuracy. Second, limited by the amount of sample data, Conv1D-based classifier does not need too many layers to achieve high classification accuracy. In addition, the size of the convolution kernel has a great influence on the classification accuracy. Finally, the accuracy of Conv1D (85.04%) is higher than that of RF model (80.61%). Especially for broadleaf species with similar spectral characteristics, such as Manchurian walnut and aspen, the accuracy of Conv1D-based classifier is significantly higher than RF classifier (87.15% and 71.77%, respectively). Thus, the Conv1D-based deep learning framework combined with hyperspectral imagery can efficiently improve the accuracy of tree species classification and has great application prospects in the future. [ABSTRACT FROM AUTHOR]