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Analyzing and investigating the impact of implementing an environmental policy mix on carbon emission from private cars and social welfare holds significant reference value. Firstly, based on vehicle trajectory big data, this paper employs reverse geocoding and artificial neural network models to predict carbon emissions from private cars in various provinces and cities in China. Secondly, by simulating different scenarios of carbon tax, carbon trading, and their policy mix, the propensity score matching model is constructed to explore the effects of the policy mix on carbon emission reduction from private cars and social welfare while conducting regional heterogeneity analysis. Finally, policy proposals are proposed to promote carbon emission reduction from private cars and enhance social welfare in China. The results indicate that the environmental policy mix has a significant positive impact on carbon emission reduction from private cars and social welfare. Furthermore, in the regional heterogeneity analysis, the implementation of the policy mix in eastern regions has a significant positive effect on both carbon emission reduction from private cars and social welfare, while in central and western regions, it shows a significant positive impact on social welfare but has no significant effect on carbon emission reduction in the private car sector. [ABSTRACT FROM AUTHOR]

The goal of reaching the peak of carbon in the construction industry is urgent. However, the research on the feasibility of realizing this goal and the implementation of relevant policies in China is relatively superficial. In view of the historical data of energy consumption and building CO2 emission from 1995 to 2019, this paper establishes a BP neural network model for predicting building CO2 emissions. Moreover, the influencing factors, such as population, GDP, and total construction output, are introduced as the parameters in the model. Through the scenario analysis method explores the practical path to accomplish the peak of building CO2 emissions. When using traditional prediction methods to predict building carbon emissions, the long prediction cycle will increase the possibility of significant errors. Therefore, this paper constructs the calculation model of building carbon emission and forecasts the future carbon emission value through the BP neural network to avoid the error caused by the nonlinear relationship between influencing factors and predicted value. It will effectively predict the feasibility of the carbon peak and the carbon-neutral target set by government, and provide a useful predictive tool for adjusting the new energy structure and formulating related emission reduction policies. [ABSTRACT FROM AUTHOR]

The classification that distinguishes whether machines are driving on roads or working in fields based on their global navigation satellite system (GNSS) trajectories is essential for effective management of cross-regional agricultural machinery services in China. In this paper, a novel field–road classification method utilizing multiple deep neural networks (MultiDNN) is proposed to enhance the accuracy of field and road point classification. The MultiDNN model incorporates a bi-directional long short-term memory network (BiLSTM), a topology adaptive graph convolution network (TAG), and a self-attention network (ATT) to effectively extract spatio-temporal features for field–road classification. The BiLSTM is used to capture temporal relationships along the time axis of a trajectory, providing global contextual information for each point. Then, the TAG network is used to obtain the spatio-temporal relationships between adjacent points in a trajectory, offering local contextual information for each point. Finally, the ATT network assigns varying weights to features to emphasize important characteristics. The performance of the MultiDNN model was evaluated using a wheat harvesting trajectory dataset, and the results showed that it achieved a high degree of accuracy, up to 89.75%, outperforming the best baseline method (GCN) by 2.79%. [ABSTRACT FROM AUTHOR]

Soil salinization has become one of the major environmental problems threatening food security. The identification and knowledge of the spatial distributions of soil salinization are key in addressing this problem. This study assumes that a good saline land identification effect can be obtained with the help of deep learning methods. Therefore, this study used the OLI sensor data from the Landsat-8, based on the U2-Network, and proposes a method to extract saline land from remote sensing images. The study also adds different salinity indices (SI, SI1, and SI2) to explore its impact on classification accuracy. Through our method, accurate saline soil distribution information were obtained, and several verification indicators (the Intersection-over-Union (IoU), recall, precision, and F1-score) were all measured above 0.8. In addition, compared with the multi-spectral training results, the classification accuracy increased after adding a specific salinity index, and most of the accuracy indices increased by about 2% (the IoU increased by 3.70%, recall increased by 1.50%, precision increased by 2.81%, and F1-score increased by 2.13%). In addition, we also included a case study based on our methodology to analyze the distribution characteristics and changes of saline soil in the Zhenlai area of Northeast China from 2016 to 2020. We found that the area of saline land in the Zhenlai area has reduced, which shows that the extraction method proposed in this study is feasible. Overall, this paper indicates that deep learning-based methods can efficiently extract the salinity of soil and enhance the mapping of its spatial distribution. The study has the broad impact of supplementing satellite imagery for salinity modeling and helping to guide agricultural land management practices for northeastern China and other salinized regions. [ABSTRACT FROM AUTHOR]

Northern China is vigorously promoting cogeneration and clean heating technologies. The accurate prediction of building energy consumption is the basis for heating regulation. In this paper, the daily, weekly, and annual periods of building energy consumption are determined by Fourier transformation. Accordingly, a period-based neural network (PBNN) is proposed to predict building energy consumption. The main innovation of PBNN is the introduction of a new data structure, which is a time-discontinuous sliding window. The sliding window consists of the past 24 h, 24 h for the same period last week, and 24 h for the same period the previous year. When predicting the building energy consumption for the next 1 h, 12 h, and 24 h, the prediction errors of the PBNN are 2.30%, 3.47%, and 3.66% lower than those of the traditional sliding window PBNN (TSW-PBNN), respectively. The training time of PBNN is approximately half that of TSW-PBNN. The time-discontinuous sliding window reduces the energy consumption prediction error and neural network model training time. [ABSTRACT FROM AUTHOR]

The unidirectional consolidation theory of soils is widely used in certain conditions and approximate calculations. The multidirectional theory of soil consolidation is more reasonable than the unidirectional theory in practical applications but is much more complicated in terms of index determination and solution. To address the above problem, in this paper, we propose a deep learning method using physics-informed neural networks (PINN) to predict the excess pore water pressure of two-dimensional soil consolidation. In the proposed method, (1) a fully connected neural network is constructed; (2) the computational domain, partial differential equation (PDE), and constraints are defined to generate data for model training; and (3) the PDE of two-dimensional soil consolidation and the model of the neural network are connected to reduce the loss of the model. The effectiveness of the proposed method is verified by comparison with the numerical solution of PDE for two-dimensional consolidation. Moreover, the FEM and the proposed PINN-based method are applied to predict the consolidation of foundation soils in a real case of Sichuan Railway in China, and the results are quite consistent. The proposed deep learning approach can be used to investigate large and complex multidirectional soil consolidation. [ABSTRACT FROM AUTHOR]

Satellite-based precipitation products (SPPs) provide valuable precipitation information for various applications. Their performance, however, varies significantly from region to region due to various data sources and production processes. This paper aims to evaluate four selected SPPs (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Gauge-adjusted Global Satellite Mapping of Precipitation (GSMAP-gauge), and Global Precipitation Measurement (GPM)) over mainland China from 2016 to 2019. Both conventional statistical indicators (e.g., correlation coefficients (CC), root mean square error (RMSE), mean absolute error (MAE), relative bias (RB), and Nash–Sutcliffe efficiency (NSE)) and categorical indicators (probability of detection (POD), probability of true detection (POTD), false-alarm rate (FAR), and critical success index (CSI)) are used for quantitative analysis. The results show that: (1) GSMAP-gauge and GPM perform best in reproducing the spatial distribution pattern of precipitation over mainland China, whereas SPPs generally underestimate summer precipitation with a high frequency of no-rain cases. (2) MSWEP has the best capability for recording precipitation events, although some parts of northern China exhibit abnormal overestimations for winter precipitation. (3) All SPPs, especially the PERSIANN-CDR, significantly underestimate the precipitation in the mountainous areas of southwestern China. (4) The GSMAP-gauge and GPM outperformed the other two of the four SPPs, in terms of the probability density function of daily precipitation for cases (PDFc) and the probability density function of daily precipitation for volume (PDFv). Generally, PERSIANN-CDR shows the poorest performance when compared to the other three products. The product's algorithm for estimating heavy precipitation and mountainous precipitation needs further improvement. [ABSTRACT FROM AUTHOR]

Extraterrestrial solar radiation (ESR) is the essential basic background for solar radiation, which determines the occurrence of the weather and atmospheric phenomena. Since the influence of ESR variation on actual rugged terrain is a diverse, complex, and dynamic process, simulating ESR over a large spatial-temporal span, especially with a high-resolution digital elevation model (DEM), is a significant challenge. In this paper, we developed a new scheme for simulating ESR over the entirety of China using a DEM with a resolution of 30 m. To fully consider regional terrain status, the feature variables used were elevation, slope, and aspects of the located grid and the surrounding four grids to reveal the topography. In addition, latitude was used as a feature variable to consider the geographical location, and the month number was used to consider the duration. On the basis of different geographical locations, the training dataset was established from 20,000 grids. With the feature variable composition and training dataset, a backpropagation artificial neural network (BP ANN) was found to have the best performance compared with the other three machine learning methods in simulating ESR for a DEM. In terms of the proposed scheme and BP ANN, we drew an ESR map of China with a resolution of 30 m. The determination coefficient of the simulation result achieved 0.99 and the root-mean-square error was less than 50 MJ/m2 in all sample areas, confirming its remarkable accuracy. In terms of efficiency, the time consumption of ESR simulated using the proposed scheme shrinks over 150 times in all sample areas compared to that simulated via the theoretical model. Simultaneously, the developed scheme was also used to simulate an ESR for a DEM with a resolution of 90 m to verify the universality and robustness of the developed scheme. In addition, we used the proposed scheme to derive the direct solar radiation and global solar radiation, thereby further proving the reliability and applicability of our study. Overall, our work convincingly proved that the proposed scheme is a potential and effective approach for quickly simulating ESR with high accuracy. This study provides the basis for different solar radiation inversions of long time series and large spatial scales, offering additional insights for simulating ESR on a large spatial-temporal span. [ABSTRACT FROM AUTHOR]

With the economic growth and increasing urbanization in the last three decades, the air quality over China has continuously degraded, which poses a great threat to human health. The concentration of fine particulate matter (PM2.5) directly affects the mortality of people living in the polluted areas where air quality is poor. The Beijing-Tianjin-Hebei (BTH) region, one of the well organized urban regions in northern China, has suffered with poor air quality and atmospheric pollution due to recent growth of the industrial sector and vehicle emissions. In the present study, we used the back propagation neural network model approach to estimate the spatial distribution of PM2.5 concentration in the BTH region for the period January 2014-December 2016, combining the satellite-derived aerosol optical depth (S-DAOD) and meteorological data. The results were validated using the ground PM2.5 data. The general method including all PM2.5 training data and 10-fold cross-method have been used for validation for PM2.5 estimation (R2 = 0.68, RMSE = 20.99 for general validation; R2 = 0.54, RMSE = 24.13 for cross-method validation). The study provides a new approach to monitoring the distribution of PM2.5 concentration. The results discussed in the present paper will be of great help to government agencies in developing and implementing environmental conservation policy. [ABSTRACT FROM AUTHOR]

The UN 2030 Agenda sets poverty eradication as the primary goal of sustainable development. An accurate measurement of poverty is a critical input to the quality and efficiency of poverty alleviation in rural areas. However, poverty, as a geographical phenomenon, inevitably has a spatial correlation. Neglecting the spatial correlation between areas in poverty measurements will hamper efforts to improve the accuracy of poverty identification and to design policies in truly poor areas. To capture this spatial correlation, this paper proposes a new poverty measurement model based on a neural network, namely, the spatial vector deep neural network (SVDNN), which combines the spatial vector neural network model (SVNN) and the deep neural network (DNN). The SVNN was applied to measure spatial correlation, while the DNN used the SVNN output vector and explanatory variables dataset to measure the multidimensional poverty index (MPI). To determine the optimal spatial correlation structure of SVDNN, this paper compares the model performance of the spatial distance matrix, spatial adjacent matrix and spatial weighted adjacent matrix, selecting the optimal performing spatial distance matrix as the input data set of SVNN. Then, the SVDNN model was used for the MPI measurement of the Yangtze River Economic Belt, after which the results were compared with three baseline models of DNN, the back propagation neural network (BPNN), and artificial neural network (ANN). Experiments demonstrate that the SVDNN model can obtain spatial correlation from the spatial distance dataset between counties and its poverty identification accuracy is better than other baseline models. The spatio-temporal characteristics of MPI measured by SVDNN were also highly consistent with the distribution of urban aggregations and national-level poverty counties in the Yangtze River Economic Belt. The SVDNN model proposed in this paper could effectively improve the accuracy of poverty identification, thus reducing the misallocation of resources in tracking and targeting poverty in developing countries. [ABSTRACT FROM AUTHOR]

As one of the most devastating disasters to pine forests, pine wilt disease (PWD) has caused tremendous ecological and economic losses in China. An effective way to prevent large-scale PWD outbreaks is to detect and remove the damaged pine trees at the early stage of PWD infection. However, early infected pine trees do not show obvious changes in morphology or color in the visible wavelength range, making early detection of PWD tricky. Unmanned aerial vehicle (UAV)-based hyperspectral imagery (HI) has great potential for early detection of PWD. However, the commonly used methods, such as the two-dimensional convolutional neural network (2D-CNN), fail to simultaneously extract and fully utilize the spatial and spectral information, whereas the three-dimensional convolutional neural network (3D-CNN) is able to collect this information from raw hyperspectral data. In this paper, we applied the residual block to 3D-CNN and constructed a 3D-Res CNN model, the performance of which was then compared with that of 3D-CNN, 2D-CNN, and 2D-Res CNN in identifying PWD-infected pine trees from the hyperspectral images. The 3D-Res CNN model outperformed the other models, achieving an overall accuracy (OA) of 88.11% and an accuracy of 72.86% for detecting early infected pine trees (EIPs). Using only 20% of the training samples, the OA and EIP accuracy of 3D-Res CNN can still achieve 81.06% and 51.97%, which is superior to the state-of-the-art method in the early detection of PWD based on hyperspectral images. Collectively, 3D-Res CNN was more accurate and effective in early detection of PWD. In conclusion, 3D-Res CNN is proposed for early detection of PWD in this paper, making the prediction and control of PWD more accurate and effective. This model can also be applied to detect pine trees damaged by other diseases or insect pests in the forest. [ABSTRACT FROM AUTHOR]

For the power systems, for which few data are available for mid-term electricity market clearing price (MCP) forecasting at the early stage of market reform, a novel grey prediction model (defined as interval GM(0, N) model) is proposed in this paper. Over the traditional GM(0, N) model, three major improvements of the proposed model are: (i) the lower and upper bounds are firstly identified to give an interval estimation of the forecasting value; (ii) a novel whitenization method is then established to determine the definite forecasting value from the forecasting interval; and (iii) the model parameters are identified by an improved particle swarm optimization (PSO) instead of the least square method (LSM) for the limitation of LSM. Finally, a newly-reformed electricity market in Yunnan province of China is studied, and input variables are contrapuntally selected. The accuracy of the proposed model is validated by observed data. Compared with the multiple linear regression (MLR) model, the traditional GM(0, N) model and the artificial neural network (ANN) model, the proposed model gives a better performance and its superiority is further ensured by the use of the modified Diebold-Mariano (MDM) test, suggesting that it is suitable for mid-term electricity MCP forecasting in a data-sparse electricity market. [ABSTRACT FROM AUTHOR]

Dissolved gas analysis (DGA) based in insulating oil has become a more mature method in the field of transformer fault diagnosis. However, due to the complexity and diversity of fault types, the traditional modeling method based on oil sample analysis is struggling to meet the industrial demand for diagnostic accuracy. In order to solve this problem, this paper proposes a probabilistic neural network (PNN)-based fault diagnosis model for power transformers and optimizes the smoothing factor of the pattern layer of PNN by the improved gray wolf optimizer (IGWO) to improve the classification accuracy and robustness of PNN. The standard GWO easily falls into the local optimum because the update mechanism is too single. The update strategy proposed in this paper enhances the convergence ability and exploration ability of the algorithm, which greatly alleviates the dilemma that GWO is prone to fall into local optimum when dealing with complex data. In this paper, a reliability analysis of thirteen diagnostic methods is conducted using 555 transformer fault samples collected from Jiangxi Power Supply Company, China. The results show that the diagnostic accuracy of the IGWO-PNN model reaches 99.71%, which is much higher than that of the traditional IEC (International Electrotechnical Commission) three-ratio method. Compared with other neural network models, IGWO-PNN also has higher diagnostic accuracy and stability, and is more applicable to the field of transformer fault diagnosis. [ABSTRACT FROM AUTHOR]

Buildings account for a majority of the primary energy consumption of the human society, therefore, analyses of building energy consumption monitoring data are of significance to the discovery of anomalous energy usage patterns, saving of building utility expenditures, and contribution to the greater environmental protection effort. This paper presents a unified framework for the automatic extraction and integration of building energy consumption data from heterogeneous building management systems, along with building static data from building information models to serve analysis applications. This paper also proposes a diagnosis framework based on density-based clustering and artificial neural network regression using the integrated data to identify anomalous energy usages. The framework and the methods have been implemented and validated from data collected from a multitude of large-scale public buildings across China. [ABSTRACT FROM AUTHOR]

Cloud recognition is a basic task in ground meteorological observation. It is of great significance to accurately identify cloud types from long-time-series satellite cloud images for improving the reliability and accuracy of weather forecasting. However, different from ground-based cloud images with a small observation range and easy operation, satellite cloud images have a wider cloud coverage area and contain more surface features. Hence, it is difficult to effectively extract the structural shape, area size, contour shape, hue, shadow and texture of clouds through traditional deep learning methods. In order to analyze the regional cloud type characteristics effectively, we construct a China region meteorological satellite cloud image dataset named CRMSCD, which consists of nine cloud types and the clear sky (cloudless). In this paper, we propose a novel neural network model, UATNet, which can realize the pixel-level classification of meteorological satellite cloud images. Our model efficiently integrates the spatial and multi-channel information of clouds. Specifically, several transformer blocks with modified self-attention computation (swin transformer blocks) and patch merging operations are used to build a hierarchical transformer, and spatial displacement is introduced to construct long-distance cross-window connections. In addition, we introduce a Channel Cross fusion with Transformer (CCT) to guide the multi-scale channel fusion, and design an Attention-based Squeeze and Excitation (ASE) to effectively connect the fused multi-scale channel information to the decoder features. The experimental results demonstrate that the proposed model achieved 82.33% PA, 67.79% MPA, 54.51% MIoU and 70.96% FWIoU on CRMSCD. Compared with the existing models, our method produces more precise segmentation performance, which demonstrates its superiority on meteorological satellite cloud recognition tasks. [ABSTRACT FROM AUTHOR]

The extraction and evaluation of crop production units are important foundations for agricultural production and management in modern smallholder regions, which are very significant to the regulation and sustainable development of agriculture. Crop areas have been recognized efficiently and accurately via remote sensing (RS) and machine learning (ML), especially deep learning (DL), which are too rough for modern smallholder production. In this paper, a delimitation-grading method for actual crop production units (ACPUs) based on RS images was explored using a combination of a mask region-based convolutional neural network (Mask R-CNN), spatial analysis, comprehensive index evaluation, and cluster analysis. Da'an City, Jilin province, China, was chosen as the study region to satisfy the agro-production demands in modern smallholder areas. Firstly, the ACPUs were interpreted from perspectives such as production mode, spatial form, and actual productivity. Secondly, cultivated land plots (C-plots) were extracted by Mask R-CNN with high-resolution RS images, which were used to delineate contiguous cultivated land plots (CC-plots) on the basis of auxiliary data correction. Then, the refined delimitation-grading results of the ACPUs were obtained through comprehensive evaluation of spatial characteristics and real productivity clustering. For the conclusion, the effectiveness of the Mask R-CNN model in C-plot recognition (loss = 0.16, mean average precision (mAP) = 82.29%) and a reasonable distance threshold (20 m) for CC-plot delimiting were verified. The spatial features were evaluated with the scale-shape dimensions of nine specific indicators. Real productivities were clustered by the incorporation of two-step cluster and K-Means cluster. Furthermore, most of the ACPUs in the study area were of a reasonable scale and an appropriate shape, holding real productivities at a medium level or above. The proposed method in this paper can be adjusted according to the changes of the study area with flexibility to assist agro-supervision in many modern smallholder regions. [ABSTRACT FROM AUTHOR]

The identification of overpass structures in road networks has great significance for multi-scale modeling of roads, congestion analysis, and vehicle navigation. The traditional vector-based methods identify overpasses by the methodologies coming from computational geometry and graph theory, and they overly rely on the artificially designed features and have poor adaptability to complex scenes. This paper presents a novel method of identifying overpasses based on a target detection model (Faster-RCNN). This method utilizes raster representation of vector data and convolutional neural networks (CNNs) to learn task adaptive features from raster data, then identifies the location of an overpass by a Region Proposal network (RPN). The contribution of this paper is: (1) An overpass labelling geodatabase (OLGDB) for the OpenStreetMap (OSM) road network data of six typical cities in China is established; (2) Three different CNNs (ZF-net, VGG-16, Inception-ResNet V2) are integrated into Faster-RCNN and evaluated by accuracy performance; (3) The optimal combination of learning rate and batchsize is determined by fine-tuning; and (4) Five geometric metrics (perimeter, area, squareness, circularity, and W/L) are synthetized into image bands to enhance the training data, and their contribution to the overpass identification task is determined. The experimental results have shown that the proposed method has good accuracy performance (around 90%), and could be improved with the expansion of OLGDB and switching to more sophisticated target detection models. The deep learning target detection model has great application potential in large-scale road network pattern recognition, it can task-adaptively learn road structure features and easily extend to other road network patterns. [ABSTRACT FROM AUTHOR]

It is an objective demand for sustainable agricultural development to realize fast and accurate cultivated land quality assessment. In this paper, Tengzhou city (county-scale hilly area: scale A), Shanghe county (county-scale plain area: scale B), and Huang-Huai-Hai region (including large-scale hilly and plain area: scale C and D) were taken as research areas. Through the conversion of evaluation systems, the inversion models at the county-scale were constructed. Then, the image scale conversion was carried out based on the numerical regression method, and the upscaling inversion was realized. The results showed that: (1) the conversion models of evaluation systems (CMES) are Y = 1.021x − 4.989 (CMESA−B), Y = 0.801x + 16.925 (CMESA−C), and Y = 0.959x + 3.458 (CMESC−D); (2) the booting stage is the best inversion phase; (3) the back propagation neural network model based on the combination index group (CI-BPNN) is the best inversion model, with the R2 are 0.723 (modeling set) and 0.722 (verification set). CI-BPNN and CI-BPNN-CMESA−B models are suitable for the hilly and plain areas at the county-scale, and the level area ratio difference is less than 4.87%. Furthermore, (4) the reflectance conversion model of short-wave infrared 2 is cubic, and the rest are quadratic. CI-BPNN-CMESA−C and CI-BPNN-CMESA−C-CMESC−D models realized upscaling inversion in the hilly and plain areas, with the maximum level area ratio difference being 1.60%. Additionally, (5) the wheat field quality has improved steadily since 2001 in the Huang-Huai-Hai region. This study proposes an upscaling inversion method of wheat field quality, which provides a scientific basis for cultivated land management and agricultural production in large areas. [ABSTRACT FROM AUTHOR]

Landslides pose a constant threat to the lives and property of mountain people and may also cause geomorphological destruction such as soil and water loss, vegetation destruction, and land cover change. Landslide susceptibility assessment (LSA) is a key component of landslide risk evaluation. There are many related studies, but few analyses and comparisons of models for optimization. This paper aims to introduce the Tree-structured Parzen Estimator (TPE) algorithm for hyperparameter optimization of three typical neural network models for LSA in Shuicheng County, China, as an example, and to compare the differences of predictive ability among the models in order to achieve higher application performance. First, 17 influencing factors of landslide multiple data sources were selected for spatial prediction, hybrid ensemble oversampling and undersampling techniques were used to address the imbalanced sample and small sample size problem, and the samples were randomly divided into a training set and validation set. Second, deep neural network (DNN), recurrent neural network (RNN), and convolutional neural network (CNN) models were adopted to predict the regional landslides susceptibility, and the TPE algorithm was used to optimize the hyperparameters respectively to improve the assessment capacity. Finally, to compare the differences and optimization effects of these models, several objective measures were applied for validation. The results show that the high-susceptibility regions mostly distributed in bands along fault zones, where the lithology is mostly claystone, sandstone, and basalt. The DNN, RNN, and CNN models all perform well in LSA, especially the RNN model. The TPE optimization significantly improves the accuracy of the DNN and CNN (3.92% and 1.52%, respectively), but does not improve the performance of the RNN. In summary, our proposed RNN model and TPE-optimized DNN and CNN model have robust predictive capability for landslide susceptibility in the study area and can also be applied to other areas containing similar geological conditions. [ABSTRACT FROM AUTHOR]

In this paper, a deep learning long-short-term memory (LSTM) method is applied to the forecasting of the critical frequency of the ionosphere F2 layer (foF2). Hourly values of foF2 from 10 ionospheric stations in China and Australia (based on availability) from 2006 to 2019 are used for training and verifying. While 2015 and 2019 are exclusive for verifying the forecasting accuracy. The inputs of the LSTM model are sequential data for the previous values, which include local time (LT), day number, solar zenith angle, the sunspot number (SSN), the daily F10.7 solar flux, geomagnetic the Ap and Kp indices, geographic coordinates, neutral winds, and the observed value of foF2 at the previous moment. To evaluate the forecasting ability of the deep learning LSTM model, two different neural network forecasting models: a back-propagation neural network (BPNN) and a genetic algorithm optimized backpropagation neural network (GABP) were established for comparative analysis. The foF2 parameters were forecasted under geomagnetic quiet and geomagnetic disturbed conditions during solar activity maximum (2015) and minimum (2019), respectively. The forecasting results of these models are compared with those of the international reference ionosphere model (IRI2016) and the measurements. The diurnal and seasonal variations of foF2 for the 4 models were compared and analyzed from 8 selected verification stations. The forecasting results reveal that the deep learning LSTM model presents the optimal performance of all models in forecasting the time series of foF2, while the IRI2016 model has the poorest forecasting performance, and the BPNN model and GABP model are between two of them. [ABSTRACT FROM AUTHOR]

In order to estimate the energy consumption demand of residential buildings, this paper first discusses the status and shortcomings of current domestic energy consumption models. Then it proposes and develops a residential building energy consumption demand model based on a back propagation (BP) neural network model. After that, taking residential buildings in Chongqing (P.R. China) as an example, 16 energy consumption indicators are introduced as characteristics of the residential buildings in Chongqing. The index system of the BP neutral network prediction model is established and the multi-factorial BP neural network prediction model of Chongqing residential building energy consumption is developed using the Cshap language, based on the SQL server 2005 platform. The results obtained by applying the model in Chongqing are in good agreement with actual ones. In addition, the model provides corresponding approximate data by taking into account the potential energy structure adjustments and relevant energy policy regulations. [ABSTRACT FROM AUTHOR]

The accurate and timely estimation of regional crop biomass at different growth stages is of great importance in guiding crop management decision making. The recent availability of long time series of remote sensing data offers opportunities for crop monitoring. In this paper, four machine learning models, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), and extreme gradient boosting (XGBoost) were adopted to estimate the seasonal corn biomass based on field observation data and moderate resolution imaging spectroradiometer (MODIS) reflectance data from 2012 to 2019 in the middle reaches of the Heihe River basin, China. Nine variables were selected with the forward feature selection approach from among twenty-seven variables potentially influencing corn biomass: soil-adjusted total vegetation index (SATVI), green ratio vegetation index (GRVI), Nadir_B7 (2105–2155 nm), Nadir_B6 (1628–1652 nm), land surface water index (LSWI), normalized difference vegetation index (NDVI), Nadir_B4 (545–565 nm), and Nadir_B3 (459–479 nm). The results indicated that the corn biomass was suitably estimated (the coefficient of determination (R2) was between 0.72 and 0.78) with the four machine learning models. The XGBoost model performed better than the other three models (R2 = 0.78, root mean squared error (RMSE) = 2.86 t/ha and mean absolute error (MAE) = 1.86 t/ha). Moreover, the RF model was an effective method (R2 = 0.77, RMSE = 2.91 t/ha and MAE = 1.91 t/ha), with a performance comparable to that of the XGBoost model. This study provides a reference for estimating crop biomass from MOD43A4 datasets. In addition, the research demonstrates the potential of machine learning techniques to achieve a relatively accurate estimation of daily corn biomass at a large scale. [ABSTRACT FROM AUTHOR]

The energy consumption pattern dominated by traditional fossil energy has led to global energy resource constraints and the deterioration of the ecological environment. These challenges have become a major issue all over the world. At present, the Chinese government aims to significantly reduce the fossil energy consumption contribution in the terminal energy consumption. The development of renewable energy in the terminal energy and energy conversion links has significantly increased the proportion of clean low-carbon energy. In order to accurately get the proportion of renewable energy terminal power consumption, firstly, this paper selects a primary influencing-factors set including the gross GDP, fixed investment in renewable energy industry, total length of cross-provincial and cross-regional high-voltage transmission lines, etc. as influencing factors of China's electricity consumption fraction produced by renewable energy based on a multitude of papers. Secondly, from the perspective of signal decomposition, the data inevitably has a lot of interference and noise. This paper uses the empirical mode decomposition (EMD) algorithm to reduce the degree of signal distortion and decomposes the signal into natural modes including several intrinsic mode functions (IMFs) and a residual term (Res); afterwards, a new extreme learning machine (ELM) forecasting model optimized by an Inverse Square Root Linear Units (ISRLU) activation function is proposed, and the ISRLU function is used to replace the implicit layer activation function in the original ELM algorithm. Then, a new bacterial foraging algorithm (BFOA) is applied to optimize the parameters of the optimized ELM forecasting model. After multiple learning and training operations, the optimal parameters are obtained. Finally, we superimpose the output of each IMF and Res training task to get the amount of China's power consumption produced by renewable energy. Some statistical indicators including root mean squard error (RMSE) are applied to compare the accuracy of several intelligent machine forecasting algorithms. We prove that the proposed forecasting model has higher prediction accuracy and achieves faster training speed by an empirical analysis. Finally, the proposed combined forecasting algorithm is applied to predict China's renewable energy terminal power consumption from 2018 to 2030. According to the forecasting results, it is found that China's renewable energy terminal power consumption shows a gradual growth trend, and will exceeded 3300 billion kWh in 2030, which will represent a renewable energy terminal power ratio of about 38% in 2030. [ABSTRACT FROM AUTHOR]

The accurate determination of methane adsorption isotherms in coals is crucial for both the evaluation of underground coalbed methane (CBM) reserves and design of development strategies for enhancing CBM recovery. However, the experimental measurement of high-pressure methane adsorption isotherms is extremely tedious and time-consuming. This paper proposed the use of an ensemble machine learning (ML) method, namely the gradient boosting decision tree (GBDT), in order to accurately estimate methane adsorption isotherms based on coal properties in the Qinshui basin, China. The GBDT method was trained to correlate the adsorption amount with coal properties (ash, fixed carbon, moisture, vitrinite, and vitrinite reflectance) and experimental conditions (pressure, equilibrium moisture, and temperature). The results show that the estimated adsorption amounts agree well with the experimental ones, which prove the accuracy and robustness of the GBDT method. A comparison of the GBDT with two commonly used ML methods, namely the artificial neural network (ANN) and support vector machine (SVM), confirms the superiority of GBDT in terms of generalization capability and robustness. Furthermore, relative importance scanning and univariate analysis based on the constructed GBDT model were conducted, which showed that the fixed carbon and ash contents are primary factors that significantly affect the adsorption isotherms for the coal samples in this study. [ABSTRACT FROM AUTHOR]

The purpose of this study is to produce a landslide susceptibility map of Southeastern Helong City, Jilin Province, Northeastern China. According to the geological hazard survey (1:50,000) project of Helong city, a total of 83 landslides were mapped in the study area. The slope unit, which is classified based on the curvature watershed method, is selected as the mapping unit. Based on field investigations and previous studies, three groups of influencing Factors—Lithological factors, topographic factors, and geological environment factors (including ten influencing factors)—are selected as the influencing factors. Artificial neural networks (ANN's) and support vector machines (SVM's) are introduced to build the landslide susceptibility model. Five-fold cross-validation, the receiver operating characteristic curve, and statistical parameters are used to optimize model. The results show that the SVM model is the optimal model. The landslide susceptibility maps produced using the SVM model are classified into five grades—very high, high, moderate, low, and very low—and the areas of the five grades were 127.43, 151.60, 198.77, 491.19, and 506.91 km2, respectively. The very high and high susceptibility areas included 79.52% of the total landslides, demonstrating that the landslide susceptibility map produced in this paper is reasonable. Consequently, this study can serve as a guide for landslide prevention and for future land planning in the southeast of Helong city. [ABSTRACT FROM AUTHOR]

Image classification is a fundamental task in remote sensing image processing. In recent years, deep convolutional neural networks (DCNNs) have experienced significant breakthroughs in natural image recognition. The remote sensing field, however, is still lacking a large-scale benchmark similar to ImageNet. In this paper, we propose a remote sensing image classification benchmark (RSI-CB) based on massive, scalable, and diverse crowdsourced data. Using crowdsourced data, such as Open Street Map (OSM) data, ground objects in remote sensing images can be annotated effectively using points of interest, vector data from OSM, or other crowdsourced data. These annotated images can, then, be used in remote sensing image classification tasks. Based on this method, we construct a worldwide large-scale benchmark for remote sensing image classification. This benchmark has large-scale geographical distribution and large total image number. It contains six categories with 35 sub-classes of more than 24,000 images of size 256 × 256 pixels. This classification system of ground objects is defined according to the national standard of land-use classification in China and is inspired by the hierarchy mechanism of ImageNet. Finally, we conduct numerous experiments to compare RSI-CB with the SAT-4, SAT-6, and UC-Merced data sets. The experiments show that RSI-CB is more suitable as a benchmark for remote sensing image classification tasks than other benchmarks in the big data era and has many potential applications. [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]

Horizontal visibility (HVIS) is a primary index used for assessing air quality. Although satellite images provide information regarding atmospheric aerosols, atmospheric visibility is not directly measured. In this paper, a deep learning approach is proposed to retrieve HVIS using moderate-resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) data, the European Centre for Medium-Range Weather Forecasts reanalysis dataset, and ground-based visibility observations. The deep neural network model comprises a multi-layer unsupervised restricted Boltzmann machine (RBM) and a layer for supervised learning. The dropout mechanism was used in the training process to overcome the errors caused by over-fitting. The results demonstrate that the correlation coefficient values between HVIS observations and retrievals during training, pre-validating, and evaluation were 0.74, 0.723, and 0.697, respectively. The retrieved HVIS in Eastern China exhibited a north-to-south increasing trend, increasing and decreasing in summer and winter, respectively. In conclusion, the proposed model presents an effective and more reliable method for HVIS retrieval. However, the small samples, low AOD, low albedo, high total column water, high longitude, and the low vertical wind component at 10 m likely cause HVIS bias. [ABSTRACT FROM AUTHOR]

Strawberry growers in Florida suffer from a lack of efficient and accurate yield forecasts for strawberries, which would allow them to allocate optimal labor and equipment, as well as other resources for harvesting, transportation, and marketing. Accurate estimation of the number of strawberry flowers and their distribution in a strawberry field is, therefore, imperative for predicting the coming strawberry yield. Usually, the number of flowers and their distribution are estimated manually, which is time-consuming, labor-intensive, and subjective. In this paper, we develop an automatic strawberry flower detection system for yield prediction with minimal labor and time costs. The system used a small unmanned aerial vehicle (UAV) (DJI Technology Co., Ltd., Shenzhen, China) equipped with an RGB (red, green, blue) camera to capture near-ground images of two varieties (Sensation and Radiance) at two different heights (2 m and 3 m) and built orthoimages of a 402 m2 strawberry field. The orthoimages were automatically processed using the Pix4D software and split into sequential pieces for deep learning detection. A faster region-based convolutional neural network (R-CNN), a state-of-the-art deep neural network model, was chosen for the detection and counting of the number of flowers, mature strawberries, and immature strawberries. The mean average precision (mAP) was 0.83 for all detected objects at 2 m heights and 0.72 for all detected objects at 3 m heights. We adopted this model to count strawberry flowers in November and December from 2 m aerial images and compared the results with a manual count. The average deep learning counting accuracy was 84.1% with average occlusion of 13.5%. Using this system could provide accurate counts of strawberry flowers, which can be used to forecast future yields and build distribution maps to help farmers observe the growth cycle of strawberry fields. [ABSTRACT FROM AUTHOR]

Predicting the passenger flow of metro networks is of great importance for traffic management and public safety. However, such predictions are very challenging, as passenger flow is affected by complex spatial dependencies (nearby and distant) and temporal dependencies (recent and periodic). In this paper, we propose a novel deep-learning-based approach, named STGCNNmetro (spatiotemporal graph convolutional neural networks for metro), to collectively predict two types of passenger flow volumes—inflow and outflow—in each metro station of a city. Specifically, instead of representing metro stations by grids and employing conventional convolutional neural networks (CNNs) to capture spatiotemporal dependencies, STGCNNmetro transforms the city metro network to a graph and makes predictions using graph convolutional neural networks (GCNNs). First, we apply stereogram graph convolution operations to seamlessly capture the irregular spatiotemporal dependencies along the metro network. Second, a deep structure composed of GCNNs is constructed to capture the distant spatiotemporal dependencies at the citywide level. Finally, we integrate three temporal patterns (recent, daily, and weekly) and fuse the spatiotemporal dependencies captured from these patterns to form the final prediction values. The STGCNNmetro model is an end-to-end framework which can accept raw passenger flow-volume data, automatically capture the effective features of the citywide metro network, and output predictions. We test this model by predicting the short-term passenger flow volume in the citywide metro network of Shanghai, China. Experiments show that the STGCNNmetro model outperforms seven well-known baseline models (LSVR, PCA-kNN, NMF-kNN, Bayesian, MLR, M-CNN, and LSTM). We additionally explore the sensitivity of the model to its parameters and discuss the distribution of prediction errors. [ABSTRACT FROM AUTHOR]

The Chinese tourism industry has been developing rapidly for the past several years, and the number of people traveling has been increasing year by year. However, many problems still beset current tourism management. Lack of effective management has caused numerous problems, such as tourists stranded during tourist season and the declining service quality of scenic spots, which have become the focus of tourists' attention. Network search data can intuitively reflect the attention of most users through the combination of the network search index and the back propagation (BP) neural network model. This study predicts the daily tourism demand in the Huangshan scenic spot in China. The filtered keyword in the Baidu index is added to the hybrid neural network, and a BP neural network model optimized by a fruit fly optimization algorithm (FOA) based on the web search data is established in this study. Different forecasting methods are compared in this paper; the results prove that compared with other prediction models, higher accuracy can be obtained when it comes to the peak season using the FOA-BP method that includes web search data, which is a sustainable means of practically solving the tourism management problem by a more accurate prediction of tourism demand of scenic spots. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for semantic segmentation of building roofs in dense urban environments with a Deep Convolution Neural Network (DCNN) using Chinese Very High Resolution (VHR) satellite (i.e., GF2) imagery. To provide an operational end-to-end approach for accurately mapping build roofs with feature extraction and image segmentation, a fully convolutional DCNN with both convolutional and deconvolutional layers is designed to perform building roof segmentation. We selected typical cities with dense and diverse urban environments in different metropolitan regions of China as study areas, and sample images were collected over cities. High performance GPU-mounted workstations are employed to perform the model training and optimization. With the building roof samples collected over different cities, the predictive model with convolution layers is developed for building roof segmentation. The validation shows that the overall accuracy (OA) and the mean Intersection Over Union (mIOU) of DCNN-based semantic segmentation results are 94.67% and 0.85, respectively, and the CRF-refined segmentation results achieved OA of 94.69% and mIOU of 0.83. The results suggest that the proposed approach is a promising solution for building roof mapping with VHR images over large areas in dense urban environments with different building patterns. With the operational acquisition of GF2 VHR imagery, it is expected to develop an automated pipeline of operational built-up area monitoring, and the timely update of building roof map could be applied in urban management and assessment of human settlement-related sustainable development goals over large areas. [ABSTRACT FROM AUTHOR]