Use Of Multi-source Data Research Articles

A Novel Intelligent Fault Diagnosis Method for Bearings with Multi-Source Data and Improved GASA.

In recent years, single-source-data-based deep learning methods have made considerable strides in the field of fault diagnosis. Nevertheless, the extraction of useful information from multi-source data remains a challenge. In this paper, we propose a novel approach called the Genetic Simulated Annealing Optimization (GASA) method with a multi-source data convolutional neural network (MSCNN) for the fault diagnosis of rolling bearing. This method aims to identify bearing faults more accurately and make full use of multi-source data. Initially, the bearing vibration signal is transformed into a time-frequency graph using the continuous wavelet transform (CWT) and the signal is integrated with the motor current signal and fed into the network model. Then, a GASA-MSCNN fault diagnosis method is established to better capture the crucial information within the signal and identify various bearing health conditions. Finally, a rolling bearing dataset under different noisy environments is employed to validate the robustness of the proposed model. The experimental results demonstrate that the proposed method is capable of accurately identifying various types of rolling bearing faults, with an accuracy rate reaching up to 98% or higher even in variable noise environments. The experiments reveal that the new method significantly improves fault detection accuracy.

Multi-source deep-learning approach for automatic geomorphological mapping: the case of glacial moraines

ABSTRACT Landform mapping is the initial step of many geomorphological analyses (e.g. assessment of natural hazards and natural resources) and requires vast resources to be applied to wide areas at high-resolution. Among geomorphological objects, we focus on glacial moraine mapping, since it is a task relevant to many fields (e.g. paleoclimate and glacial geomorphology). Here we proposed to exploit the potential of Deep Learning-based approaches to map moraine landforms by exploiting multi-source remote sensing imagery. To this end, we propose the first Deep Learning model to map glacial moraines, namely MorNet. As multi-source remote sensing information, we combine together three different sources: Topographic (Pleiades-derived DSM), Multispectral (Sentinel-2), and SAR (Sentinel-1) data. To cope with such heterogeneous information, the proposed model has a dedicated branch for each input source and, a late fusion mechanism is leveraged to combine them with the aim to provide the final mapping. The performance of the MorNet model is evaluated on several glacier valleys in China in the Himalayan range. This area contains minimally eroded moraines, so they are well-defined and of varied morphology. The behavior of the proposed method is compared to models using individual mono-source models in order to highlight the benefit to simultaneously leverage multi-source information. The use of multi-source data allows MorNet to exploit the complementarity of the three input sources and improve its performance from an f1-score of about 41.6 using a single source to 52.8 using three sources. MorNet provides a first-order moraine map through its ability to identify well-defined moraines. Consequently, MorNet can identify areas likely to contain moraines and intends to be used as a tool by experts to facilitate and support large-scale mapping.

Classification of Urban Functional Zones Based on Multiple Data Sources

With the rapid development of cities, more and more people are flocking to them. Therefore, cities require good planning to meet the needs of all urban residents. The first step in planning a city is to classify the functional areas of the city. This article introduces a method for classifying urban functional areas in the context of rapid urban population growth, aiming to plan cities better and meet population needs. Based on remote sensing, social perception, and multi-source data, corresponding classification methods were explored, and their roles in urban functional area classification were analyzed. Research has found that focusing remote sensing images on local areas, utilizing social perception data uploaded by passersby, and complimentary use of multi-source data can all improve classification accuracy. The exploration of these methods helps to classify urban functional areas more accurately, providing a better basis for future urban development planning and alleviating the pressure brought by urban population growth.

Evaluation of Remote Sensing Products for Wetland Mapping in the Irtysh River Basin

As a transboundary river with rich and unique wetland types, the Irtysh River faces various challenges and threats from human activities and climate change, which affect area, type, and function of wetland. To accurately obtain information on the spatial and temporal distribution of wetlands in this basin, this study compares and evaluates the consistency and accuracy of a total of eleven remote sensing (RS) based land use/land cover (LULC), and wetland products. The information extraction effect of each RS product was examined through methods such as wetland area and type description, thematic map comparison, and similarity coefficient and Kappa coefficient calculations, which can reflect the wetland distribution characteristics and differences among the RS products in the Irtysh River Basin. The results show that although there is a consensus among the products in the major wetland distribution areas, there are still obvious deviations in detail depiction due to differences in factors such as data sources and methods. The products of Global 30 m Wetland Fine Classification Data (GWL_FCS30) and Global 30 m Land Cover Data (GLC_FCS30-2020) released by the Institute of Space and Astronautical Information Innovation (ISAI) of the Chinese Academy of Sciences (CAS) have a clear advantage in extracting spatial morphology features of wetlands due to the use of multi-source data, while the Esri Global 10 m Land Cover Data (ESRI_Global-LULC_10m) and products such as the global 10 m land cover data (FROM_GLC10_2017) from Tsinghua University have higher classification consistency. Moreover, data resolution, classification scheme design, and validation methods are key factors affecting the quality of wetland information extraction in the Irtysh River Basin. In practical terms, the findings of this study hold significant implications for informed decision-making in wetland conservation and management within the Irtysh River Basin. By advancing wetland monitoring technologies and addressing critical considerations in information extraction, this research effectively bridges the gap between remote sensing technology and practical applications, offering valuable insights for regional wetland protection efforts.

Conversion and fusion method of multi-source and different populations maintainability prior data

Maintainability is an important universal quality characteristic that reflects the convenience, speed and economy of weapon and equipment maintenance. Making full use of multi-source data to accurately verify the degree to which the developed equipment meets the maintainability requirements is an important basis for equipment identification and acceptance. To solve the low reliability of equipment maintainability verification results caused by inaccurate comprehensive prior distribution obtained by fusing multi-source and different populations' prior data, a method of data conversion and fusion is proposed. A data conversion model based on the mean value ratio of failure mode maintenance data is constructed. The conversion factor is defined according to objective data to convert the different populations' prior data to the same populations. Next, a comparison of the prior distribution fitting performance of Bayes bootstrap, bootstrap, and two improved sample-resampling methods to are used obtain the closest fitting distribution to the true distribution. By constructing a multi-source data fusion model based on improved KL divergence, a symmetrical KL divergence is constructed to describe the similarity between each prior distribution and the field distribution for the weighted fusion of multi-source prior distribution in addition to determining and testing the normal comprehensive prior distribution. The results show that the conversion and fusion method effectively converts the multi-source and different populations’ maintainability prior data and obtains an accurate, comprehensive prior distribution by fusion, laying the foundation for applying the Bayes test method to verify the quantitative index of equipment maintainability.

Regulation of Planting Structure Considering Irrigation Water, Carbon Emission, and Grain Security in the Yellow River Basin, China, by Using Multisource Data

Grain security is the foundation for national stability and prosperity. In China, grain production depends heavily on irrigation water, direct energy for machinery use, and indirect energy (such as fertilizers, pesticides, and agricultural film), resulting in growing pressure on water supply and demand and high carbon emissions. Therefore, it is urgent to study the regulation of planting structures to reduce the irrigation water input and carbon emissions on the premise of grain security. It is within the scope of the water–energy–food nexus. In China, water resources are mostly managed at the basin scale. Because of missing or inadequate basin-scale data, in this study, the Yellow River Basin (YRB) was chosen as an example to shed light on basin scale regulation analysis; YRB is an important grain production base in China, but it is facing serious water shortage and severe environmental problems. First, production and consumption of diverse grain species in the basin were estimated using remote sensing and statistical data simultaneously. Then, the blue water and carbon footprints were calculated to analyze the water use and carbon emission characteristics of grain production. Finally, the grain planting structure was regulated to reduce the irrigation water input and carbon emissions. Our findings suggest that, with effective planting structure regulation, 4.11×108 m3 irrigation water and 16.98×104 tons CO2 equivalents could be reduced and the grain production increased by about 31×104 tons in the YRB. This study can be viewed as a step towards the use of multisource data to facilitate the analysis of diverse grain species characteristics and sustainable grain development while considering the water–energy–food nexus. A combination of effective planting structure regulation in arid regions and policies will contribute to an increase in grain production as well as saving of resources.

VGAEDTI: drug-target interaction prediction based on variational inference and graph autoencoder

MotivationAccurate identification of Drug-Target Interactions (DTIs) plays a crucial role in many stages of drug development and drug repurposing. (i) Traditional methods do not consider the use of multi-source data and do not consider the complex relationship between data sources. (ii) How to better mine the hidden features of drug and target space from high-dimensional data, and better solve the accuracy and robustness of the model.ResultsTo solve the above problems, a novel prediction model named VGAEDTI is proposed in this paper. We constructed a heterogeneous network with multiple sources of information using multiple types of drug and target dataIn order to obtain deeper features of drugs and targets, we use two different autoencoders. One is variational graph autoencoder (VGAE) which is used to infer feature representations from drug and target spaces. The second is graph autoencoder (GAE) propagating labels between known DTIs. Experimental results on two public datasets show that the prediction accuracy of VGAEDTI is better than that of six DTIs prediction methods. These results indicate that model can predict new DTIs and provide an effective tool for accelerating drug development and repurposing.

Exploring the use of multi-source high-resolution satellite data for snow water equivalent reconstruction over mountainous catchments

Abstract. The hydrological cycle is strongly influenced by the accumulation and melting of seasonal snow. For this reason, mountains are often claimed to be the “water towers” of the world. In this context, a key variable is the snow water equivalent (SWE). However, the complex processes of snow accumulation, redistribution, and ablation make its quantification and prediction very challenging. In this work, we explore the use of multi-source data to reconstruct SWE at a high spatial resolution (HR) of 25 m. To this purpose, we propose a novel approach based on (i) in situ snow depth or SWE observations, temperature data and synthetic aperture radar (SAR) images to determine the pixel state, i.e., whether it is undergoing an SWE increase (accumulation) or decrease (ablation), (ii) a daily HR time series of snow cover area (SCA) maps derived by high- and low-resolution multispectral optical satellite images to define the days of snow presence, and (iii) a degree-day model driven by in situ temperature to determine the potential melting. Given the typical high spatial heterogeneity of snow in mountainous areas, the use of HR images represents an important novelty that allows us to sample its distribution more adequately, thus resulting in highly detailed spatialized information. The proposed SWE reconstruction approach also foresees a novel SCA time series regularization technique that models impossible transitions based on the pixel state, i.e., the erroneous change in the pixel class from snow to snow-free when it is expected to be in accumulation or equilibrium and, vice versa, from snow-free to snow when it is expected to be in ablation or equilibrium. Furthermore, it reconstructs the SWE for the entire hydrological season, including late snowfall. The approach does not require spatialized precipitation information as input, which is usually affected by uncertainty. The method provided good results in two different test catchments: the South Fork of the San Joaquin River, California, and the Schnals catchment, Italy. It obtained good agreement when evaluated against HR spatialized reference maps (showing an average bias of −22 mm, a root mean square error – RMSE – of 212 mm, and a correlation of 0.74), against a daily dataset at coarser resolution (showing an average bias of −44 mm, an RMSE of 127 mm, and a correlation of 0.66), and against manual measurements (showing an average bias of −5 mm, an RMSE of 191 mm, and a correlation of 0.35). The main sources of error are discussed to provide insights into the main advantages and disadvantages of the method that may be of interest for several hydrological and ecological applications.

A Soft Sensor Model of Sintering Process Quality Index Based on Multi-Source Data Fusion

In complex industrial processes such as sintering, key quality variables are difficult to measure online and it takes a long time to obtain quality variables through offline testing. Moreover, due to the limitations of testing frequency, quality variable data are too scarce. To solve this problem, this paper proposes a sintering quality prediction model based on multi-source data fusion and introduces video data collected by industrial cameras. Firstly, video information of the end of the sintering machine is obtained via the keyframe extraction method based on the feature height. Secondly, using the shallow layer feature construction method based on sinter stratification and the deep layer feature extraction method based on ResNet, the feature information of the image is extracted at multi-scale of the deep layer and the shallow layer. Then, combining industrial time series data, a sintering quality soft sensor model based on multi-source data fusion is proposed, which makes full use of multi-source data from various sources. The experimental results show that the method effectively improves the accuracy of the sinter quality prediction model.

Application of Remote Sensing Technology in Different Natural Disasters

Flood, typhoon and heat wave are common natural disasters in the world. Satellite remote sensing technology has become an effective method for geological hazard assessment due to its wide coverage, large imaging area and high time resolution. Based on introducing the research progress of flood disaster remote sensing monitoring, this paper compares and summarizes the research progress of principle and method of multi-source remote sensing flood monitoring, and analyses the advantages and disadvantages of various data. We also analyze the comprehensive application of remote sensing technology in typhoon monitoring from the perspectives of conventional observation data, satellite remote sensing data and unmanned aerial vehicle remote sensing data. In this paper, remote sensing technology is also used to monitor urban heat island and other climatic problems through machine learning, starting from solving heat wave problems in the climatic field by remote sensing. At the same time, the existing problems of various data resources in the analysis of various disasters are summarized, and the remote sensing technology which needs to make comprehensive use of multi-source data, the construction of climate monitoring system and other aspects are summarized to study the remote sensing information system in the field of natural climate disasters. The development trend of this technology is also summarized and prospected.

Beaver pond identification from multi-temporal and multi- sourced remote sensing data

ABSTRACT The maintenance and restoration of wetland habitat is a priority conservation action for most waterfowl and other wetland-dependent species in North America. Despite much progress in targeting habitat management in staging and wintering areas, methods to identify and target high-quality breeding habitats that result in the greatest potential for wildlife are still required. This is particularly true for species that breed in remote, inaccessible areas such as the American black duck, an intensively managed game bird in Eastern North America. Although evidence suggests that black ducks prefer productive, nutrient-rich waterbodies, such as beaver ponds, information about the distribution and quality of these habitats across the vast boreal forest is lacking with accurate identification remaining a challenge. Continuing advancements in remote sensing technologies that provide spatially extensive and temporally repeated information are particularly useful in meeting this information gap. In this study, we used multi-source remotely sensed information and a fuzzy analytical hierarchy process to map the spatial distribution of beaver ponds in Ontario. The use of multi-source data, including a Digital Elevation Model, a Sentinel-2 Multi-Spectral Image, and RadarSat 2 Polarimetric data, enabled us to identify individual beaver ponds on the landscape. Our model correctly identified an average of 83.0% of the known beaver dams and 72.5% of the known beaver ponds based on validation with an independent dataset. This study demonstrates that remote sensing is an effective approach for identifying beaver-modified wetland features and can be applied to map these and other wetland habitat features of interest across large spatial extents. Furthermore, the systematic acquisition strategy of the remote sensors employed is well suited for monitoring changes in wetland conditions that affect the availability of habitats important to waterfowl and other wildlife.

Inter-Seasonal Estimation of Grass Water Content Indicators Using Multisource Remotely Sensed Data Metrics and the Cloud-Computing Google Earth Engine Platform

Indicators of grass water content (GWC) have a significant impact on eco-hydrological processes such as evapotranspiration and rainfall interception. Several site-specific factors such as seasonal precipitation, temperature, and topographic variations cause soil and ground moisture content variations, which have significant impacts on GWC. Estimating GWC using multisource data may provide robust and accurate predictions, making it a useful tool for plant water quantification and management at various landscape scales. In this study, Sentinel-2 MSI bands, spectral derivatives combined with topographic and climatic variables, were used to estimate leaf area index (LAI), canopy storage capacity (CSC), canopy water content (CWC) and equivalent water thickness (EWT) as indicators of GWC within the communal grasslands in Vulindlela across wet and dry seasons based on single-year data. The results illustrate that the use of combined spectral and topo-climatic variables, coupled with random forest (RF) in the Google Earth Engine (GEE), improved the prediction accuracies of GWC variables across wet and dry seasons. LAI was optimally estimated in the wet season with an RMSE of 0.03 m−2 and R2 of 0.83, comparable to the dry season results, which exhibited an RMSE of 0.04 m−2 and R2 of 0.90. Similarly, CSC was estimated with high accuracy in the wet season (RMSE = 0.01 mm and R2 = 0.86) when compared to the RMSE of 0.03 mm and R2 of 0.93 obtained in the dry season. Meanwhile, for CWC, the wet season results show an RMSE of 19.42 g/m−2 and R2 of 0.76, which were lower than the accuracy of RMSE = 1.35 g/m−2 and R2 = 0.87 obtained in the dry season. Finally, EWT was best estimated in the dry season, yielding a model accuracy of RMSE = 2.01 g/m−2 and R2 = 0.91 as compared to the wet season (RMSE = 10.75 g/m−2 and R2 = 0.65). CSC was best optimally predicted amongst all GWC variables in both seasons. The optimal variables for estimating these GWC variables included the red-edge, near-infrared region (NIR) and short-wave infrared region (SWIR) bands and spectral derivatives, as well as environmental variables such as rainfall and temperature across both seasons. The use of multisource data improved the prediction accuracies for GWC indicators across both seasons. Such information is crucial for rangeland managers in understanding GWC variations across different seasons as well as different ecological gradients.

How Did the Built Environment Affect Urban Vibrancy? A Big Data Approach to Post-Disaster Revitalization Assessment.

Quantitative assessment of urban vibrancy is crucial to understanding urban development and promoting sustainability, especially for rapidly developing areas and regions that have experienced post-disaster reconstruction. Taking Dujiangyan City, the hardest-hit area of the earthquake, as an example, this paper quantifies the urban economic, social, and cultural vibrancy after reconstruction by the use of multi-source data, and conducts a geographic visualization analysis. The purpose is to establish an evaluation framework for the relationship between the urban built environment elements and vibrancy in different dimensions, to evaluate the benefits of post-disaster restoration and reconstruction. The results show that the urban vibrancy reflected by classified big data can not be completely matched due to the difference in the data generation and collection process. The Criteria Importance Though Inter-criteria Correlation and entropy (CRITIC-entropy) method is used to construct a comprehensive model is a better representation of the urban vibrancy spatial characteristics. On a global scale, comprehensive vibrancy demonstrates high continuity and a bi-center structure. In the old town, the distribution of various urban vibrancies show diffusion characteristics, while those in the new district demonstrated a high degree of aggregation, and the comprehensive vibrancy is less sensitive to land-use mixture and more sensitive to residential land.

The use of remote sensing and GIS for land use and land cover mapping in Eswatini: A Review

Remote sensing and GIS are often used to assess spatiotemporal variations for land use/land cover (LULC) monitoring and classification. While LULC monitoring and classification has been undertaken in Eswatini, little attention has been given to ascertaining covered thematic areas, methods of image classification, and approaches and techniques for improving classification accuracy. This paper summarises and synthesizes the progress made in the Kingdom of Eswatini regarding the application of remote sensing and GIS in LULC monitoring and classification. Eight thematic areas (water resources mapping; land degradation; forestry; wildfire detection; urban expansion; crop production; disease surveillance; general mapping) dominate evaluated LULC studies, employing three LULC classification methods (classic; manual; advanced). While some studies include strengths and weaknesses of LULC classification techniques applied, others do not. This review shows that only two advanced classifiers (random forest; object-based) were identified from the reviewed articles. In addition, reviewed studies applied only two approaches (use of multi temporal data; fine spatial resolution data) and three techniques (use of ancillary data; post-classification procedure; the use of multisource data) for improving classification accuracy. Furthermore, the review finds that limited LULC investigations have been covered in Eswatini with a specific focus on the Sustainable Development Goals (SDGs). As such, this review recommends 1) the inclusion of higher resolution imagery for mapping purposes, 2) the adaptation of strengths and weaknesses for any image classification technique employed in future publications, 3) the use of more varied approaches and techniques for improving classification accuracy and area estimates, 4) inclusion of standard errors or confidence intervals for error-adjusted area estimates as part of accuracy assessment reporting, 5) the application of advanced image classifiers, and 6) the application of Earth Observation (EO) Analysis Ready Data (ARD) in the production of information for the support of the SDGs.

Automatic Detection and Classification of Radio Galaxy Images by Deep Learning

Surveys conducted by radio astronomy observatories, such as SKA, MeerKAT, Very Large Array, and ASKAP, have generated massive astronomical images containing radio galaxies (RGs). This generation of massive RG images has imposed strict requirements on the detection and classification of RGs and makes manual classification and detection increasingly difficult, even impossible. Rapid classification and detection of images of different types of RGs help astronomers make full use of the observed astronomical image data for further processing and analysis. The classification of FRI and FRII is relatively easy, and there are more studies and literature on them at present, but FR0 and FRI are similar, so it is difficult to distinguish them. It poses a greater challenge to image processing. At present, deep learning has made breakthrough progress in the field of image analysis and processing and has preliminary applications in astronomical data processing. Compared with classification algorithms that can only classify galaxies, object detection algorithms that can locate and classify RGs simultaneously are preferred. In target detection algorithms, YOLOv5 has outstanding advantages in the classification and positioning of small targets. Therefore, we propose a deep-learning method based on an improved YOLOv5 object detection model that makes full use of multisource data, combining FIRST radio with SDSS optical image data, and realizes the automatic detection of FR0, FRI, and FRII RGs. The innovation of our work is that on the basis of the original YOLOv5 object detection model, we introduce the SE Net attention mechanism, increase the number of preset anchors, adjust the network structure of the feature pyramid, and modify the network structure, thereby allowing our model to demonstrate galaxy classification and position detection effects. Our improved model produces satisfactory results, as evidenced by experiments. Overall, the mean average precision (mAP@0.5) of our improved model on the test set reaches 89.4%, which can determine the position (R.A. and decl.) and automatically detect and classify FR0s, FRIs, and FRIIs. Our work contributes to astronomy because it allows astronomers to locate FR0, FRI, and FRII galaxies in a relatively short time and can be further combined with other astronomically generated data to study the properties of these galaxies. The target detection model can also help astronomers find FR0s, FRIs, and FRIIs in future surveys and build a large-scale star RG catalog. Moreover, our work is also useful for the detection of other types of galaxies.

Remote Sensing of Wetlands in the Prairie Pothole Region of North America

The Prairie Pothole Region (PPR) of North America is an extremely important habitat for a diverse range of wetland ecosystems that provide a wealth of socio-economic value. This paper describes the ecological characteristics and importance of PPR wetlands and the use of remote sensing for mapping and monitoring applications. While there are comprehensive reviews for wetland remote sensing in recent publications, there is no comprehensive review about the use of remote sensing in the PPR. First, the PPR is described, including the wetland classification systems that have been used, the water regimes that control the surface water and water levels, and the soil and vegetation characteristics of the region. The tools and techniques that have been used in the PPR for analyses of geospatial data for wetland applications are described. Field observations for ground truth data are critical for good validation and accuracy assessment of the many products that are produced. Wetland classification approaches are reviewed, including Decision Trees, Machine Learning, and object versus pixel-based approaches. A comprehensive description of the remote sensing systems and data that have been employed by various studies in the PPR is provided. A wide range of data can be used for various applications, including passive optical data like aerial photographs or satellite-based, Earth-observation data. Both airborne and spaceborne lidar studies are described. A detailed description of Synthetic Aperture RADAR (SAR) data and research are provided. The state of the art is the use of multi-source data to achieve higher accuracies and hybrid approaches. Digital Surface Models are also being incorporated in geospatial analyses to separate forest and shrub and emergent systems based on vegetation height. Remote sensing provides a cost-effective mechanism for mapping and monitoring PPR wetlands, especially with the logistical difficulties and cost of field-based methods. The wetland characteristics of the PPR dictate the need for high resolution in both time and space, which is increasingly possible with the numerous and increasing remote sensing systems available and the trend to open-source data and tools. The fusion of multi-source remote sensing data via state-of-the-art machine learning is recommended for wetland applications in the PPR. The use of such data promotes flexibility for sensor addition, subtraction, or substitution as a function of application needs and potential cost restrictions. This is important in the PPR because of the challenges related to the highly dynamic nature of this unique region.

Forecast of regional water demand based on NSGAII-FORAGM

Abstract Regional water demand is an important basic data for regional engineering planning, design and management. Making full use of multi-source data and prior knowledge to quickly and economically obtain high-precision regional water demand is of great significance to the optimal allocation of regional water resources. In order to accurately predict the regional water demand, this study took Yulin City as a research area to predict the water demand of the city from 2017 to 2019. Aiming at the oscillating characteristics of the regional water demand sequence and the over-fitting problem of traditional prediction models, this study proposed the non-dominated sorting genetic algorithm II-fractional order reverse accumulative grey model (NSGAII-FORAGM). The regional water demand oscillation sequence was transformed into a monotonically decreasing non-negative sequence. Based on the transformation sequence, an optimization model was constructed according to the two objective functions of ‘maximum (or minimum) order’ and ‘best fit to historical data’, and the NSGAII method was adopted to solve the model. The three model structures of ‘fractional order’, ‘reverse accumulation’ and ‘obtaining order through multi-objective optimization model ‘ were tested based on the water use sequence of the three sectors (industry, tertiary industry and domestic) in Yulin City, and the performance of the method is compared with NSGAII-IORAGM, NSGAII-FOFAGM and SOGA-FORAGM. The results showed that the average relative error of the model established in this study for the simulation of industry, tertiary industry (the tertiary industry is a technical name for the service sector of the economy, which encompasses a wide range of businesses), and domestic was 15.54%, 11.20%, 9.98% respectively. The average relative error of the model established in this study for the prediction of industry, tertiary industry and domestic was 9.46%, 7.9%, and 1.8%, respectively. For the simulation of water demand sequences in three sections, the simulation average relative errors of the other three models were not absolutely dominant except for the SOGA-FORAGM model. The average relative predicted error by the model in this study was the smallest (the relative errors of the three sequence predictions for industry, tertiary industry and domestic were lower than the relative errors of the optimal results of the comparison model, which were 0.97%, 0.72% and 4.5%, respectively), indicating that the model had certain applicability for the water demand prediction of various sectors (industry, tertiary industry and domestic) in the region compared with other models, and can improve the accuracy of the prediction results.

A 2020 perspective on “DeRec: A data-driven approach to accurate recommendation with deep learning and weighted loss function”

The development of Internet comes up with the prosperity of E-commerce all over the world. In order to promote sales and save consumers’ labor in commodity browsing, recommender systems are proposed by E-commerce platforms to provide online consumers with products and services of their potential interests. The primary challenge in recommendation roots in the intricacy in quantifying users’ preferences on items with the reality of data sparsity and the computation complexity. Hence, more and more researchers are attempting deep learning techniques to deal with the challenge with the hope of using advanced algorithms to alleviate the intricacy. Word embedding is used to learn the association of items in a space of low dimensionality. Multi-layer perception is used to learn users’ preferences on items in a data-driven manner with a customized loss function. The future work of recommender systems includes three folds. The one is to make use of multi-source data to combine implicit and explicit user behavior data to address the problem of data sparsity. The second is dynamic recommendation with the changing users’ preferences on items and make recommender systems light-weight and useable in complex scenarios. The third is to provide effective and verifiable recommendation under the premise of user privacy protection

Linking cohort-based data with electronic health records: a proof-of-concept methodological study in Hong Kong

ObjectivesData linkage of cohort-based data and electronic health records (EHRs) has been practised in many countries, but in Hong Kong there is still a lack of such research. To expand...

Modeling Soil Moisture from Multisource Data by Stepwise Multilinear Regression: An Application to the Chinese Loess Plateau

This study aims to integrate multisource data to model the relative soil moisture (RSM) over the Chinese Loess Plateau in 2017 by stepwise multilinear regression (SMLR) in order to improve the spatial coverage of our previously published RSM. First, 34 candidate variables (12 quantitative and 22 dummy variables) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and topographic, soil properties, and meteorological data were preprocessed. Then, SMLR was applied to variables without multicollinearity to select statistically significant (p-value < 0.05) variables. After the accuracy assessment, monthly, seasonal, and annual spatial patterns of RSM were mapped at 500 m resolution and evaluated. The results indicate that there was a high potential of SMLR to model RSM with the desired accuracy (best fit of the model with Pearson’s r = 0.969, root mean square error = 0.761%, and mean absolute error = 0.576%) over the Chinese Loess Plateau. The variables of elevation (0–500 m and 2000–2500 m), precipitation, soil texture of loam, and nighttime land surface temperature can continuously be used in the regression models for all seasons. Including dummy variables improved the model fit both in calibration and validation. Moreover, the SMLR-modeled RSM achieved better spatial coverage than that of the reference RSM for almost all periods. This is a significant finding as the SMLR method supports the use of multisource data to complement and/or replace coarse resolution satellite imagery in the estimation of RSM.