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China has resolved its overall regional poverty in 2020 by attaining moderate societal prosperity. The country has entered a new development stage designed to achieve its second centenary goal. However, ecological fragility and risk susceptibility have increased the risk of returning to ecological poverty. In this paper, the Liupan Mountain Region of China was used as a case study, and the counties were used as the scale to reveal the spatiotempora differentiation and influcing factors of the risk of returning to poverty in study area. The indicator data for returning to ecological poverty from 2011–2020 were collected and summarized in three dimensions: ecological, economic and social. The autoregressive integrated moving average model (ARIMA) time series and exponential smoothing method (ES) were used to predict the multidimensional indicators of returning to ecological poverty for 61 counties (districts) in the Liupan Mountain Region for 2021–2030. The back propagation neural network (BPNN) and geographic information system (GIS) were used to generate the spatial distribution and time variation for the index of the risk of returning to ecological poverty (RREP index). The results show that 1) ecological factors were the main factors in the risk of returning to ecological poverty in Liupan Mountain Region. 2) The RREP index for the 61 counties (districts) exhibited a downward trend from 2021–2030. The RREP index declined more in medium- and high-risk areas than in low-risk areas. From 2021 to 2025, the RREP index exhibited a slight downward trend. From 2026 to 2030, the RREP index was expected to decline faster, especially from 2029–2030. 3) Based on the RREP index, it can be roughly divided into three types, namely, the high-risk areas, the medium-risk areas, and the low-risk areas. The natural resource conditions in low-risk areas of returning to ecological poverty, were better than those in medium- and high-risk areas. [ABSTRACT FROM AUTHOR]

Under the long-term implementation of the low fare policy for public transport in China, the phenomenon of unsustainable subsidies is increasingly obvious. We take into account three related stakeholders including the public, operators and government, and comprehensively consider various factors affecting public transport subsidies, and establish a linkage mechanism of the subsidy based on passenger ridership, cost, fare and service quality. We propose a combination forecasting model (CFM) composed of quadratic exponential smoothing model (QESM) and Grey theory model (GM) to forecast passenger ridership. The results show that the CFM improves the forecast accuracy of passenger ridership, the factors considered in the linkage mechanism are more comprehensive, and the results are reasonable. By using this linkage mechanism, the government or operators can establish long-term or mid-term development plans to provide a systematic and theoretical reference, which is conducive to the healthy and stable development of public transport. [ABSTRACT FROM AUTHOR]

China has implemented a series of long-term measures to control the spread of COVID-19, however, the effects of these measures on other chronic and acute respiratory infectious diseases remain unclear. Tuberculosis (TB) and scarlet fever (SF) serve as representatives of chronic and acute respiratory infectious diseases, respectively. In China's Guizhou province, an area with a high prevalence of TB and SF, approximately 40,000 TB cases and hundreds of SF cases are reported annually. To assess the impact of COVID-19 prevention and control on TB and SF in Guizhou, the exponential smoothing method was employed to establish a prediction model for analyzing the influence of COVID-19 prevention and control on the number of TB and SF cases. Additionally, spatial aggregation analysis was utilized to describe spatial changes in TB and SF before and after the COVID-19 outbreak. The parameters of the TB and SF prediction models are R2 = 0.856, BIC = 10.972 and R2 = 0.714, BIC = 5.325, respectively. TB and SF cases declined rapidly at the onset of COVID-19 prevention and control measures, with the number of SF cases decreasing for about 3–6 months and the number of TB cases remaining in decline for 7 months after the 11th month. The spatial aggregation of TB and SF did not change significantly before and after the COVID-19 outbreak but exhibited a marked decrease. These findings suggest that China's COVID-19 prevention and control measures also reduced the prevalence of TB and SF in Guizhou. These measures may have a long-term positive impact on TB, but a short-term effect on SF. Areas with high TB prevalence may continue to experience a decline due to the implementation of COVID-19 preventive measures in the future. [ABSTRACT FROM AUTHOR]

Cross-border e-commerce has gradually emerged with the popularity of the Internet in the world. It represents a new business model of e-commerce. The public is still the same as the daily shopping model on e-commerce platforms. However, cross-border e-commerce can be divided into two parts at home and abroad. With the continuous emergence of new social models such as the Metaverse, the data generated by online social networking is becoming larger and larger, and data science has developed accordingly, and the effective use of data has become the key to the rapid development of various industries. Big data technology is the key to data science. A data processing and analysis technology generated in the development process, its use in the development of cross-border e-commerce enterprises will help enterprises to create products that are more in line with the actual requirements of the masses, increase the profits of enterprise operations, and allow enterprises to complete the era of data science smooth transition. This paper first describes the big data technology and related theories of cross-border e-commerce, and then takes Alibaba as an example to describe the SWOT analysis of my country's cross-border e-commerce enterprises, and then analyzes the development dilemma of China's cross-border e-commerce enterprises, and finally designs The development strategy of my country's cross-border e-commerce enterprises based on big data is presented. Through a questionnaire survey of cross-border e-commerce enterprises that use big data technology in my country, it is found that cross-border e-commerce enterprises that combine big data technology have a greater understanding of overseas customers' consumption psychology, integration of warehousing and logistics resources, and talent training. promote. [ABSTRACT FROM AUTHOR]

To reasonably ensure good groundwater quality for the safety of users, it is necessary to monitor the evolution of physicochemical parameters of water. This study assessed the evolution of groundwater quality in Xianyang City (Central China) for 6 years and forecasted the water quality index (WQI) using the automatic exponential smoothing model (AESM). The results revealed that pH, Mn, NH4+, HCO3− and Ca2+ increased significantly within 6 years. However, there was a slight decrease in K+, NO3−, SiO2, and Mg2+. Over the course of 6 years, seven out of fourteen wells maintained excellent or good water quality, whereas groundwater quality in the remaining wells fluctuated from excellent to good or from good to poor. The WQI index increased for nine wells, and five of them including well 173, well 457, well 46, well 26, and well C4 manifested an exponential increase of WQI, which was mainly caused by high concentrations of Mn, NH4+, and NO3−. The forecast accuracy verified by MASE and sMAPE, which ranged from 0.35 to 1.06 and from 0.04 to 0.69, respectively, demonstrated that the predictions were reliable. The AESM was tested in groundwater quality prediction, indicating that it might be used for actual scientific studies. [ABSTRACT FROM AUTHOR]

Landslide disasters cause serious property losses and casualties every year. Landslide displacement prediction is fundamental for mitigating landslide disasters. Several approaches have been used to predict landslide displacement, yet a more accurate and reliable displacement prediction still has a poor understanding of landslide early warning systems for landslide mitigation, due to limited data and mutational displacements. To boost the robustness and accuracy of landslide displacement prediction, this paper assembled a new hybrid model containing the local mean decomposition (LMD), innovations state space models for exponential smoothing (ETS), and the temporal convolutional network (TCN). The proposed model, which is based on over 10 years of long-term time series monitoring GPS data, was tested on the selected case—stepwise Baijiabao landslide in the Three Gorges Reservoir area of China (TGRA) was tested by the proposed model. The results presented that the LMD–ETS–TCN model has the best performance in comparison with other benchmark models. Compared with autoregressive integrated moving average (ARIMA), support vector regression (SVR), and long short-term memory neural network (LSTM), the accuracy was noticeably improved by an average of 40.9%, 46.2%, and 22.1%, respectively. The robustness and effectiveness of the presented approach are attested, and it has discernible improvements for landslide displacement prediction. [ABSTRACT FROM AUTHOR]

Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (SNPP-VIIRS) data are valuable records of nighttime lights (NTLs) in analyzing socioeconomic development. However, inconsistencies between these data have severely restricted long time-series analyses. Published time-series NTL data sets are not widely available or accurate because the DMSP-OLS calibration is inadequate and some missing data in the SNPP-VIIRS data are seldom considered for patching. To address these issues, we calibrated DMSP-OLS data (1992–2013) by using a quadratic model based on a “pseudo-invariant pixel” method. Thereafter, an exponential smoothing model was used to predict and patch missing data in the monthly SNPP-VIIRS data (2013–2019). Outliers and noise were also removed from the annual data. In addition, a sigmoid model was employed to generate improved simulated DMSP-OLS (SDMSP-OLS) data (2013–2019), which were appended with the calibrated DMSP-OLS data (1992–2013) to develop improved DMSP-OLS-like data (1992–2019) in China. Finally, we qualitatively and quantitatively compared these data with published NTL data to examine data availability. Results showed that choosing invariant pixels to calibrate DMSP-OLS data can minimize discontinuity. The correlation between the SNPP-VIIRS data synthesized by the patched monthly SNPP-VIIRS data and the official annual SNPP-VIIRS data in 2015 ($R^{2} =0.931$) and 2016 ($R^{2} =0.930$) was higher than those of the two existing correction methods with $R^{2}$ values below 0.90. Spatial patterns of pixels in the improved SDMSP-OLS data in 2013 were more similar with the DMSP-OLS data than those in the published data. Strong correlations likewise existed between the total (average) pixel values of the improved SDMSP-OLS data (2013–2019) and the DMSP-OLS data in 2012. We also found that the improved DMSP-OLS-like data held strong linear correlations with different statistics, the average $R^{2}$ values of which were 0.931 and 0.654 at the national and provincial levels, respectively. Meanwhile, the average regression $R^{2}$ values between the two published data sets and statistics were 0.858/0.506 and 0.911/0.611, respectively. Our study has proven that the improved DMSP-OLS-like data (1992–2019) have immense potential to effectively evaluate socioeconomic development and anthropic activities. [ABSTRACT FROM AUTHOR]

With the rapid development of air transportation, the complexity, importance, and severity of civil aviation safety have gradually become prominent. It is essential to use various data to analyze and predict the level of aviation safety. This paper used a combined prediction model based on Induced Ordered Weighted Averaging (IOWA) operator to forecast the civil aviation incident rate. We compiled and calculated civil aviation incident data and total flight hours from 2008 to 2019 in China and took the civil aviation incident rate (incident numbers per ten thousand flight hours) as the prediction object. First, this paper used the nonlinear regression model, Grey Verhulst model, and Holt-Winters exponential smoothing model to forecast the civil aviation incident rate individually. Then, it used the smallest sum of squared errors as the principle to use a combined prediction model based on the IOWA operator. It can be seen from the experimental results that the prediction accuracy of the combined model is better than single models. Finally, this paper forecasted the civil aviation incident rate in 2020 and 2021. The results showed that the predicted rates are 0.524 and 0.551. Most notably the incident rate will increase significantly compared with 2019. [ABSTRACT FROM AUTHOR]

Soil visible-near-infrared reflection (Vis-NIR; 350 to 2500 nm) spectra is a comprehensive reflection of various soil physical and chemical properties, resulting in significant differences of hyperspectral characteristics and diversity of soil property prediction models. Calcium carbonate regulates several soil properties that were widely used to describe soil types and to quantify vulnerability to erosion. Therefore, spectral-based determination of soil calcium carbonate content is essential for agricultural management and environmental evaluation. Based on their reflectance similarity, the soil Vis-NIR spectra data itself can be classified into different spectral groups to predict soil calcium carbonate. To obtain the best hyperspectral quantitative prediction model for soil calcium carbonate based on soil group spectral data and spectral group data, a total of 246 soil samples were collected from seven soil types in Shaanxi province, China, and the soil Vis-NIR spectra were measured. All the soil spectral data were smoothed using Savitzky-Golay and continuum removal method. In this paper, we combined spectral angle cosine (SAC) algorithm with spectral correlation coefficient (SCC) algorithm to classify soil spectra. The results indicated that the coefficient of correlations between measured and predicted soil properties for seven prediction models based on random regression forest (RFR) were low (Ratio of performance to deviation, RPD < 2.00). When soil spectra were classified into three types based on the SAC-SCC spectral similarity measurement method, accuracy of all the prediction models increased significantly (R2 ≥ 0.96; RPD > 2.00) compared with the individual soil type models. These results provide a novel method for soil spectral classification and soil attribute estimation. [ABSTRACT FROM AUTHOR]

Objective: Hemorrhagic fever with renal syndrome (HFRS), one of the main public health concerns in mainland China, is a group of clinically similar diseases caused by hantaviruses. Statistical approaches have always been leveraged to forecast the future incidence rates of certain infectious diseases to effectively control their prevalence and outbreak potential. Compared to the use of one base model, model stacking can often produce better forecasting results. In this study, we fitted the monthly reported cases of HFRS in mainland China with a model stacking approach and compared its forecasting performance with those of five base models. Method: We fitted the monthly reported cases of HFRS ranging from January 2004 to June 2019 in mainland China with an autoregressive integrated moving average (ARIMA) model; the Holt-Winter (HW) method, seasonal decomposition of the time series by LOESS (STL); a neural network autoregressive (NNAR) model; and an exponential smoothing state space model with a Box-Cox transformation; ARMA errors; and trend and seasonal components (TBATS), and we combined the forecasting results with the inverse rank approach. The forecasting performance was estimated based on several accuracy criteria for model prediction, including the mean absolute percentage error (MAPE), root-mean-squared error (RMSE) and mean absolute error (MAE). Result: There was a slight downward trend and obvious seasonal periodicity inherent in the time series data for HFRS in mainland China. The model stacking method was selected as the best approach with the best performance in terms of both fitting (RMSE 128.19, MAE 85.63, MAPE 8.18) and prediction (RMSE 151.86, MAE 118.28, MAPE 13.16). Conclusion: The results showed that model stacking by using the optimal mean forecasting weight of the five abovementioned models achieved the best performance in terms of predicting HFRS one year into the future. This study has corroborated the conclusion that model stacking is an easy way to enhance prediction accuracy when modeling HFRS. [ABSTRACT FROM AUTHOR]

The high demand of the competitive market for innovation has brought the increase of research and development (R&D) investment. High-tech enterprises can reasonably control R&D cost and effectively manage R&D activities by accurately predicting R&D investment. Given the characteristics that high-tech enterprises have high uncertainty and frequently changing information in R&D investment, this paper uses the grey metabolic GM (1, 1) model and the exponential smoothing method in time series to establish a single prediction model of R&D investment in high-tech enterprises. With the analysis of the advantages and disadvantages of each single model, a combined forecast model of R&D investment in high-tech enterprises is thus established. The model was applied to the forecast of R&D investment of a high-tech enterprise in China from 2019 to 2023, and the results verified the higher accuracy and practicability of this model. The establishment of this model can provide effective support for high-tech enterprises in R&D cost management. [ABSTRACT FROM AUTHOR]

We know that SARS-Cov2 produces the new COVID-19 disease, which is one of the most dangerous pandemics of modern times. This pandemic has critical health and economic consequences, and even the health services of the large, powerful nations may be saturated. Thus, forecasting the number of infected persons in any country is essential for controlling the situation. In the literature, different forecasting methods have been published, attempting to solve the problem. However, a simple and accurate forecasting method is required for its implementation in any part of the world. This paper presents a precise and straightforward forecasting method named SVR-ESAR (Support Vector regression hybridized with the classical Exponential smoothing and ARIMA). We applied this method to the infected time series in four scenarios, which we have taken for the Github repository: the Whole World, China, the US, and Mexico. We compared our results with those of the literature showing the proposed method has the best accuracy. [ABSTRACT FROM AUTHOR]

The solution of Bernoulli differential equation can be described as a generalized Logistic curve function. Weibull cumulative distribution function is also an analytic solution of one variable coefficient differential equation. But, however because of the exact analytical solution problem of the equation, up to now, the developing coefficient of all the grey forecasting models is still defined as a constant. So, the aim of this paper is just to construct a novel grey differential equation model by combining NGBM(1,1) and Weibull cumulative distribution function. The proposed model(WBGM(1,1)) has the advantages of NGBM(1,1) and Weibull cumulative distribution. Where there are double shape parameters, the developing coefficient of the grey forecasting model is extended to be a variant. Property analysis of WBGM(1,1) shows that the fitting accuracy is higher and the applicable confines are wider. Finally, this paper gives an optimization method for the parameters of WBGM(1,1). A classic example and a practical case are studied for confirming the effectiveness of WBGM(1,1). The case study is the prediction for the number of invention patents of integrated circuit(IC) filed in China from 2007 to 2017. Results of the example and case study are compared to other forecasting models, including GM(1,1), NGBM(1,1), Holt exponential smoothing and ARIMA. Results show that WBGM(1,1) is a more general and more efficient model in grey prediction theory. [ABSTRACT FROM AUTHOR]

[Objective] Estimating soil total potassium content quickly and accurately is very important to precision agriculture. [Method]Based on soil spectral data and soil total potassium content data obtained from laboratory analysis, the relationship between soil spectral and soil total potassium content was studied. The smooth spectral data of soil total potassium were treated with the first derivative, second derivative and reciprocal logarithmic to select the spectral indicators with the highest correlation with soil total potassium content. Finally, a model was established to predict soil total potassium content. [Result] Spectral variables based on first derivative are the best spectral index for estimating soil total potassium content. The predicted accuracy of hyperspectral inversion model(y=2E+06x2+11328x+16. 372) was the best with R2 of 0. 64 and RMSE of 4. 850 g/kg. [Conclusion] It is feasible to rapidly estimate the soil total potassium content of Guangdong Province by using this model. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • The intrinsic complexity of electrical energy consumption forecasting is analyzed. • A forecasting model that integrates EMD, MAF, LSSVR and QES is established. • The overall process of the EMD based hybrid ensemble model is discussed. • Effects of the EMD-LSSVR&QES hybrid ensemble model are examined. Abstract Forecasting the electrical energy consumption of the cement grinding process remains a difficult task due to the intrinsic complexity and irregularity of its time series. To solve this difficulty and improve the prediction accuracy, a novel hybrid model is proposed based on the "decomposition-prediction-integration" methodology. The hybrid model integrates empirical mode decomposition (EMD), moving average filter (MAF), least squares support vector regression (LSSVR), and quadratic exponential smoothing (QES). And it is suitable for non-stationary time series. The proposed model is tested using hourly electrical energy consumption data of one cement grinding process in China. EMD is first applied to decompose the original data series into a limited number of independent intrinsic mode functions (IMFs) and a trend component. Then, MAF is used to reduce the high frequency noises in the IMFs. Next LSSVR is adopted to predict different IMFs, and QES is utilized to predict the trend component. At last, the predicted IMFs and trend component are summed to formulate an ensemble forecast for the original series. The performance of the EMD-LSSVR&QES hybrid model is compared with five other forecasting models. Experiment results indicate that the proposed hybrid ensemble model can give full play to the advantages of each algorithm and outperform other forecasting models. [ABSTRACT FROM AUTHOR]

It is appropriate to apply the forecasting model based on grey theory when the relevant data are hard to come by, nm-linear and non-normal. An extended version of the NGBM(1,1) is addressed, which simultaneously takes adjustable hyper-parameters such as power exponent, smoothing factor of background value, selection of initial conditions and scaling factor of residual modification into consideration. We then apply the procedures of hyper-parameter optim: nation and hyper-parameter screening using the genetic algorithm (GA) and the 2k factorial design so as to alleviate the problems of manual selection of hyper-parameters and over-fitting, respectively. The resulting model is called an adaptive NGBM(1,1) which does not deviate from the simple ideas of grey theory. In this study, a preliminary comparative analysis is conducted in two benchmark sequences and two real-world sequences for China's energy consumption. The results of this analysis are used to simulate fluctuating and smooth data conditions for evaluation purposes. The experimental results suggest that the high-precision adaptive NGBM(I,1) can potentially improve the effectiveness of decision making. [ABSTRACT FROM AUTHOR]

Background: Accurate forecasting of hospital outpatient visits is beneficial for the reasonable planning and allocation of healthcare resource to meet the medical demands. In terms of the multiple attributes of daily outpatient visits, such as randomness, cyclicity and trend, time series methods, ARIMA, can be a good choice for outpatient visits forecasting. On the other hand, the hospital outpatient visits are also affected by the doctors' scheduling and the effects are not pure random. Thinking about the impure specialty, this paper presents a new forecasting model that takes cyclicity and the day of the week effect into consideration.Methods: We formulate a seasonal ARIMA (SARIMA) model on a daily time series and then a single exponential smoothing (SES) model on the day of the week time series, and finally establish a combinatorial model by modifying them. The models are applied to 1 year of daily visits data of urban outpatients in two internal medicine departments of a large hospital in Chengdu, for forecasting the daily outpatient visits about 1 week ahead.Results: The proposed model is applied to forecast the cross-sectional data for 7 consecutive days of daily outpatient visits over an 8-weeks period based on 43 weeks of observation data during 1 year. The results show that the two single traditional models and the combinatorial model are simplicity of implementation and low computational intensiveness, whilst being appropriate for short-term forecast horizons. Furthermore, the combinatorial model can capture the comprehensive features of the time series data better.Conclusions: Combinatorial model can achieve better prediction performance than the single model, with lower residuals variance and small mean of residual errors which needs to be optimized deeply on the next research step. [ABSTRACT FROM AUTHOR]

The exponential smoothing methods with the grey model and the generalized Wengshi model have been introduced in the present article. Two new methods for forecasting the yield of natural gas in China have also pointed out based on the exponential smoothing method with the grey model and the generalized Wengshi model. The experimental results show that both models can predict the yield of natural gas in China and the experimental data are in agreement with the quantitatively analytical conclusions drawn from the calculated data. This proves that two types of new models can be used to forecast the yield of natural gas. It results in good economic and social benefits in China. [ABSTRACT FROM PUBLISHER]

Analyzing the drivers of energy demand and predicting energy consumption can help to shape national policies on energy transformation and energy security. This paper estimates the demand for the various forms of energy consumed in China. A least absolute shrinkage and selection operator-random forest (Lasso-RF) two-stage model is first proposed to identify the drivers of the demand in energy. A support vector regression-compositional data second exponential smoothing (SVR-CDSES) model is then established to forecast the demand for coal, oil, natural gas, and primary electricity. The empirical analysis results inform that China's appetite for energy in the next decade will grow at an annualised rate of 2.68%. The demand for coal will rise initially and then fall at a rate of 0.79% per annum. The annual growth rate in the demand for oil will be relatively stable at 3.52%. However, the demand for natural gas and primary electricity will increase rapidly, with the highest annual growth rate at 8.05%, pointing to greater awareness of clean energy. • Study the drivers of China's energy demand and the trend of energy demand. • A new two-stage model is presented to identify the drivers in energy demand. • A new compound Support Vector Regression model is proposed to predict energy demand. • Some managerial insights and policy guidelines for energy demand are provided. [ABSTRACT FROM AUTHOR]

For the search engine, error-input query is a common phenomenon. This paper uses web log as the training set for the query error checking. Through the n-gram language model that is trained by web log, the queries are analyzed and checked. Some features including query words and their number are introduced into the model. At the same time data smoothing algorithm is used to solve data sparseness problem. It will improve the overall accuracy of the n-gram model. The experimental results show that it is effective. [ABSTRACT FROM AUTHOR]

Purpose: Electric vehicles industry has gotten a rapid development in the world, especially in the developed countries, but still has a gap among different countries or regions. The advanced industrialization experiences of the EVs in the developed countries will have a great helpful for the development of EVs industrialization in the developing countries. This paper seeks to research the industrialization path & prospect of American EVs by forecasting electric vehicles demand and its proportion to the whole car sales based on the historical 37 EVs monthly sales and Cars monthly sales spanning from Dec. 2010 to Dec. 2013, and find out the key measurements to help Chinese government and automobile enterprises to promote Chinese EVs industrialization. Design/methodology/approach: Compared with Single Exponential Smoothing method and Double Exponential Smoothing method, Triple exponential smoothing method is improved and applied in this study. Findings: The research results show that: (1) American EVs industry will keep a sustained growth in the next 3 months. (2) Price of the EVs, price of fossil oil, number of charging station, EVs technology and the government market & taxation polices have a different influence to EVs sales. So EVs manufacturers and policy-makers can adjust or reformulatesome technology tactics and market measurements according to the forecast results. And (3) China can learn from American EVs polices and measurements to develop Chinese EVs industry. Originality/value: The main contribution of this paper is to use the triple exponential smoothing method to forecast the electric vehicles demand and its proportion to the whole automobile sales, and analyze the industrial development of Chinese electric vehicles by American EVs industry. [ABSTRACT FROM AUTHOR]

• Yield monitoring system design. • Yield reconstruction model. • Analysis of spatiotemporal variability. An impact-type grain yield monitoring system was developed and tested in laboratory and field to meet the requirement of precision agriculture in China. The system consisted of a mass flow sensor, a clean elevator speed sensor, a ground speed sensor, a moisture content sensor, a controller area network-bus processor, a global navigation satellite system receiver, and an industrial personal computer. The system was used to collect yield data and create the grid map in a field of winter wheat from 2018 to 2019. Laboratory and field calibrations were implemented to verify the accuracy of the mass flow sensor and the moisture content sensor. The errors of time delay and parking were removed, and exponential smoothing was performed in the data preprocessing. A yield reconstruction method was proposed to calculate the yield data of the sampling points based on the empirical model between flow voltage and grain weight. The predicted errors of total yield in both years were less than 5%, which indicated that the accuracy of yield monitoring system was high. An averaging method was used to generate the yield grid map. The spatiotemporal variation of the two-year yield was analyzed. Hierarchical management for precision farming was studied according to spatial variability ( CV S = 31%) in 2018. Results showed that the temporal variability was CV T = 34%, which confirmed that the yield increased by about 0.3 t compared with the previous year. Moreover, the variability of the grid map showed the distribution of the yield reduction area, which was related to the characteristics of the seeds and the regional properties of the soil. The results proved that the developed yield monitoring system can be used as a tool to provide a reference for precision agriculture and high-yield management of winter wheat. [ABSTRACT FROM AUTHOR]

This paper compares the performance of five popular machine learning methods, namely, particle swarm optimization–extreme learning machine (PSO–ELM), particle swarm optimization–kernel extreme learning machine (PSO–KELM), particle swarm optimization–support vector machine (PSO–SVM), particle swarm optimization–least squares support vector machine (PSO–LSSVM), and long short-term memory neural network (LSTM), in the prediction of reservoir landslide displacement. The Baishuihe, Shuping, and Baijiabao landslides in the Three Gorges reservoir area of China were used for case studies. Cumulative displacement was decomposed into trend displacement and periodic displacement by the Hodrick–Prescott filter. The double exponential smoothing method and the five machine learning methods were used to predict the trend and periodic displacement, respectively. The five machine learning methods are compared in three aspects: highest single prediction accuracy, mean prediction accuracy, and prediction stability. The results show that no method performed the best for all three aspects in the three landslide cases. LSTM and PSO–ELM achieved better single prediction accuracy, but worse mean prediction accuracy and stability. PSO–KELM, PSO–LSSVM, and PSO–SVM always yielded consistent predictions with slight variations. On the whole, PSO–KELM and PSO–LSSVM are recommended for their superior mean prediction accuracy and prediction stability. • The performances of machine learning methods depend on the selected evaluation index and reservoir landslide case. • Using single prediction accuracy for evaluating the superiority of machine learning methods may be unreliable. • PSO-KELM and PSO-LSSVM are recommended for their superior mean prediction accuracy and prediction stability. [ABSTRACT FROM AUTHOR]

To accurately forecast energy consumption plays a vital part in rational energy planning formulation for a country. This study applies individualmodels (BP, GM(1, 1), triple exponential smoothingmodel, and polynomial trend extrapolationmodel) and combination forecastingmodels to predict China's energy consumption. Since area correlation degree (ACD) can comprehensively evaluate both the correlation and fitting error of forecastingmodel, it ismore effective to evaluate the performance of forecastingmodel. Firstly, the forecastingmodel's performances rank in line with ACD. Then ACD is firstly proposed to choose individualmodels for combination and determine combination weight in this paper. Forecast results show that combination models usually have more accurate forecasting performance than individual models. The new method based onACDshows its superiority in determining combination weights, compared with some other combination weight assignment methods such as: entropy weight method, reciprocal of mean absolute percentage error weight method, and optimal method of absolute percentage error minimization. By using combination forecasting model based on ACD, China's energy consumption will be up to 5.7988 billion tons of standard coal in 2018. [ABSTRACT FROM AUTHOR]

Summary: This paper examines inter-provincial consumption risk sharing and intertemporal consumption smoothing across Chinese provinces before and after the 1979 economic reform. Our results indicate that the degree of consumption risk sharing among Chinese provinces is lower than that within the United States and across the national boundaries of OECD countries. On the other hand, the level of consumption smoothing among Chinese provinces is higher than that across OECD or European Union countries, but lower than that in the United States. Moreover, our results show that consumption risk sharing and smoothing in China have deteriorated since the 1979 economic reform. Finally, we show that eliminating consumption fluctuations yields substantial welfare gains, which suggests that stabilization policies are desirable for China. [ABSTRACT FROM AUTHOR]

Resources and environment provide the fundamental conditions for human beings to live. Coordinating the relationship between resources and environmental protection and economic development is a necessary measure for humans to pursue sustainable development. To explore the Haihe River basin's economic and resource carrying capacity development, 26 indexes from 2000 to 2016 were selected based on pressure state response model selection. Through the TOPSIS model, the entropy method, the exponential smoothing method, and the Hurst index, the comprehensive capacity of the Haihe River is constructed. This study aims to quantify each carrying capacity of the Haihe River basin and analyse their levels and characteristics, with an emphasis on the trade-off analysis between economic carrying capacity and resource and environmental carrying capacity; finally, the study aims to predict the development of each type of carrying capacity. The results showed that in the Haihe River basin, there's dramatic change in comprehensive carrying capacity's level and subsystems from 2000 to 2016. The resource and environmental carrying capacity have negative correlation with economic carrying capacity, while a positive correlation between talent carrying capacity and economic carrying capacity. Based on prediction, from 2017 to 2021, the level of development of each capacity subsystem indicated an increase in sustainability. This research provides a trade-off analysis between the economic and resource and environmental carrying capacity. The study also offers a theoretical basis to the coordinated development of various carrying capacities, which has reference significance for promoting the sustainable development of the river basin. Image 1 • Explored the characteristics and development trends of the carrying capacity systems from the perspective of time series. • The trade-off relationship between economic and resource and environmental carrying capacity had been analysed. • Predict the development of socioeconomic carrying capacity and resource and environmental carrying capacity. • Added the talent carrying capacity into the construction of the carrying capacity system. [ABSTRACT FROM AUTHOR]

Background: The World Health Organization End TB Strategy meant that compared with 2015 baseline, the reduction in pulmonary tuberculosis (PTB) incidence should be 20 and 50% in 2020 and 2025, respectively. The case number of PTB in China accounted for 9% of the global total in 2018, which ranked the second high in the world. From 2007 to 2019, 854 672 active PTB cases were registered and treated in Henan Province, China. This study was to assess whether the WHO milestones could be achieved in Henan Province. Methods: The active PTB numbers in Henan Province from 2007 to 2019, registered in Chinese Tuberculosis Information Management System were analyzed to predict the active PTB registration rates in 2020 and 2025, which is conductive to early response measures to ensure the achievement of the WHO milestones. The time series model was created by monthly active PTB registration rates from 2007 to 2016, and the optimal model was verified by data from 2017 to 2019. The Ljung-Box Q statistic was used to evaluate the model. The statistically significant level is α = 0.05. Monthly active PTB registration rates and 95% confidence interval (CI) from 2020 to 2025 were predicted. Results: High active PTB registration rates in March, April, May and June showed the seasonal variations. The exponential smoothing winter's multiplication model was selected as the best-fitting model. The predicted values were approximately consistent with the observed ones from 2017 to 2019. The annual active PTB registration rates were predicted as 49.1 (95% CI: 36.2–62.0) per 100 000 population and 34.4 (95% CI: 18.6–50.2) per 100 000 population in 2020 and 2025, respectively. Compared with the active PTB registration rate in 2015, the reduction will reach 23.7% (95% CI, 3.2–44.1%) and 46.8% (95% CI, 21.4–72.1%) in 2020 and 2025, respectively. Conclusions: The high active PTB registration rates in spring and early summer indicate that high risk of tuberculosis infection in late autumn and winter in Henan Province. Without regard to the CI, the first milestone of WHO End TB Strategy in 2020 will be achieved. However, the second milestone in 2025 will not be easily achieved unless there are early response measures in Henan Province, China. [ABSTRACT FROM AUTHOR]

Electricity consumption forecasting is essential for intelligent power systems. In fact, accurate forecasting of monthly consumption to predict medium- and long-term demand substantially contributes to the appropriate dispatch and management of electric power systems. Most existing studies on monthly electricity consumption forecasting require large datasets for accurate prediction, which is severely undermined when scarce data are available. However, in practical scenarios, data is not always sufficient, thereby hindering the accurate forecasting of monthly electricity consumption. The Holt–Winters exponential smoothing allows to accurately forecast periodic series with relatively few training samples. Based on this method, we propose a hybrid forecasting model to predict electricity consumption. The fruit fly optimization algorithm is used to select the best smoothing parameters for the Holt–Winters exponential smoothing. We used electricity consumption data from a city in China to comprehensively evaluate the forecasting performance of the proposed model compared to similar methods. The results indicate that the proposed model can substantially improve the prediction accuracy of monthly electricity consumption even when few training samples are available. Moreover, the computation time of the proposed model is the shortest among the evaluated hybrid benchmark algorithms. • It is the first attempt to enhance the Holt–Winters exponential smoothing method. • A high forecasting accuracy can be obtained by FOA-MHW method for the monthly electricity consumption. • The proposed FOA-MHW method can obtain incredible performance even in the situation of insufficient training data. • The proposed FOA-MHW model has the shortest execution time among with all the other hybrid models. [ABSTRACT FROM AUTHOR]

Reliable energy consumption prediction plays a vital role in formulating government's energy policies. In this study, a novel multiple decomposition-ensemble method based on error compensation is proposed to stably predict energy consumption. Firstly, trend decomposition is used to decompose the energy consumption into trend-subseries and error-subseries. In view of error compensation is a feasible approach for improving the prediction accuracy, the error-subseries that mentioned above are further divided into one low-frequency approximation error-subseries and several high-frequency detailed error-subseries by wavelet transform. Depending on their different dynamic changing characteristics, this study uses a Linear Regression Model to predict the trend-subseries, employs a Triple Exponential Smoothing Model to estimate the low-frequency approximation error-subseries, and uses an Auto Regression model to find the high-frequency detailed error-subseries. Finally, the overall energy consumption is the summation of these subseries predictions. Using the energy consumption data from China in 2007–2016, empirical study is carried out. The proposed multiple decomposition-ensemble method based on error compensation achieves the highest performance, which is compared with other six models (three single models, two traditional decomposition-ensemble models, and combination model). The proposed model is also validated by the U.S. data. Forecasts indicate that the energy demand of China will increase to 4.957343 billion Tons of standard Coal Equivalent in 2021, implying that China should speed up its transition to an energy-efficiency economy. • A novel prediction model based on multiple decomposition-ensemble method is built. • Error compensation is a feasible approach for improving the prediction accuracy. • Multiple decomposition-ensemble method improves the accuracy of error compensation. • Energy consumptions of China and U.S. are forecasted to demonstrate these methods. • The proposed model achieves the highest performance compared with other six models. [ABSTRACT FROM AUTHOR]

As product variety is an important feature for modern enterprises, multi-product demand forecasting is essential to support order decision-making and inventory management. However, these well-established forecasting approaches for multi-dimensional time series, such as Vector Autoregression (VAR) or dynamic factor model (DFM), all cannot deal very well with time series with high or ultra-high dimensionality, especially when the time series are short. Considering that besides the demand trends in historical data, that of associated products (including highly correlated ones or ones having significantly causality) can also provide rich information for prediction, we propose new forecasting approaches for multiple products in this study. The demand of associated products is treated as predictors to add in AR model to improve its prediction accuracy. If there are many time series associated with the object, we introduce two schemes to simplify variables to avoid over-fitting. Then procurement data from a grid company in China is applied to test forecasting performance of the proposed approaches. The empirical results reveal that compared with four conventional models, namely single exponential smoothing (SES), autoregression (AR), VAR and DFM respectively, the new approaches perform better in terms of forecasting errors and inventory simulation performance. They can provide more effective guidance for actual operational activities. [ABSTRACT FROM AUTHOR]