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The 'Huangguan' pear is one of the high-quality pear cultivars produced in China. However, the bagged fruit of the 'Huangguan' pear often suffers from peel browning spots after rain during their mature period. In this study, in an effort to discover the impact of bagging treatments on the occurrence of peel browning spots and fruit quality, fruits were covered by single-layer, two-layer, or triple-layer paper bags six weeks after reaching full bloom. The results showed that the bagged fruits were characterized by smooth surfaces and reduced lenticels compared with the unbagged ones. The unbagged and the two-layer bagged fruits had yellow/green peels, while the single- and triple-layer bagged ones had yellow/white peels. Compared with the unbagged fruits, the bagged fruits had higher vitamin C (Vc) contents and values of peel color indexes L and a and lower soluble solid contents (SSCs), titratable acid (TA) contents, absorbance index differences (IAD), and b values. Additionally, the triple-layer bagged group was superior to other groups in terms of fruit quality, but it also had the maximum incidence of peel browning spots. Before and after the appearance of peel browning spots, the bagged fruits had smoother and thinner cuticles compared with the unbagged ones. Furthermore, the triple-layer bagged fruits had minimum lignin contents and maximum phenolic contents in their peels, with minimum activity of lignin synthesis-related enzymes such as phenylalanine ammonia lyase (PAL), peroxidase (POD), and polyphenol oxidase (PPO), as well as minimum expressions of relevant genes such as cinnamyl alcohol dehydrogenase (CAD), cinnamoyl CoA reductase (CCR), 4-coumarate: coenzyme A ligase (4CL6), and cinnamate 4-hydroxylase (C4H1). It was deduced that POD activity and the relative expressions of CAD9, CCR3, CCR4, and CCR5 may play key roles in the occurrence of peel browning spots. In summary, lignin synthesis affected the incidence of peel browning spots in bagged 'Huangguan' pears. This study provides a theoretical basis for understanding the incidence of peel browning spots in 'Huangguan' pears. [ABSTRACT FROM AUTHOR]

Photovoltaic power generation prediction constitutes a significant research area within the realm of power system artificial intelligence. Accurate prediction of future photovoltaic output is imperative for the optimal dispatchment and secure operation of the power grid. This study introduces a photovoltaic prediction model, termed ICEEMDAN-Bagging-XGBoost, aimed at enhancing the accuracy of photovoltaic power generation predictions. In this paper, the original photovoltaic power data initially undergo decomposition utilizing the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) algorithm, with each intrinsic mode function (IMF) derived from this decomposition subsequently reconstructed into high-frequency, medium-frequency, and low-frequency components. Targeting the high-frequency and medium-frequency components of photovoltaic power, a limiting gradient boosting tree (XGBoost) is employed as the foundational learner in the Bagging parallel ensemble learning method, with the incorporation of a sparrow search algorithm (SSA) to refine the hyperparameters of XGBoost, thereby facilitating more nuanced tracking of the changes in the photovoltaic power's high-frequency and medium-frequency components. Regarding the low-frequency components, XGBoost-Linear is utilized to enable rapid and precise prediction. In contrast with the conventional superposition reconstruction approach, this study employs XGBoost for the reconstruction of the prediction output's high-frequency, intermediate-frequency, and low-frequency components. Ultimately, the efficacy of the proposed methodology is substantiated by the empirical operation data from a photovoltaic power station in Hebei Province, China. Relative to integrated and traditional single models, this paper's model exhibits a markedly enhanced prediction accuracy, thereby offering greater applicational value in scenarios involving short-term photovoltaic power prediction. [ABSTRACT FROM AUTHOR]

With the orderly advancement of China Energy Development Strategic Action Plan, clean energy has become a major trend in the energy market. As a major industry of clean energy, natural gas industry plans to consume at least 10% of the total primary energy by 2020. The energy structure will be improved in an orderly manner to achieve the goal of energy conservation, consumption reduction, and emission reduction. To achieve energy saving and emission reduction, and using clean energy effectively, accurate prediction of natural gas consumption is of great importance. Because of the many influencing factors affecting natural gas demand, this paper first utilizes STRIPAT to analyze the factors affecting natural gas consumption and then uses a deep learning ensemble approach to analyze and predict China's natural gas consumption. One is an advanced deep neural network model named gated recurrent unit model which is used to model the nonlinear and complex relationships of natural gas consumption with its factors. The other is a powerful ensemble method named bootstrap aggregation which generates multiple data sets for training a set of base models. Our approach combines the advantages of these two technologies to forecast the demand for China's natural gas market. In empirical research, our method has been tested by some competitive methods and has shown superiority. [ABSTRACT FROM AUTHOR]

Accurate and reliable low-voltage load forecasting is critical to optimal operation and control of distribution network and smart grid. However, compared to traditional regional load forecasting at high-voltage level, it faces tough challenges due to the inherent high uncertainty of the low-capacity load and distributed renewable energy integrated in the demand side. This paper proposes a novel hybrid ensemble deep learning (HEDL) approach for deterministic and probabilistic low-voltage load forecasting. The deep belief network (DBN) is applied to low-voltage load point prediction with the strong ability of approximating nonlinear mapping. A series of ensemble learning methods including bagging and boosting variants are introduced to improve the regression ability of DBN. In addition, the differencing transformation technique is utilized to ensure the stationarity of load time series for the application bagging and boosting methods. On the basis of the integrated thought of ensemble learning, a new hybrid ensemble algorithm is developed via integrating multiple separate ensemble methods. Considering the diversity in various ensemble algorithms, an effective K nearest neighbor classification method is utilized to adaptively determine the weights of sub-models. Furthermore, HEDL based probabilistic forecasting is proposed by taking advantage of the inherent resample idea in bagging and boosting. The effectiveness of the HEDL method for both deterministic and probabilistic forecasting has been systematically verified based on realistic load data from East China and Australia, indicating its promising prospective for practical applications in distribution networks. [ABSTRACT FROM AUTHOR]

Landslide hazards have attracted increasing public attention over the past decades due to a series of catastrophic consequences of landslide occurrence. Thus, the mitigation and prevention of landslide hazards have been the topical issues. Thereinto, numerous research achievements on landslide susceptibility assessment have been springing up in recent years. In this paper, four benchmark models including best-first decision tree (BFTree), functional tree, support vector machine and classification regression tree (CART) and were integrated with bagging strategy. Then, these bagging-based models were applied to map regional landslide susceptibility in Jiange County, Sichuan Province, China. Fifteen conditioning factors were employed in establishing landslide susceptibility models, respectively, slope aspect, slope angle, elevation, plan curvature, profile curvature, TWI, SPI, STI, lithology, soil, land use, NDVI, distance to rivers, distance to roads and distance to lineaments. Then utilize correlation attribute evaluation method to weigh the contribution of each factor. Finally, the comprehensive performance of various bagging-based models and corresponding benchmark models was evaluated and systematically compared applying receiver operating characteristic curve and area under curve (AUC) values. Results demonstrated that bagging-based ensemble models significantly outperformed their corresponding benchmark models with validation dataset. Among them the Bag-CART model has the highest AUC value of 0.874; however, the AUC value of CART model is only 0.766, which reflected satisfying predictive capacity of integrated models in some degree. The achievements obtained in this study have some reference values for landslides prevention and land resource planning in Jiange County. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to use ensembles techniques of functional tree-based bagging, rotation forest, and dagging (functional trees (FT), bagging-functional trees (BFT), rotation forest-functional trees (RFFT), dagging-functional trees (DFT)) for landslide susceptibility modeling in Zichang County, China. Firstly, 263 landslides were identified, and the landslide inventory map was established, and the landslide locations were randomly divided into 70% (training data) and 30% (validation data). Then, 14 landslide conditioning factors were selected. Furthermore, the correlation analysis between conditioning factors and landslides was applied using the certainty factor method. Hereafter, four models were applied for landslide susceptibility modeling and zoning. Finally, the receiver operating characteristic (ROC) curve and statistical parameters were used to evaluate and compare the overall performance of the four models. The results showed that the area under the curve (AUC) for the four models was larger than 0.74. Among them, the BFT model is better than the other three models. In addition, this study also illustrated that the integrated model is not necessarily more effective than a single model. The ensemble data mining technology used in this study can be used as an effective tool for future land planning and monitoring. [ABSTRACT FROM AUTHOR]

[Objective] The present paper aimed to explore the effects of different types of fruit bags on the formation of fruit quality and the change of peel pigment of Qiumitao No. 1, so as to provide reference for the high-quality production of Qiumitao No. 1 in southern China. [Method] Taking the fruit of Qiumitao No. 1 as the experimental material, four different types of fruit bags (yellow outside and black inside, single yellow bag, single white bag, single semi white translucent bag) were bagged after the first physiological fruit drop. The changes of pigment content in single fruit weight, vertical and horizontal diameters, fruit size, peel pigment, and pulp glyc acid content and relative enzyme activities were measured and analyzed every 7 days (5 times), before plucking fruit, and the change rule was analyzed. [Result] The results showed that the surface of fruit covered with yellow outside and black inside double-layer bag was smooth, the chlorophyll content was significantly lower than that of single-layer bag and showing white jade color (P < 0. OS, the same as below), the longitudinal diameter, transverse diameter and single fruit weight were higher than those of the other three treatments, and the content of quinic acid, malic acid, sucrose, sorbitol and total sugar of fruit covered with yellow outside and black inside double-layer bag were significantly lower than those of other single-layer bag treatments, which match with the light taste at maturity. Among the three single-layer bags, the yellow singlelayer bag had higher sucrose and fructose content, lower citric acid and quinic acid content, the highest total sugar content, the lowest total acid content and the highest sugar acid ratio. The results of correlation analysis showed that there was no significant correlation between the acid invertase activity and neutral invertase activity with sucrose content at late stage of fruit development (P > 0. OS). [Conclusion] Qiumitao No. 1 bagged with double-layer yellow outside and black inside has low pigment content and beautiful appearance, but the content of sugar and acid was lower, and the taste was lighter; the peel pigment content of Qiumitao No. 1 bagged with single-layer yellow fruit bag was higher than that of double-layer bag, and the fruit appearance was green and dull, but the content of sugar and acid in the fruit was higher than that of other bagged fruits. In production and markting, the type of fruit bag can be selected according to the needs of sales, which can improve the overall economic benefits. [ABSTRACT FROM AUTHOR]

Explicit spatial information about crop types on smallholder farms is important for the development of local precision agriculture. However, due to highly fragmented and heterogeneous cropland landscapes, fine-scale mapping of smallholder crops, based on low- and medium-resolution satellite images and relying on a single machine learning (ML) classifier, generally fails to achieve satisfactory performance. This paper develops an ensemble ML-based framework to improve the accuracy of parcel-level smallholder crop mapping from very high spatial resolution (VHSR) images. A typical smallholder agricultural area in central China covered by WorldView-2 images is selected to demonstrate our approach. This approach involves the task of distinguishing eight crop-level agricultural land use types. To this end, six widely used individual ML classifiers are evaluated. We further improved their performance by independently implementing bagging and stacking ensemble learning (EL) techniques. The results show that the bagging models improved the performance of unstable classifiers, but these improvements are limited. In contrast, the stacking models perform better, and the Stacking #2 model (overall accuracy = 83.91%, kappa = 0.812), which integrates the three best-performing individual classifiers, performs the best of all of the built models and improves the classwise accuracy of almost all of the land use types. Since classification performance can be significantly improved without adding costly data collection, stacking-ensemble mapping approaches are valuable for the spatial management of complex agricultural areas. We also demonstrate that using geometric and textural features extracted from VHSR images can improve the accuracy of parcel-level smallholder crop mapping. The proposed framework shows the great potential of combining EL technology with VHSR imagery for accurate mapping of smallholder crops, which could facilitate the development of parcel-level crop identification systems in countries dominated by smallholder agriculture. [ABSTRACT FROM AUTHOR]

In this paper, an intelligent ensemble machine learning (EML) method - Bagging was developed for thermal perception prediction. Field data was collected in naturally ventilated (NV) and split air-conditioning (SAC) dormitory buildings in hot summer and cold winter (HSCW) area of China during the summer of 2016. The indoor physical measurement and subjective survey were conducted simultaneously during the field study. To determine the merit of the proposed Bagging approach, the performances of Bagging approach were compared against the artificial neural network (ANN) and support vector machine (SVM) regarding conventional statistical indicators, i.e., mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R 2 ) and Pearson correlation coefficient (r). Several thermal indices, i.e., thermal sensation (TS), effective temperature (ET*), standard effective temperature (SET*) and predicted mean vote (PMV), were adopted to access the occupants’ thermal comfort and evaluate the model prediction. In our case study, the Bagging model's R 2 for TS, PMV, ET* and SET* were 0.4986, 0.9892, 0.9920 and 0.9900, respectively. It shows higher accuracy than SVM and ANN models in thermal perception prediction and outperforms the classical PMV index in TS prediction. Results indicate the proposed Bagging model's prediction performance is reliable and is highly accurate to predict the thermal perception. [ABSTRACT FROM AUTHOR]

A novel machine learning ensemble model that is a hybridization of Bagging and random subspace–based naïve Bayes tree (RSNBtree), named as BRSNBtree, was used to prepare a landslide susceptibility map for Zigui County of the Three Gorges Reservoir Area, China. The proposed method is implemented by using the Bagging scheme to integrate the base-level RSNBtree model. To predict landslide susceptibility for the study area, a spatial database consisted of 807 landslides and 11 conditioning factors has been prepared. Evaluation of conditioning factors was conducted using the Pearson correlation coefficient and Relief-F method. The results indicate that all factors except the topographic wetness index can be accepted as modeling inputs. Particularly, the distance to rivers is the most important factor in landslide susceptibility prediction. The performance of landslide models was evaluated using statistical indices and areas under the receiver operatic characteristic curve (AUC). The support vector machines (SVM) and random forest (RF) were adopted for the comparison with our methods. Results show that the BRSNBtree (AUC = 0.968) achieves the highest prediction performance, which successfully refines the RSNBtree (AUC = 0.938) and outperforms the RF (AUC = 0.949) and SVM (AUC = 0.895). Therefore, the proposed BRSNBtree presents advantages in targeting landslide susceptible areas and provides a promising method for landslide susceptibility assessment. The developed susceptibility maps could facilitate effective landslide risk management for this landslide-prone area. [ABSTRACT FROM AUTHOR]

Compared with the limited capability of ground-level monitoring, remote sensing provides useful image data at a moderate or high spatial or temporal resolution with global coverage for monitoring of air pollutants, e.g., aerosol optical depth (AOD) observations from the MODIS for fine particulate matter (PM 2.5) and Ozone Monitoring Instrument (OMI) nitrogen dioxide (NO 2) vertical columns for ground-level NO 2 concentration. However, the extensive nonrandom missingness of OMI-NO 2 data (e.g., an approximate per-pixel missing proportion of 59% for mainland China in 2015) due to cloud contamination or high reflectance limits applicability of these data in estimation of ground-level NO 2. This paper proposes the use of a full residual deep learning method to impute missing satellite-borne NO 2 data (OMI-NO 2) and to estimate (map) ground-level NO 2 with uncertainty (coefficient of variation) at a high spatial (1 × 1 km2) and temporal (daily) resolution. For the large study region (mainland China except Hainan Province), the presented method achieved robust performance with a stable learning efficiency (mean test R2: 0.98 with a small standard deviation of 0.01; mean test RMSE: 0.42 × 1015 molecules/cm2) for imputation of OMI-NO 2. In the model, the coordinates and elevation were used to capture the spatial variability of the OMI-NO 2 columns, and fused meteorological grid data and planetary boundary layer height and ozone data from GEOS-FP were used to capture spatiotemporal variability of OMI-NO 2. The evaluation with ground in situ NO 2 measurements showed considerable contribution of the complete (raw observed and imputed) OMI-NO 2 columns, meteorology and traffic variables to inference of ground-level NO 2 (test R2: 0.82; test RMSE: 8.80 μg/m3). The complete grids of OMI-NO 2 columns showed natural and smooth spatial transitions between the raw observed and imputed values. The surfaces of predicted NO 2 concentration not only showed consistent distributions with OMI-NO 2 at a regional and temporal scale, but also presented local spatial gradients of ground-level NO 2. OMI-NO 2 can be downscaled and imputed to be used as an important predictor to improve the estimation of high-resolution ground-level NO 2. The reliable estimates of ground-level NO 2 concentration with uncertainty can reduce the bias in estimates of NO 2 exposure and subsequently evaluations of its health effects. Unlabelled Image • A coarse spatial resolution and extensive missingness limit applications of OMI-NO 2. • Full residual network was used to make reliable imputation of missing OMI-NO 2. • Traffic and land-use variables were used to capture local gradient of ground NO 2. • Bagging of base models was done to improve high-res nationwide prediction of NO 2. • Predicted NO 2 showed spatiotemporal distributions and local gradients. [ABSTRACT FROM AUTHOR]