Prediction of Winter Wheat Harvest Based on Back Propagation Neural Network Algorithm and Multiple Remote Sensing Indices

Purpose In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing. Design/methodology/approach In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing. Findings In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing. Originality/value In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.

Cross-cultural learning competence, a critical skill in our globally interconnected world, is advanced through the application of the Backpropagation (BP) neural network algorithm. This innovative approach involves leveraging neural network techniques to model and enhance individuals' abilities to navigate and understand diverse cultural contexts. The BP neural network algorithm facilitates personalized learning experiences by adapting to individuals' cultural backgrounds and preferences. This research explores a comprehensive approach for assessing and enhancing cross-cultural learning competence among Chinese college students, integrating the Word Embedding Multilingual Model with the Back Propagation Neural Network (WEMM-BPNN) algorithm. Recognizing the importance of global competencies in higher education, our study focuses on leveraging advanced neural network techniques to evaluate and elevate students' cross-cultural learning abilities. The WEMM-BPNN model combines the power of word embedding and multilingual considerations, tailoring the learning experience to individual cultural backgrounds. Through a meticulous analysis of cross-cultural data and linguistic patterns, the algorithm refines its recommendations for personalized learning strategies. The research aims not only to assess the current state of cross-cultural learning competence but also to provide targeted interventions to enhance students' intercultural understanding and adaptability. By merging linguistic models with neural network algorithms, this study offers a pioneering approach to cultivating cross-cultural competencies, contributing valuable insights to the ongoing discourse on globalized education.

The suitability of using Moderate Resolution Imaging Spectroradiometer (MODIS) images for surface soil moisture estimation to investigate the importance of soil moisture in different applications, such as agriculture, hydrology, meteorology and natural disaster management, is evaluated in this study. Soil moisture field measurements and MODIS images of relevant dates have been acquired. Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI) are calculated from MODIS images. In addition, MODIS Land Surface Temperature (LST) data (MOD11A1) are used in this analysis. Four different soil moisture estimation models, which are based on NDVI–LST, EVI–LST, NDVI–LST–NDWI and EVI–LST–NDWI, are developed and their accuracies are assessed. Statistical analysis shows that replacing EVI with NDVI in the model that is based on LST and NDVI increases the accuracy of soil moisture estimation. Accuracy evaluation of soil moisture estimation using check points shows that the model based on LST, EVI and NDWI values gives a higher accuracy than that based on LST and EVI values. It is concluded that the model based on the three indices is a suitable model to estimate soil moisture through MODIS imagery.

Forecasting and alert of atmospheric bioaerosol concentration profile based on adaptive genetic algorithm back propagation neural network, atmospheric parameter and fluorescence lidar

In order to improve the efficiency and accuracy of thermal and moisture comfort prediction of underwear, a new prediction model is designed by using principal component analysis method to reduce the dimension of related variables and eliminate the multi-collinearity relationship between variables, and then inputting the converted variables into genetic algorithm (GA) and BP neural network. In order to avoid the problems of slow convergence speed and easy falling into local minimum of Back Propagation (BP) neural network, this paper adopted GA to optimize the weights and thresholds of BP neural network, and utilized MATLAB software to program, and established the prediction models of BP neural network and GA–BP neural network. To verify the superiority of the model, the predicted result of GA–BP, PCA–BP and BP are compared with GA–BP neural network. The results show that PCA could improve the accuracy and adaptability of GA–BP neural network for thermal and moisture comfort prediction. PCA–GA–BP model is obviously superior to GA–BP, PCA–BP, BP, SVM and K-means prediction models, which could accurately predict thermal and moisture comfort of underwear. The model has better accuracy prediction and simpler structure.

The rapid advance of urbanization leads to growing construction waste. To achieve the goals of "peak carbon emissions and carbon neutrality", construction waste must be treated properly to reduce the consumption of non-renewable resources. In this paper, five general physical parameters that affect the compaction performance of the recycled material-the proportions of fine, medium, and coarse particles, moisture content, and the brick-concrete ratio-are selected to establish a prediction model based on the BP neural network through the BP neural network algorithm, based on the array test of a metro depot project. The results demonstrate that the relative errors between the fitted and predicted values of the BP neural network and the corresponding measured values fluctuated within small ranges, which indicates that it is feasible to apply the BP neural network to predict material performance.

With the rapid development of the economy and agricultural production, cotton is an indispensable crop in people's production and life, and the average annual cotton yield is particularly significant in production. The unit yield of cotton is affected by many factors, so it is extremely important to analyze the factors affecting future cotton yield forecast. This article varies seven relevant indicators, including fertilizer cost,consumption of agricultural plastic, pesticide cost, seed cost, irrigation cost, chlorpyrifos control area, and labor input, and establishes a prediction model based on Back Propagation (BP) neural network. The model uses the function approximation ability of BP neural network and gradient descent algorithm to predict the nonlinear functional relationship between the unit output and the seven factors. Meanwhile, a multiple linear regression model is used to forecast the unit yield and the prediction accuracy of the two methods for unit output is compared. The experimental results show that compared with multiple linear regression, BP neural network has higher accuracy in predicting cotton unit yield, which can be used as an idea for future prediction.

Internal leakage rate prediction and failure diagnosis of buried pipeline ball valve based on valve cavity pressure detection

HighlightsWe propose five spraying parameters according to the characteristics of pig carcasses in the spray-chilling process.A prediction model for pig carcass weight loss, based on a genetic algorithm back-propagation neural network, is proposed to reveal the relationship between weight loss and spraying parameters.To study the effects of various spraying parameters on weight loss, an automatic spray-chilling device was designed, which can modify up to five spraying parameters.Abstract. Because the weight loss of a pig carcass in the spray-chilling process is easily affected by the spraying frequency and duration, a prediction model for weight loss based on a genetic algorithm (GA) back-propagation (BP) neural network is proposed in this article. With three-way crossbred pig carcasses selected as the test materials, the duration and time interval of high-frequency spraying, the duration and time interval of low-frequency spraying, and the duration of a single spray were selected as inputs to the network model. The weight and threshold of the network were then optimized by the GA. The prediction model for pig carcass weight loss established by the GA BP neural network yielded a correlation coefficient of R = 0.99747 between the network output value of the test samples and the target value. Weight loss prediction by the model is feasible and allows better expression of the nonlinear relationship between weight loss and the main controlling factors. The results can be a reference for chilled meat production. Keywords: BP neural network, Genetic algorithm, Pig carcass, Predictive model, Weight loss

Pavement cracks are an important hidden danger for driving safety. Rapid and accurate detection of road crack types and effective treatment can greatly reduce the incidence of traffic accidents, which has important practical significance. Based on the advantages of scale invariant feature transform (SIFT) and back-propagation neural network (BP) algorithm, this paper proposes a method of road crack detection and recognition for four types of cracks, such as cracks, longitudinal cracks, transverse cracks and massive cracks.Firstly, the crack image is denoised and enhanced; Then scale invariant feature transform method is used to extract the feature point information of the route crack image; Finally, Back propagation neural network is used for training and recognition. The results show that this method can quickly and accurately identify the crack type, in which the accuracy of training set is 98.79%, and the accuracy of recognition set is 86.47%. The results show that the combination of scale invariant feature transform and back propagation neural network algorithm can effectively improve the efficiency of pavement crack type detection.

Carbon emission is a significant indicator to balance regional development and environmental pollution. Currently, to achieve the global goal of carbon neutrality, many countries had formulated relevant policies to control carbon emissions in the future. It is of great importance to solve the problem of mid-term and long-term prediction of regional carbon emissions. To improve the prediction accuracy and lower the calculation cost, this paper uses the Principal Component Analysis (PCA) method to reasonably screen the driving factors of carbon emission prediction. It combines the Back Propagation (BP) neural network and Genetic Algorithm (GA) to construct the Genetic Algorithm - Back Propagation neural network (GA-BP) model for mid-term and long-term prediction. Based on historical statistics and official policy indicators, the prediction of carbon emissions in Jiangsu Province of China from 2025 to 2050 is carried out. By comparing the prediction results of the Multiple Linear Regression (MLR) model and the classic BP model, the improved GA-BP model based on Principal Component Analysis Stochastic Impacts by Regression on Population, Affluence, and Technology (PCA-STIRPAT) is significantly better than other methods in the mid-term and long-term prediction of regional carbon emissions, with a maximum relative error of 0.76% and a high prediction accuracy. This result can provide a reasonable basis for relevant parties to make decisions on controlling carbon emissions.

Most nature and health research use the normalized difference vegetation index (NDVI) for measuring greenness exposure. However, little is known about what NDVI measures in terms of vegetation types (e.g., canopy, grass coverage) within certain analysis zones (e.g., 500 m buffer). Additionally, exploration is needed to understand how to interpret changes in average NDVI (e.g., per 0.1 increments) exposure in relation to changes in vegetation amount and types. In this study, we aimed to explore what vegetation types and amounts best explain the average NDVI and how changes in average NDVI values indicate changes in different vegetation coverages. We used spatial modeling to sample mean NDVI and percentages of vegetation for sample locations within the Greater Manchester case study area. We fitted linear, nonlinear, and mixed multivariate and univariate generalized additive models (GAMs) for multiple spatial scales to identify the relationships between NDVI and vegetation amount and types. Our results showed that the relationships between NDVI and individual vegetation types mostly follow nonlinear trends. We found that canopy and shrubs coverage exhibited a greater influence on mean NDVI exposure values than grass coverage at 300 and 500 m indicating that NDVI values are sensitive to certain types and amounts of vegetation within various buffer zones. We also identified increment in mean NDVI exposure values at lower, mid, and high ranges might be associated with varying changes in total greenspace percentage and individual vegetation types. For instance, at 300 m buffer, an increment of mean NDVI in the lower range (e.g., from 0.2 to 0.3) is associated with an about 17% increase in greenspace percentage. Overall, interpreting changes in NDVI values for urban greening interventions would require careful evaluation of the relative changes in types and quantities of vegetation for different buffer zones.

Drought is expected to increase in water bodies due to climate change. Monitoring long-term changes in wetlands is crucial for identifying fluctuations and conserving biodiversity. In this study, we assessed the long-term variability of remote sensing indicators in 25 watershed areas in Algeria known for their significant biodiversity. We employed two statistical methods, namely linear regression and the Mann–Kendall (MK) test, to capture long-term fluctuations by integrating data from various sources, including Modis and Landsat satellite data. A time-series dataset spanning 22 years was developed, consisting of the following indicators: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference water index (NDWI), normalized difference moisture index (NDMI), and land surface temperature (LST). We evaluated the relationships between these variables. The results indicated that NDVI exhibited a stronger temporal response compared to EVI, NDWI, and NDMI. Additionally, negative associations between NDVI and LST confirmed the impact of drought and plant stress on vegetation in the study areas (R2 = 0.109–R2 = 0.5701). The NDMI results pointed to water stress in the water bodies, showing a significant decreasing trend. The results from the MK trend analysis underscored the importance of NDVI and highlighted its strong association with EVI, NDWI, and NDMI. Understanding the dynamics of vegetation and water stress has become crucial for ecosystem forecasts.

PDF HTML阅读 XML下载 导出引用 引用提醒 2000—2018年黄河流域生长季植被指数变化及其对气候因子的响应 DOI: 10.5846/stxb202104271108 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点研发计划（2020YFA0608101）；国家自然科学基金（31872703，31570617） Changes of NDVI and EVI and their responses to climatic variables in the Yellow River Basin during the growing season of 2000-2018 Author: Affiliation: Fund Project: The National Basic Research Program of China (973 Program),The National Natural Science Foundation of China 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:黄河流域位于我国干旱、半干旱地区，生态环境脆弱，探究其植被指数变化和对气候因子的响应对该地区生态建设具有重要意义。基于黄河流域2000-2018年MODIS归一化植被指数、增加型植被指数和气象数据，利用最大值合成法、趋势分析和相关分析等方法，分析了两种植被指数的时空变化特征及受气候因子的影响机制，探讨了NDVI与EVI在反映植被变化和对气候因子响应的差异。结果表明：2000-2018年，黄河流域地区植被NDVI、EVI分别以0.059/10a、0.038/10a的变化率增加，空间上以显著改善为主，面积占比分别为77.13%和75.27%，大多分布在1000-1500 m海拔处，中游地区改善较为良好，林地改善率最高。显著退化区域较小，主要分布在巴颜喀拉山西北部、西宁市、银川市、包头市、呼和浩特市、太原市、西安市及关中盆地和洛阳市周边，建设用地退化率最高。在生长季期间，植被指数变化与气温和降水以正相关为主，气温滞后时间为1个月，降水滞后时间为3个月，都为草地最为相关；与辐射之间为负相关，滞后时间为3个月，其中林地最为相关。在0.05显著性检验水平下，驱动黄河流域生长季植被变化的主要气候因子为降水和辐射。整体上看降水对该地区植被变化影响较大。在时间和空间上，黄河流域地区NDVI比EVI反应植被变化更敏感，且与降水和辐射的相关性更高。 Abstract:The Yellow River Basin is located in the arid and semi-arid area of China, which ecological environment is fragile. The researches about vegetation index changes and its response to climate factors were significant to the ecological construction of the region. Based on the MODIS normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and meteorological data, we mainly used Maximum Value Composite (MVC), trend analysis, and partial correlation analysis to explore the characteristics of the spatio-temporal changes of vegetation cover and driving mechanism of climatic factors in the Yellow River Basin from 2000 to 2018. At the same time, the differences between the NDVI and EVI reflected variations of vegetation and their responses to climatic factors were discussed. Results showed that the temporal change rate of NDVI and EVI in the Yellow River Basin increased at 0.059/10 a and 0.038/10 a, respectively. In terms of space, the main areas significantly improved, accounting for 77.13% and 75.27% of the area, respectively. Most of them were distributed at 1000-1500 m elevations, and the middle reaches have improved well. The improvement rate of forest land was higher than that in other areas. The degraded areas were relatively rare and mainly distributed in the northwestern Bayan Qula Mountain, Xining City, Yinchuan City, Baotou City, Hohhot City, Taiyuan City, Xi'an City, the Guanzhong Basin and the surrounding areas of Luoyang City. The degradation rate of construction land was higher compared with other areas. During the growing season, the change of vegetation index (VI) was positively correlated with air temperature and precipitation, the lag time of air temperature in the VI was one month, while in precipitation the lag time were three months. Among them, grassland showed the most significantly positive correlation. There was a negative correlation between radiation and vegetation index, and the time lag was about 3 months, among which the most relevant area was in forest land. At the significant level of 0.05, the precipitation and radiation driven the vegetation change during the growing season in the Yellow River Basin. On the whole, precipitation had a great influence on the change of vegetation in this area. In terms of temporal and spatial changes, the NDVI was more sensitive to vegetation change than EVI, and had a higher correlation with precipitation and radiation in the Yellow River Basin. 参考文献 相似文献 引证文献

Wood plastic composites (WPCs) were prepared and used as pencil board. The primary raw materials were corn straw fiber powder, high-density polyethylene, and polystyrene. The tensile strength, surface roughness, hardness, and roll cutting performance of the prepared composites were tested and compared with those of linden wood, which is typically used for pencil board. A back propagation (BP) neural network was adopted to build the prediction model of process technical parameters of the composite. An improved particle swarm (modified particle swarm optimizer, MPSO) was adopted to optimize the BP neural network, and the advantages of the PSO algorithm’s global optimization ability and the BP neural network algorithm’s high processing speed were realized. The results show that the composite had the best performance, with a mass ratio of 3.8 (corn straw fiber powder): 47.6 (high-density polyethylene): 28.6 (polystyrene). The composites can be used as a substitute for linden wood in the production of pencil board.