Integration of maximum crop response with machine learning regression model to timely estimate crop yield     

Qiming Zhou  Ali Ismaeel 《地球空间信息科学学报》2021,24(3):474-483

Timely and reliable estimation of regional crop yield is a vital component of food security assessment, especially in developing regions. The traditional crop forecasting methods need ample time and labor to collect and process field data to release official yield reports. Satellite remote sensing data is considered a cost-effective and accurate way of predicting crop yield at pixel-level. In this study, maximum Enhanced Vegetation Index (EVI) during the crop-growing season was integrated with Machine Learning Regression (MLR) models to estimate wheat and rice yields in Pakistan's Punjab province. Five MLR models were compared using a fivefold cross-validation method for their predictive accuracy. The study results revealed that the regression model based on the Gaussian process outperformed over other models. The best performing model attained coefficient of determination (R2), Root Mean Square Error (RMSE, t/ ha), and Mean Absolute Error (MAE, t/ha) of 0.75, 0.281, and 0.236 for wheat; 0.68, 0.112, and 0.091 for rice, respectively. The proposed method made it feasible to predict wheat and rice 6– 8 weeks before the harvest. The early prediction of crop yield and its spatial distribution in the region can help formulate efficient agricultural policies for sustainable social, environmental, and economic progress.  相似文献   

基于影像交叉学习的CBERS CCD波段模拟   总被引：1，自引：0，他引：1  

俞乐  曹凯  吴飏  张登荣 《国土资源遥感》2011,(3):48-53

与TM/ETM+相比,CBERS CCD缺少2个红外波段(波段5和波段7),这便导致了许多针对TM/ETM+数据的图像处理方法难以直接应用于CBERS CCD图像.为此,采用基于影像交叉学习的波段模拟方法,即以ETM+数据作为先验知识,通过支持向量回归( Support Vector Regression,SVR),拟...  相似文献   

Can we improve the spatial predictions of seabed sediments? A case study of spatial interpolation of mud content across the southwest Australian margin     

Jin Li  Andrew D. HeapAnna Potter  Zhi HuangJames J. Daniell 《Continental Shelf Research》2011,31(13):1365-1376

Spatially continuous data of environmental variables is often required for marine conservation and management. However, information for environmental variables is usually collected by point sampling, particularly for the marine region. Thus, methods generating such spatially continuous data by using point samples to estimate values for unknown locations become essential tools. Such methods are, however, often data- or even variable-specific and it is difficult to select an appropriate method for any given dataset. In this study, 14 methods (37 sub-methods) are compared using samples of mud content with five levels of sample density across the southwest Australian margin. Bathymetry, distance-to-coast, slope and geomorphic province were used as secondary variables. Ten-fold cross validation with relative mean absolute error (RMAE) and visual examination were used to assess the performance of these methods. A total of 1850 prediction datasets are produced and used to assess the performance of the methods and the effects of other factors considered. Considering both the accuracy and the visual examination, we found that a combined method (i.e., random forest and ordinary kriging: RKrf) is the most robust method. This method is novel, with a RMAE up to 17% less than that of the control. No threshold in sample density was detected in relation to prediction accuracy. No consistent patterns are observed between the performance of the methods and data variation. The RMAE of three most accurate methods is about 30% lower than that of the best methods in previous publications, highlighting the robustness of the methods selected in this study. The implications and limitations of this study are discussed and a number of suggestions are provided for further studies.  相似文献   

支持向量机优化的克里金插值算法及其海洋资料对比试验     

王辉赞  张韧  刘巍  刘科峰  王桂华 《南京气象学院学报》2011,34(5):567-573

针对常规克里金插值方法中变异函数为有限确定函数,难以准确刻画实际数据分布（特别是复杂要素资料的空间结构）的不足,基于最小二乘支持向量机从实际资料场中拟合重构变异函数的研究思想,提出了一种改进的插值方法——支持向量机—克里金插值算法（SVM-Kriging）。采用常规的克里金方法和支持向量机—克里金插值方法（SVM-Kriging）进行插值试验和对比分析,结果表明：支持向量机—克里金方法（SVM-Kriging）的变异函数源自实际的数据场,它克服了常规克里金插值对变异函数选择的依赖性以及变异函数选择的主观性和人为性,表现出较好的针对性和客观性,较为有效地改善了插值效果。  相似文献   

基于支持向量机的CloudSat卫星云分类算法     

韩丁  严卫  任建奇  赵现斌 《南京气象学院学报》2011,34(5):583-591

从基于云角色的分类思想出发,利用星载毫米波雷达探测资料提取云的特征参数,建立支持向量机（support vector machine,SVM）模型实现云的分类。通过与BP（back propagation）网络模型的分类结果进行对比,发现两种模型都具有较好的分类能力,但SVM模型的识别准确率更高,计算速度更快。基于CloudSat资料的云分类实例表明,SVM模型的分类结果与CloudSat数据处理中心（Data Processing Center,DPC）发布产品具有很好的一致性。  相似文献   

基于机器学习的复杂地形下短期数值天气预报误差分析与订正     

任萍  陈明轩  曹伟华  王在文  韩雷  宋林烨  杨璐 《气象学报》2020,78(6):1002-1020

初步研发了一套基于机器学习方法XGBoost且考虑地形特征影响的数值预报多模式集成技术,并与传统的等权重平均和线性回归方法的集成效果进行了对比分析。利用北京地区快速更新循环数值预报系统每天8次循环预报给出的近地面2 m温度、2 m相对湿度、10 m风速、10 m风向数据产品,分别基于机器学习方法XGBoost、等权重平均方法、线性回归方法构建了3种体现地形因子影响的多模式预报时间滞后集成模型。试验对比分析了暖季、冷季每日不同时刻的模式预报集成订正效果。结果表明:分季节试验中,基于XGBoost模型对2 m温度、10 m风速的集成预报结果相对原始最优预报结果误差明显优于其他两种传统方法。XGBoost对2 m温度集成的误差可降低11.02%—18.09%,10 m风速集成误差可降低31.23%—33.22%,10 m风向集成误差可降低4.1%—8.23%。2 m相对湿度的集成预报误差与传统方法接近。基于XGBoost的多模式集成预报模型可以充分“挖掘”不同模式或不同时刻快速更新循环预报优点,有效降低模式的系统性误差,提供准确性更高的多模式集成确定性预报产品。   相似文献   

Reconstruction and prediction of the total solar irradiance: From the Medieval Warm Period to the 21st century     

《New Astronomy》2015

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天空云量预报及支持向量机和神经网络方法比较研究   总被引：11，自引：2，他引：11  

熊秋芬  胡江林  陈永义 《热带气象学报》2007,23(3):255-260

使用支持向量机和人工神经网络两种方法,分别建立了天空云量的预报模型。利用2001年5月1日～2004年12月31日的武汉市地面、高空观测值及欧洲中心的24小时预报场等资料,通过按不同比例随机抽取样本进行交叉验证的方法,分析了SVM和ANN模型的预报能力和鲁棒性;然后再用全部样本资料建立预报模型,来预报2005年1月1日～5月31日武汉市天空云量。交叉验证和实例预报的结果显示：虽然SVM和ANN模型都表现了较好的预报能力,但SVM的预报能力高于ANN方法,且在计算速度上有ANN无法比拟的优势。  相似文献   

The performance of inclined secant micro-pile walls as active vibration barriers     

《Soil Dynamics and Earthquake Engineering》2013

The isolation of vibrations from the surroundings is one of the important problems in the design of machine foundations. The use of open trenches, infilled trenches, single and multiple pile rows have been widely studied. In this paper, the vibration screening efficiency of an inclined secant micro-pile wall positioned as an active vibration barrier is investigated. The study is performed using three-dimensional time domain finite element analyses. Various parameters such as barrier depth, inclination, barrier distance and load excitation frequency were studied. The results show that inclined secant micro-pile walls are a viable vibration isolation option for a multitude of vibration problems. It is shown that varying barrier inclination angle from 90° to 120° improved vibration isolation performance as high as 44% relative to the vertical barrier for the active isolation case. The effectiveness of the barrier increases as its depth increases and also as the excitation frequency increases. The orientation of the inclined barrier towards or against vibration source is shown to be a fundamental design consideration.  相似文献   

Data-driven topo-climatic mapping with machine learning methods   总被引：1，自引：1，他引：0  

A. Pozdnoukhov  L. Foresti  M. Kanevski 《Natural Hazards》2009,50(3):497-518

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.  相似文献