Evaluation of machine learning models for rice dry biomass estimation and mapping using quad-source optical imagery     

Lamin R. Mansaray  Adam Sheka Kanu  Lingbo Yang  Jingfeng Huang  Fumin Wang 《地理信息系统科学与遥感》2020,57(6):785-796

ABSTRACT

Several machine learning regression models have been advanced for the estimation of crop biophysical parameters with optical satellite imagery. However, literature on the comparative performances of such models is still limited in range and scope, especially under multiple data sources, despite the potential of multi-source imagery to improving crop monitoring in cloudy areas. To fill in this knowledge gap, this study explored the synergistic use of Landsat-8, Sentinel-2A, China’s environment and disaster monitoring and forecasting satellites (HJ-1 A and B) and Gaofen-1 (GF-1) data to evaluate four machine learning regression models that include Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), and Gradient Boosting Decision Tree (GBDT), for rice dry biomass estimation and mapping. Taking a major rice cultivation area in southeast China as case study during the 2016 and 2017 growing seasons, a cross-calibrated time series of the Enhanced Vegetation Index (EVI) was obtained from the quad-source optical imagery and on which the aforementioned models were applied, respectively. Results indicate that in the before rice heading scenario, the most accurate dry biomass estimates were obtained by the GBDT model (R² of 0.82 and RMSE of 191.8 g/m²) followed by the RF model (R² of 0.79 and RMSE of 197.8 g/m²). After heading, the k-NN model performed best (R² of 0.43 and RMSE of 452.1 g/m²) followed by the RF model (R² of 0.42 and RMSE of 464.7 g/m²). Whist the k-NN model performed least in the before heading scenario, SVM performed least in the after heading scenario. These findings may suggest that machine learning regression models based on an ensemble of decision trees (RF and GBDT) are more suitable for the estimation of rice dry biomass, at least with optical satellite imagery. Studies that would extend the evaluation of these machine learning models, to other parameters like leaf area index, and to microwave imagery, are hereby recommended.  相似文献   

Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis     

Ahmad Sharafati  Seyed Babak Haji Seyed Asadollah  Davide Motta 《水文科学杂志》2020,65(12):2022-2042

ABSTRACT

Ensemble machine learning models have been widely used in hydro-systems modeling as robust prediction tools that combine multiple decision trees. In this study, three newly developed ensemble machine learning models, namely gradient boost regression (GBR), AdaBoost regression (ABR) and random forest regression (RFR) are proposed for prediction of suspended sediment load (SSL), and their prediction performance and related uncertainty are assessed. The SSL of the Mississippi River, which is one of the major world rivers and is significantly affected by sedimentation, is predicted based on daily values of river discharge (Q) and suspended sediment concentration (SSC). Based on performance metrics and visualization, the RFR model shows a slight lead in prediction performance. The uncertainty analysis also indicates that the input variable combination has more impact on the obtained predictions than the model structure selection.  相似文献   

Support vector machine applied to settlement of shallow foundations on cohesionless soils   总被引：4，自引：0，他引：4  

Pijush Samui   《Computers and Geotechnics》2008,35(3):419-427

The determination of settlement of shallow foundations on cohesionless soil is an important task in geotechnical engineering. Available methods for the determination of settlement are not reliable. In this study, the support vector machine (SVM), a novel type of learning algorithm based on statistical theory, has been used to predict the settlement of shallow foundations on cohesionless soil. SVM uses a regression technique by introducing an ε – insensitive loss function. A thorough sensitive analysis has been made to ascertain which parameters are having maximum influence on settlement. The study shows that SVM has the potential to be a useful and practical tool for prediction of settlement of shallow foundation on cohesionless soil.  相似文献   

基于主成分-正则化极限学习机的超高密度电法非线性反演   总被引：2，自引：0，他引：2       下载免费PDF全文

江沸菠  戴前伟  董莉 《地球物理学报》2015,58(9):3356-3369

超高密度电法是一种新的地球物理探测技术,它通过多通道数据采集和多装置数据联合反演,极大地提高了电法勘探的成像精度.本文提出一种主成分-正则化极限学习机(PC-RELM)非线性反演方法,该方法针对超高密度电法所获取的高维勘探数据进行反演建模,通过随机设定隐层参数来简化模型的学习过程,通过主成分分析方法来进行高维数据降维,最后引入正则化因子提高反演模型的泛化能力.论文给出了超高密度电法的原理、样本构造方法和非线性反演流程,使用交叉验证方法获得了优化的隐节点数目和正则化参数,构造了优化的反演模型.通过两个经典的超高密度模型的反演结果表明,该方法能够较好地解决超高密度电法反演的高维数据非线性建模问题,能够弥补单一装置数据反演的不足,同时相较其他的非线性反演方法(ELM,BPNN和GRNN)具有更加准确的反演结果.  相似文献   

分级加载下冻土阻尼比的试验研究     

罗飞  何依婷  赵淑萍  朱占元  毛磊 《岩土力学》2015,36(11):3143-3149

利用MTS-810型振动三轴试验机对不同加载频率、围压和温度下冻结黏土和冻结黄土的阻尼比变化规律进行研究。结果表明:不同频率、围压和温度下,对于冻结黏土,随动应变幅的增大,阻尼比呈先减小再缓慢增大的变化趋势;对于冻结黄土,阻尼比随动应变幅的增加先减小再逐渐保持不变。相同动应变幅下,冻结黏土和冻结黄土的阻尼比随加载频率的增加而减小,随围压的增加变化不大,温度为?0.2~?1℃时,阻尼比的变化规律不明显,温度为?2℃时,阻尼比的取值显著小于?0.2~?1℃时的阻尼比取值。当动应力水平较低时,阻尼比受频率的影响程度较大;当动应力水平较高时,阻尼比受温度的影响程度较大;在整个加载过程中,阻尼比受围压的影响程度最小。当动应变幅较小时,频率对冻结黄土的影响程度要大于冻结黏土,随动应力水平的增高,冻结黄土受频率的影响程度逐渐小于冻结黏土。在整个加载过程中,温度对冻结黄土的影响程度大于冻结黏土,而围压对冻结黄土的影响程度小于冻结黏土。  相似文献   

基于支持向量机的GPS似大地水准面拟合   总被引：14，自引：0，他引：14  

吴兆福  宫鹏  高飞  王侬 《测绘学报》2004,33(4):303-306

GPS高精度平面控制成果在各类工程中已经得到了广泛应用,然而其高程信息目前正在作进一步的研究.介绍统计学习理论和支持向量机,提出利用支持向量机技术进行似大地水准面拟合.以实测GPS定位数据为试验资料,对支持向量机和神经网络以及多项式拟合的结果进行比较分析:支持向量机技术拟合数据的精度达到了神经网络和多项式拟合的精度,并且解决了神经网络技术不能实时处理数据、过学习、收敛速度慢、易陷于局部极值等问题.  相似文献   

Multi-step prediction of strong earthquake ground motions and seismic responses of SDOF systems based on EMD-ELM method     

《Soil Dynamics and Earthquake Engineering》2016

This paper proposes a new multi-step prediction method of EMD-ELM (empirical mode decomposition-extreme learning machine) to achieve the short-term prediction of strong earthquake ground motions. Firstly, the acceleration time histories of near-fault ground motions with nonstationary property are decomposed into several components of intrinsic mode functions (IMFs) with different characteristic scales by the technique of EMD. Subsequently, the ELM method is utilized to predict the IMF components. Moreover, the predicted values of each IMF component are superimposed, and the short-term prediction of ground motions is attained with low error. The predicted results of near-fault acceleration records demonstrate that the EMD-ELM method can realize multi-step prediction of acceleration records with relatively high accuracy. Finally, the elastic and inelastic acceleration, velocity and displacement responses of single degree of freedom (SDOF) systems are also predicted with satisfactory accuracy by EMD-ELM method.  相似文献   

全断面岩石掘进机滚刀最优刀间距计算公式研究     

谭青  易念恩  夏毅敏  朱逸  张旭辉  林赉贶 《岩土力学》2016,37(3):883-892

基于剪切与张拉破岩机制,提出拉剪综合失效机制假设;建立了非协同切削与协同切削体积模型,推导了协同切削模式的滚刀比能耗理论模型;通过离散元仿真得到滚刀侵入岩石裂纹长度与贯入度的映射关系;推导出滚刀最优刀间距理论公式;以全断面岩石掘进机（TBM）回转切削试验台为基础,水泥模拟料为切削对象,通过多次压痕试验得到滚刀贯入度和岩石裂纹长度的趋势曲线,验证了仿真结果;通过滚刀滚动破岩进行了12组不同贯入度与刀间距的物理切削试验,统计滚刀做功与岩石破碎体积,拟合得到比能耗关系曲线,验证了最优刀间距理论公式的结论。研究表明：TBM滚刀最优刀间距计算公式综合考虑了岩石特性与滚刀结构特性,适应性较广泛;刀间距超过滚刀协同工作距离时,滚刀破岩以剪切破碎为主;刀间距小于2倍岩石裂纹长度时,张拉破碎的影响更加明显;随着贯入度的增加,最优刀间距逐渐增大,而最优滚刀间距S与贯入度P之比（S/P）值则逐渐减小。  相似文献   

塔里木河下游土地利用覆被MISR多角度遥感制图   总被引：1，自引：0，他引：1  

杨雪峰  王雪梅  毛东雷 《吉林大学学报(地球科学版)》2016,46(2):617-626

通过对塔里木河下游MISR卫星多角度观测数据的不同组合构建多角度数据集,探索多角度观测与传统垂直观测对土地利用覆被遥感制图效果的影响,分别使用SVM(支持向量机)与传统的MLC(最大似然分类法)作为分类器,对分类后得到的混淆矩阵进行分析。结论证实:无论是使用传统的MLC还是SVM作为分类器,多角度观测都取得比垂直观测更高的总体分类精度;MISR近红外波段虽然分辨率较低,但依然含有丰富的信息,对地表覆被的分类有重要影响;无论使用哪一数据集,SVM法都能获得更高的分类精度;不同相机对分类结果的影响各不相同,其中C、D相机的作用更重要。  相似文献   

地理时空大数据协同计算技术     

骆剑承  胡晓东  吴炜  王博 《地球信息科学学报》2016,18(5):590-598

大数据时代,地理时空数据的迅猛增长给应用理念、技术框架和服务形式带来挑战。本文在阐述地理时空大数据概念的基础上,首先分析了地理时空大数据计算面临的挑战,从数据协同、技术协同、服务协同和生产协同4个层次阐述了地理时空大数据协同计算方法;然后,根据平台化服务的需求设计了地理时空大数据协同计算框架,从遥感数据综合预处理、地理时空数据的组织与管理、地理时空大数据高效计算、地理时空大数据可视化4个方面论述了地理时空大数据协同计算实现的关键技术;最后,以遥感大数据综合处理系统作为案例说明了地理时空大数据协同计算与服务的实现方法,并对地理时空大数据的应用模式进行了展望。  相似文献