Parameterization of the Ångström–Prescott formula based on machine learning benefit estimation of reference crop evapotranspiration with missing solar radiation data

Accurately estimated reference evapotranspiration (ET₀) is essential to regional water management. The FAO recommends coupling the Penman–Monteith (P-M) model with the Ångström–Prescott (A-P) formula as the standard method for ET₀ estimation with missing R_s measurements. However, its application is usually restricted by the two fundamental coefficients (a and b) of the A-P formula. This paper proposes a new method for estimating ET₀ with missing R_s by combining machine learning with physical-based P-M models (PM-ET₀). The benchmark values of the A-P coefficients were first determined at the daily, monthly, and yearly scales, and further evaluated in R_s and ET₀ estimates at 80 national R_s measuring stations. Then, three empirical models and four machine-learning methods were evaluated in estimating the A-P coefficients. Machine learning methods were also used to estimate ET₀ (ML-ET₀) to compare with the PM-ET₀. Finally, the optimal estimation method was used to estimate the A-P coefficients for the 839 regular weather stations for ET₀ estimation without R_s measurement for China. The results demonstrated a descending trend for coefficient a from northwest to southeast China, with larger values in cold seasons. However, coefficient b showed the opposite distribution as the coefficient a. The FAO has recommended a larger a but a smaller b for southeast China, which produced the region's largest R_s and ET₀ estimation errors. Additionally, the A-P coefficients calibrated at the daily scale obtained the best estimation accuracy for both R_s and ET₀, and slightly outperformed the monthly and yearly coefficients without significant difference in most cases. The machine learning methods outperformed the empirical methods for estimating the A-P coefficients, especially for the sites with extreme values. Further, ML-ET₀ outperformed the PM-ET₀ with yearly A-P coefficients but underperformed those with daily and monthly ones. This study indicates an exciting potential for combining machine learning with physical models for estimating ET₀. However, we found that using the A-P coefficients with finer time scales is unnecessary to deal with the missing R_s measurements.     

发现学习及其在《测量学》教学中的应用研究

本文运用现代教学理论,采用"发现学习"的教学方法进行《测量学》教学。首先论述发现学习的概念,指出采用发现学习的方法进行《测量学》教学是课程内在内容的需要。提出发现学习的《测量学》教学模式,并结合具体教学内容进行分析。从问题教学策略、情境教学策略、信息教学策略及作业教学策略四个方面进行教学策略研究。     

深度递归残差网络的遥感图像空谱融合

为了充分利用多光谱图像的光谱信息和全色图像的空间信息,本文提出一种基于深度递归残差网络的遥感图像空谱融合方法.方法 将残差网络和递归网络相结合,利用残差网络学习低空间分辨率多光谱图像与高空间分辨率多光谱图像之间的残差,同时结合全局残差和局部残差,加快网络的收敛速度,解决深层次网络容易出现的梯度消失和梯度爆炸问题;利用递...     

PRB数字填图技术在区域地质调查中的应用——以1∶25万济宁市临沂市幅区域地质调查为例

在1：25万济宁市、临沂市幅区域地质调查中采用了PRB数字填图技术，其效果良好。实现了野外地质观察图、文、像的数字化；极大地改变和优化了传统地质资料的精度与管理方式；大幅度地提高了地质人员对区域地质观察结果的广度和深度的概括能力；大大缩短了填图周期。     

类书的起源及其产生发展的原因探析

类书是一种特殊的文献编纂形式,它的真正起源应该是《皇览》。在文献聚集、文献利用、文学创作、帝王影响、文献编纂、学术思想等因素的共同作用下,类书得以产生与发展。类书是中国传统学术、文化与思想发展到魏晋南北朝时期的必然产物。     

基于遗传算法的多光谱影像非监督训练分类系统

本文将遗传算法（GA）应用于非监督训练,提高了遥感数据的分类精度。遗传竞争学习算法（GA-CL）综合了遗传算法和简单的竞争学习算法,可用于改进非监督训练的结果。遗传算法在典型样本聚类的过程中可以避免得到局部最优值。Jeffries-Matusita（J-M）距离法是通过统计测量两个训练类别之间的分离度,可用于评价这种算法。将此算法应用于TM数据的结果显示,遗传算法改进了简单的竞争学习算法,与其他非监督训练算法相比,其提供了K-均值,GA-K-均值和简单的竞争学习算法。     

Strength of ensemble learning in multiclass classification of rockburst intensity

Rockbust is a violent expulsion of rock due to the extreme release of strain energy stored in surrounding rock mass, leading to considerable damages to underground strucures and equipment, and threatening workers' safety. As the operational depth of engineering projects increases, a larger number of factors influence the mechanism of rockburst. Therefore, accurate classification of rockburst intensity cannot be achieved based on conventional criteria. It is urgent to develop new models with high accuracy and ease to implement in practice. This study proposed an ensemble machine learning method by aggregating seven individual classifiers including back propagation neural network, support vector machine, decision tree, k-nearest neighbours, logistic regression, multiple linear regression and Naïve Bayes. In addition, we proposed nine data imputation methods to replace the missing values in the compiled database including 188 rockburst instances. Five-fold cross validation and the beetle antennae search algorithm are used to tune hyperparameters and voting weights of the individual classifiers. The results show that the rockburst classification accuracy obtained by the classifier ensemble has increased by 15.4% compared with the best individual classifier on the test set. The predictor importance obtained by the classifier ensemble shows that the elastic energy index is the most sensitive input variable for rockburst intensity classification. This robust ensemble method can be extended to solve other classification problems in underground engineering projects.     

Machine learning classifiers for attributing tephra to source volcanoes: an evaluation of methods for Alaska tephras

Glass composition-based correlations of volcanic ash (tephra) traditionally rely on extensive manual plotting. Many previous statistical methods for testing correlations are limited by using geochemical means, masking diagnostic variability. We suggest that machine learning classifiers can expedite correlation, quickly narrowing the list of likely candidates using well-trained models. Eruptives from Alaska's Aleutian Arc-Alaska Peninsula and Wrangell volcanic field were used as a test environment for 11 supervised classification algorithms, trained on nearly 2000 electron probe microanalysis measurements of glass major oxides, representing 10 volcanic sources. Artificial neural networks and random forests were consistently among the top-performing learners (accuracy and kappa > 0.96). Their combination as an average ensemble effectively improves their performance. Using this combined model on tephras from Eklutna Lake, south-central Alaska, showed that predictions match traditional methods and can speed correlation. Although classifiers are useful tools, they should aid expert analysis, not replace it. The Eklutna Lake tephras are mostly from Redoubt Volcano. Besides tephras from known Holocene-active sources, Holocene tephra geochemically consistent with Pleistocene Emmons Lake Volcanic Center (Dawson tephra), but from a yet unknown source, is evident. These tephras are mostly anchored by a highly resolved varved chronology and represent new important regional stratigraphic markers.     

高分辨率遥感影像智能解译研究进展与趋势

遥感影像解译是一个不断发展的研究方向,随着日新月异的遥感应用需求、高分辨率遥感数据的快速发展、地理知识的日积月累、以及人工智能技术的发展,亟需发展自动化、智能化的遥感影像解译技术。本文针对遥感影像智能解译,首先从遥感影像解译单元、分类方法、解译认知3个方面阐述遥感影像解译的研究进展,然后提出了面向地理场景的 “地理知识图谱构建—深度学习模型构建—地理知识图谱与深度学习模型协同的遥感影像语义分类”遥感影像智能解译总体框架,并给出初步试验成果,最后对智能解译的重要发展趋势予以展望,以期拓展遥感影像智能解译研究的思路与方法,提高遥感影像智能解译的精细程度和智能化水平,使智能解译具备地理空间理解能力,推动“数据—信息—知识—智能”的深度转化。