地图集:重构复杂非线性地理世界的“百科全书”

地图学是一门经过数千年的演进发展形成的科学,地图(地图集)是地图科学研究的主阵地,是任何时代和社会都不可或缺的,对人们的工作、学习和生活具有重要作用和意义。首先,简要论述了地图学作为一门科学的社会实践和科学实践的演进发展规律,以及地图集的特点;然后,从哲学角度分析与论述了地图是怎样重构复杂非线性地理世界的(实现由"地理世界"到"地图世界"的转变),人们是怎样利用地图进一步认识复杂非线性地理世界的(实现由"地图世界"到"地理世界"的转变),提出了基于传感器网络的由"感知的地理世界"到"重构的地理世界"再到"认知的地理世界"、指导行动并反馈信息的"双向"转变的认知模式;最后,论述了地图集作为重构复杂非线性地理世界的"百科全书"的作用,举例说明了中国改革开放以来地图集的快速发展和水平的大幅提升,并阐明人工智能时代中国地图集成果将比以往任何时候都更加兴旺发达,地图集作为人们进一步认知复杂非线性地理世界的"百科全书"的功能作用将比以往任何时候都更加强大。     

人工智能在短临降水预报中应用研究综述

短临降水预报是一项重要且具有挑战性的世界性难题.研究人员曾尝试使用各种技术预报降水,但是由于降水本身具有高度非线性、随机性和复杂性的特性,使得降水预测精确度并不高.近年来,随着人工智能技术的迅猛发展,其日渐渗透到人们生活的方方面面,气象领域也因此得益.人工神经网络能够对非线性系统进行建模,因此相比于传统方法,如数值天气预报法和光流法等,人工智能方法使得降水预报的准确率大大提高.本文介绍了传统降水预报的方法,着重总结概括了用于短临降水预报的各种最新人工智能方法,并对各研究方向进行归纳分析,为各类研究人员研究提供有益参考和借鉴.     

On the First Anthropic Argument in Astrobiology

We consider the little-known anthropic argument of Fontenelledealing with the nature of cometary orbits, given a year before the publication of Newton'sPrincipia. This is particularly interesting in view of the rapid development of therecently resurgent theories of cometary catastrophism and their role in the modern astrobiologicaldebates, for instance in the ``rare Earth' hypothesis of Ward and Brownlee.     

地理空间情报预测分析的一种方法

地理空间情报是对影像和地理空间信息的发掘和分析。其中,地理空间情报预测分析是决策的基础。传统的地理空间情报预测分析大多是基于逻辑思维的定性研究,此处提出了一个地理空间情报预测分析的模型——因果关联图,同时给出了计算预测事件可信度的方法。该方法能够定量、简捷、快速、有效地实现地理空间情报预测分析。     

A bottom‐up approach to discover transition rules of cellular automata using ant intelligence

This paper presents a new method to discover transition rules of geographical cellular automata (CA) based on a bottom‐up approach, ant colony optimization (ACO). CA are capable of simulating the evolution of complex geographical phenomena. The core of a CA model is how to define transition rules so that realistic patterns can be simulated using empirical data. Transition rules are often defined by using mathematical equations, which do not provide easily understandable explicit forms. Furthermore, it is very difficult, if not impossible, to specify equation‐based transition rules for reflecting complex geographical processes. This paper presents a method of using ant intelligence to discover explicit transition rules of urban CA to overcome these limitations. This ‘bottom‐up’ ACO approach for achieving complex task through cooperation and interaction of ants is effective for capturing complex relationships between spatial variables and urban dynamics. A discretization technique is proposed to deal with continuous spatial variables for discovering transition rules hidden in large datasets. The ACO–CA model has been used to simulate rural–urban land conversions in Guangzhou, Guangdong, China. Preliminary results suggest that this ACO–CA method can have a better performance than the decision‐tree CA method.     

Improving map generalisation with new pruning heuristics

Many automated generalisation methods are based on local search optimisation techniques: Starting from an initial state of the data, one or several new child states are produced using some transformation algorithms. These child states are then evaluated according to the final data requirements, and possibly used as new candidate state to transform. According to this approach, the generalisation process can be seen as a walk in a tree, each node representing a state of the data, and each link a transformation. In such an approach, the tree exploration heuristic has a great impact on the final result: Depending on which parts of the tree are either explored or pruned, the final result is different, and the process more or less computationally prohibitive. This article investigates the importance of exploration heuristic choice in automated generalisation. Different pruning criteria are proposed and tested on real generalisation cases. Recommendations on how to choose the pruning criterion depending on the need are provided.     

Performance of Multivariate Adaptive Regression Splines (MARS) in predicting runoff in mid-Himalayan micro-watersheds with limited data / Performances de régressions par splines multiples et adaptives (MARS) pour la prévision d'écoulement au sein de micro-bassins versants Himalayens d'altitudes intermédiaires avec peu de données

Abstract

Steep topography and land-use transformations in Himalayan watersheds have a major impact on hydrological characteristics and flow regimes, and greatly affect the perenniality and sustainability of water resources in the region. To identify the appropriate conservation measures in a watershed properly, and, in particular, to augment flow during lean periods, accurate estimation of streamflow is essential. Due to the complexity of rainfall—runoff relationships in hilly watersheds and non-availability of reliable data, process-based models have limited applicability. In this study, data-driven models, based upon the Multiple Adaptive Regression Splines (MARS) technique, were employed to predict streamflow (surface runoff, baseflow and total runoff) in three mid-Himalayan micro-watersheds. In addition, the effect of length of historical records on the performance of MARS models was critically evaluated. Though acceptable MARS models could be developed with a 2-year data set, their performance improved considerably with a 3-year data set. Various indicators of model performance, such as correlation coefficient, average deviation, average absolute deviation and modelling efficiency, showed significant improvement for simulation of surface runoff, baseflow and total flow. To further analyse the versatility and general applicability of the MARS approach, 2-year data sets were used to develop the model and test it on a third-year data set to assess its performance. The models simulated the surface runoff, baseflow and total flow reasonably well and can be reliably applied in ungauged small watersheds under identical agro-climatic settings.     

Application of statistical learning algorithms to ultimate bearing capacity of shallow foundation on cohesionless soil

This study employs two statistical learning algorithms (Support Vector Machine (SVM) and Relevance Vector Machine (RVM)) for the determination of ultimate bearing capacity (q_u) of shallow foundation on cohesionless soil. SVM is firmly based on the theory of statistical learning, uses regression technique by introducing varepsilon‐insensitive loss function. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. It also gives variance of predicted data. The inputs of models are width of footing (B), depth of footing (D), footing geometry (L/B), unit weight of sand (γ) and angle of shearing resistance (?). Equations have been developed for the determination of q_u of shallow foundation on cohesionless soil based on the SVM and RVM models. Sensitivity analysis has also been carried out to determine the effect of each input parameter. This study shows that the developed SVM and RVM are robust models for the prediction of q_u of shallow foundation on cohesionless soil. Copyright © 2010 John Wiley & Sons, Ltd.     

基于A-star算法的矿井事故救援研究

针对井下不同形状巷道几何空间特征,确立以空间点、线、面为基本图元,以巷道中心线作为三维模型构建的基础框架,建立巷道三维模型。采用启发式路径搜索A-star算法,实现了应急救援路线智能快速选择。实践证明,该方法对于矿井事故定位和救援具有一定的实用性。