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。     

多类支持向量机在语音识别中的应用

针对语音模式识别,引入多类（M-ary）支持向量机来进行分类。并详细的介绍了M-ary支持向量机的概念以及语音识别系统中的实现方式。通过仿真实验,与BP神经网络、SVM进行比较,验证了M-ary支持向量机具有识别率高,抗噪能力强等优越性。     

通用走航式海洋测量作业平台的设计与实现

扼要回顾了我国走航式海洋测量作业平台的研制历程,提出了通用走航式海洋测量作业平台研制的总体目标、设计原则、功能需求,介绍了该平台软件硬件研制实现及其主要性能、功能技术指标,以及平台的应用情况,最后对平台的未来发展进行了展望。     

Use of Gene Expression Programming in regionalization of flow duration curve

In this paper, a recently introduced artificial intelligence technique known as Gene Expression Programming (GEP) has been employed to perform symbolic regression for developing a parametric scheme of flow duration curve (FDC) regionalization, to relate selected FDC characteristics to catchment characteristics. Stream flow records of selected catchments located in the Auckland Region of New Zealand were used. FDCs of the selected catchments were normalised by dividing the ordinates by their median value. Input for the symbolic regression analysis using GEP was (a) selected characteristics of normalised FDCs; and (b) 26 catchment characteristics related to climate, morphology, soil properties and land cover properties obtained using the observed data and GIS analysis. Our study showed that application of this artificial intelligence technique expedites the selection of a set of the most relevant independent variables out of a large set, because these are automatically selected through the GEP process. Values of the FDC characteristics obtained from the developed relationships have high correlations with the observed values.     

Market driven management of climate change impacts in the Spanish mussel sector

Mussel farming is the largest aquaculture activity in Spain both in volume and value, being a key pillar for the development of the coastal areas in Galicia (NW Spain), where this production is concentrated. The available scientific knowledge suggests that in the long term the primary productivity of the Galician estuaries will be reduced due to climate change. Consequently, adaptive management will be required to face this challenge. The impact of the likely production decrease will depend, among other factors, on the availability of substitutes, being their identification the main object of this research. So, it was analysed whether this markets are integrated or not through cointegration tests, finding that only the French and the Spanish markets are partially integrated. This implies that since very little substitutes are available for the Galician fresh mussels, decreases in production will not necessarily affect producer's income, as prices increases will compensate the (eventual) reduction in volume. In terms of policy, this study suggests that market intelligence may contribute to an adaptive, pragmatic and flexible framework to face climate change impacts, avoiding too anticipated or blind responses that may result in economic, environmental and social costs.     

Real time earthquake forecasting in Italy

We have applied an earthquake clustering epidemic model to real time data at the Italian Earthquake Data Center operated by the Istituto Nazionale di Geofisica e Vulcanologia (INGV) for short-term forecasting of moderate and large earthquakes in Italy. In this epidemic-type model every earthquake is regarded, at the same time, as being triggered by previous events and triggering following earthquakes. The model uses earthquake data only, with no explicit use of tectonic, geologic, or geodetic information. The forecasts are displayed as time-dependent maps showing both the expected rate density of M_l ≥ 4.0 earthquakes and the probability of ground shaking exceeding Modified Mercalli Intensity VI (PGA ≥ 0.01 g) in an area of 100 × 100 km² around the zone of maximum expected rate density in the following 24 h. For testing purposes, the overall probability of occurrence of an M_l ≥ 4.5 earthquake in the same area of 100 × 100 km² is also estimated. The whole procedure is tested in real time, for internal use only, at the INGV Earthquake Data Center.Forecast verification procedures have been carried out in forward-retrospective way on the 2006–2007 INGV data set, making use of statistical tools as the Relative Operating Characteristics (ROC) diagrams. These procedures show that the clustering epidemic model performs up to several hundred times better than a simple random forecasting hypothesis. The seismic hazard modeling approach so developed, after a suitable period of testing and refinement, is expected to provide a useful contribution to real time earthquake hazard assessment, even with a possible practical application for decision making and public information.