The Role of Aga Khan Rural Support Programme in Rural Development in the Karakorum, Hindu Kush ＆ Himalayan Region： Examples from the Northern Mountainous Belt of Pakistan

Pakistan is predominantly a mountainous country where rural development activities are characterised by inconsistency, politically motivated short-term projects without proper feedback. Since the inception of the country, the top-down approach has been followed, and the same development plans that were formulated for the plain areas have been extended to the mountains without any modification. In doing so, neither the participation of the local communities was cared for, nor the mountain specificities were considered in the planning process. Moreover, the representation of the local inhabitants was improper and contradictory to the facts. This biased approach has been one of the main causes for the failure of development projects carried out by different agencies of the Government. Contrary to the perception of the state authorities, the mountain communities proved to be more open to accept new approaches and demonstrated the capacity and capability of being a dependable development partner. In this paper, a detailed account of the Aga Khan Rural Support Programme （AKRSP） has been presented to assess and evaluate the approach followed by this non-governmental organisation （NGO）, and the response of the local inhabitants as collaborators in the development process. The achievements of the AKRSP from project planning, implementation and monitoring can be adopted as a model for rural development not only in the plains, but also in the mountainous areas of the developing countries in the world.     

成都市冬季重污染日气象条件成因分析及预报

本文通过对成都10个重污染日进行天气学分析,将污染浓度与气象要素进行聚类、研究了重污染日形成原因及污染浓度与气象要素的关系。在此基础上,建立了SO_2、TSP日平均浓度分级预报方程。     

Sensitivity analysis of support safety factor for predicting the effects of contributing parameters on roof falls in underground coal mines

Roof falls accounted for 18.18% of all fatal accidents in Indian coal mines, contributing about 35.29% of all fatal accidents in below-ground operations in 2005. The support safety factor, always preferred in support planning and design of underground coal mines, may be an important predictor for roof falls. In this paper, geotechnical data were collected from 14 roof fall incident places in an underground coal mine, located in the Eastern India, which has bord and pillar method of workings. The mean value of probabilistic support safety factor for the case study mine was found to be 1.24. However, the probability, of the estimated support safety factor of less than or equal to one, was found to be 0.246. Sensitivity analysis was conducted to analyze the effects of the contributing parameters on support safety factor and the likelihood of the roof fall. The multi-variate regression analysis was carried out for the data generated by Monte Carlo method to correlate the contributing factors to support safety factor. It ranked gallery width as the first parameter to control the support safety factor.     

Spatial Variability of Rock Depth in Bangalore Using Geostatistical, Neural Network and Support Vector Machine Models

Geospatial technology is increasing in demand for many applications in geosciences. Spatial variability of the bed/hard rock is vital for many applications in geotechnical and earthquake engineering problems such as design of deep foundations, site amplification, ground response studies, liquefaction, microzonation etc. In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 km². In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, Geostatistical model based on Ordinary Kriging technique, Artificial Neural Network (ANN) and Support Vector Machine (SVM) models have been developed. In Ordinary Kriging, the knowledge of the semi-variogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of the Bangalore, where field measurements are not available. A new type of cross-validation analysis developed proves the robustness of the Ordinary Kriging model. ANN model based on multi layer perceptrons (MLPs) that are trained with Levenberg–Marquardt backpropagation algorithm has been adopted to train the model with 90% of the data available. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing loss function has been used to predict the reduced level of rock from a large set of data. In this study, a comparative study of three numerical models to predict reduced level of rock has been presented and discussed.     

A new type of Shipboard Meteorological Satellite Receiving-processing System

INTRODUCTIONTheSMSRPSisstalledontheshipnavigatingovertheseaandreceivessatellitecloudmapsatanytimetoprovidereliablereal-timedataofmeteorologyandocean.Itisanimportantequipmentforsafeguardofshipnavigation.Chinahasthousandsofoceanicships.Butalmostallships,withonlyveryfewexception,arenotstalledbySMSRPS.Thecausesareasfollows:1.Thetechniqueisverycomplicated.2.Thedevelopmentcostisveryhigh.3.Ifweintroduceforeignequipment,thecostistooexpensive.4.Foreignequipmentanditssoftwarearecompletelyclo…     

基于支持向量机的CBERS-02卫星影像信息提取

CBERS卫星是由中国空间技术研究院与巴西空间研究院联合研制的地球资源遥感卫星,CBERS-02卫星数据总体质量比CBERS-01卫星有所提高,本文利用支持向量机方法对CBERS-02卫星影像信息进行提取。研究中首先用6S模式对影像进行大气校正,然后选择RBF为支持向量机方法的核函数,并用交叉验证方法得到影响RBF核函数的两个最佳参数值进行学习完成信息提取,最后将提取结果制作成矢量图。通过研究得出用大气校正后的数据进行信息提取分类精度有所提高;与最大似然法和最小距离法相比,支持向量机方法分类精度较高。通过将研究结果与ETM+影像进行比较得出,CBERS-02卫星影像精度能够满足应用需求并能代替TM/ETM+数据开展研究工作。     

Change of Support for Estimating Local Block Grade Distributions

An important aspect in mineral resource evaluation is the reduction of variance when post-processing the grade distributions defined on the support (volume) of the available data into distributions defined on the support of the proposed selective mining units. Although the volume-variance relationship is well understood for the estimation of global grade distributions, it is still an unsolved issue for local estimation studies based on non-parametric geostatistical methods, such as indicator kriging, for which the support correction is not inherent to the method. To clarify this relationship, the local change of support problem is examined in the scope of two parametric models (multi-Gaussian and discrete Gaussian models). It is shown that the variance reduction factor between point and block-support local distributions depends on the block being considered and is less than the global variance reduction factor. As a consequence, post-processing the local point-support grade distributions on the basis of the latter systematically understates the importance of the change of support at the local scale and makes selective mining appear more economically attractive than it really is. In the light of these results, a methodology is proposed to post-process the local point-support distributions obtained via non-parametric (indicator) methods into block-support distributions. An application to simulated data indicates that this methodology provides an accurate estimation at the block support when dealing with diffusion-type random fields.     

Modèles géostatistiques de concentrations ou de débits le long des cours d'eau

Estimating concentrations or flow rates along a stream network requires specific models. Two classes of models, recently proposed in the literature, are generalized, to the intrinsic case in particular. We present a global construction by ‘streams’, i.e. on the whole set of paths between sources and outlet. Combining stationary or intrinsic one-dimensional random functions leads to stationary or intrinsic models on segments, with discontinuities at the forks. A construction from outlet to sources, leads to stationary or intrinsic models on each stream, without any discontinuity at the forks. The linear variogram is found as a particular case. The extension to the linear model of coregionalization is immediate, allowing a multivariate modelling of concentrations. To cite this article: C. de Fouquet, C. Bernard-Michel, C. R. Geoscience 338 (2006).     

气象资料三维化技术中的插值问题

通过对不同源气象资料及数字地理信息资料的插值，实现了不同气象资料与地理信息资料分辨率的协调；从而将多种信息叠加到一起，实现了多源资料的二维、三维仿真显示。这对于模拟真实的大气空间结构、理解地形及植被对大气运动的影响、辨识各种天气系统及多种信息的融合等有积极意义。     

基于气象因子的华中电网负荷预测方法研究

在分析各种节假日负荷变化规律的基础上, 利用气象因子作预报变量, 使用动态的综合线性回归和自回归相结合的混合线性回归方法及非线性的人工神经网络方法来进行华中电网日负荷和日最大负荷及日最小负荷的预测。对12个月共365天的独立样本试预报表明, 该客观方案对华中电网负荷的预测精度可满足业务调度的需要。