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.     

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.     

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。     

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).     

巷道围岩松动圈的地质雷达探测及应用

在简述地质雷达探测原理的基础上, 分析了巷道围岩松动圈雷达探测的可行性。结合华丰矿巷道围岩松动圈探测实例, 提出了松动圈雷达探测方法、图像分析及探测结果应用。结果表明, 采用地质雷达探测巷道围岩松动圈是可行的, 可以用于指导巷道围岩的锚杆支护设计。     

基于支持向量机的喀斯特山区土壤环境质量评价——以贵州北部一茶叶园区为例

以贵州北部一茶叶园区80个表层土壤样品为研究对象,对其Hg、As、Cd、Pb、Cr和Cu含量进行测定,在MATLAB中应用支持向量机构建土壤环境质量评价模型,并与模糊综合评价法和内梅罗综合污染指数法的评价结果对比分析,探究支持向量机模型在喀斯特山区土壤环境质量评价中的适用性,其结果表明:研究区土壤质量Ⅰ类与Ⅱ类样品比例为33∶7,土壤环境质量大多数为I类;支持向量机方法的评价结果与模糊综合评价法和内梅罗综合污染指数法结果的相同率分别达到82.5%和80.0%,并分析结果有差异的样品,发现支持向量机评价结果更符合实际情况,这说明该模型适用于土壤环境质量的评价。      

Quantifying land use/land cover spatio-temporal landscape pattern dynamics from Hyperion using SVMs classifier and FRAGSTATS®

This study aims to quantify the landscape spatio-temporal dynamics including Land Use/Land Cover (LULC) changes occurred in a typical Mediterranean ecosystem of high ecological and cultural significance in central Greece covering a period of 9 years (2001–2009). Herein, we examined the synergistic operation among Hyperion hyperspectral satellite imagery with Support Vector Machines, the FRAGSTATS^® landscape spatial analysis programme and Principal Component Analysis (PCA) for this purpose. The change analysis showed that notable changes reported in the experimental region during the studied period, particularly for certain LULC classes. The analysis of accuracy indices suggested that all the three classification techniques are performing satisfactorily with overall accuracy of 86.62, 91.67 and 89.26% in years 2001, 2004 and 2009, respectively. Results evidenced the requirement for taking measures to conserve this forest-dominated natural ecosystem from human-induced pressures and/or natural hazards occurred in the area. To our knowledge, this is the first study of its kind, demonstrating the Hyperion capability in quantifying LULC changes with landscape metrics using FRAGSTATS^® programme and PCA for understanding the land surface fragmentation characteristics and their changes. The suggested approach is robust and flexible enough to be expanded further to other regions. Findings of this research can be of special importance in the context of the launch of spaceborne hyperspectral sensors that are already planned to be placed in orbit as the NASA’s HyspIRI sensor and EnMAP.     

一种新的实时电磁逆散射方法

为解决介质圆柱体逆散射问题,提出一种新的在线逆散射方法,通过支持向量机将逆散射问题转化成一个回归估计问题. 该方法可应用于各种逆散射方面, 尤其是目标的几何与电磁参数重构和埋地目标探测. 文中首次将支持向量机方法应用到该领域,设置多个散射场的观测点,通过提取散射场的不同信息作为样本信息训练支持向量机, 建立了介质圆柱体的逆散射模型, 利用该模型重构了介质圆柱体的电磁参数,同时探测了埋地位置. 数值结果显示了该方法的有效性和准确性,为目标的实时逆散射研究提供了一种有效方法.     

基于支持向量机的京津冀城市群热环境时空形态模拟

城市群热环境作为区域生态重要组成部分,已成为近年来的研究热点。而如何选择针对城市群这种复杂地地貌特征的热环境量化工具一直是亟待解决的技术难点,基于此本研究提出了一种解决多样本、非线性、非平稳及高维函数拟合的计算方法,并建立了基于支持向量机(SVM)的京津冀城市群热环境曲面模型来揭示城市群热环境的时空形态变化。研究结果表明：① SVM模型在刻画多核心、多种土地利用类型城市群热环境的空间分布方面具有理论与实践可行性,能够根据热环境的整体空间布局通过高斯核函数进行局部优化差值,最大限度减少缺省值对模型拟合结果的影响。相比于对照方法可以模拟出更高精度的复杂地貌特征城市群热岛空间分布格局;② 在SVM模型曲面拟合的过程中,拟合精度和拟合时间是衡量拟合结果的重要指标,而原始影像的分辨率则是影响该指标的决定性因素;③ 2003-2013年区域内北京市与天津市的城市热岛效应变化最为明显,热岛面积分别增加7091 km²与4196 km²,空间上呈现出逐年接近连片发展趋势,热岛重心移动轨迹具有明显的时空分异性。北京城市热岛特征为东南部地区异速增长,西部地区缓慢增长;天津城市热岛特征为以城市中心为圆心向周围扩展。本研究进一步丰富了城市群热环境评测的定量方法,可以在实践上对城市群的城市规划、城市建设、环境保护和区域可持续发展等提供定量化、可视化的决策支持。