An Integrated GIS Based Statistical Model to Compute Groundwater Vulnerability Index for Decision Maker in Agricultural Area

The conservation areas in a plain are affected by the groundwater contamination from intense application of the fertilizers. The vulnerability of groundwater can be tested by using the DRASTIC model for the pollutants. The groundwater susceptibility to pollution in the various areas is mapped through DRASTIC model. However, the effects of pollution types and its characteristics are not considered, as this model is used without any modifications. This technique must be standardized for usage in the various aquifers and specific pollution types. The rates of DRASTIC parameters are corrected to obtain the potential for a more accurate analysis of the vulnerability pollution. The relationships between the parameters are identified with respect to the nitrate concentration in the groundwater by calculating the new rates. The methodology was applied to the selected area situated in the south eastern region of Iran at Kerman plain. Twenty-seven different locations were selected to test and analyse the nitrate concentration in the water from underground wells. The pollution in the aquifer was associated and correlated with the DRASTIC index by using the measured nitrate concentrations. The relationship between the index and the measured pollution in the Kerman plain was determined by applying the Wilcoxon rank-sum nonparametric statistical tests and the rates were calculated. It was found specifically in the agricultural areas that the modified DRASTIC model performed more efficiently than the traditional method for nonpoint source pollution, as indicated by the results. After modifications, the regression coefficients revealed that the relationship between the vulnerability index and the nitrate concentration was 77 %, while it was 37 % before the modifications were used. These statistics show that the modified DRASTIC performed far more efficiently than the original version.     

基于AHP和GIS的扬泰地区浅层地下水脆弱性评价

地下水脆弱性评价是保护地下水资源的重要手段,本文根据扬泰地区浅层地下水的水文地质条件,对DRASTIC模型进行了修改。选取地下水位埋深、大气降水入渗净补给量、含水层介质、含水层厚度、地形坡度、包气带介质影响、含水层富水性7个因子作为评价因子,利用层次分析法确定各因子的权重值,建立了符合扬泰地区浅层地下水脆弱性评价模型;利用ArcGIS10．0软件的空间叠加分析功能,编制了扬泰地区浅层地下水脆弱性评价等级图,评价结果与该地区实际水文地质条件基本吻合,对扬泰地区的地下水资源保护和区域规划具有一定的参考价值。     

Development of groundwater favourability map using GIS-based driven data mining models: an approach for effective groundwater resource management

The development of groundwater favourability map is an effective tool for the sustainability management of groundwater resources in typical agricultural regions, such as southern Perak Province, Malaysia. Assessing the potentiality and pollution vulnerability of groundwater is a fundamental phase of favourability mapping. A geographic information system (GIS)-based Boolean operator of a spatial analyst module was applied to combine a groundwater potentiality map (GPM) model and a groundwater vulnerability to pollution index (GVPI) map, thereby establishing the favourable zones for drinking water exploration in the investigated area. The area GPM model was evaluated by applying a GIS-based Dempster–Shafer–evidential belief function model. In the evaluation, six geoelectrically determined groundwater potential conditioning factors (i.e. overburden resistivity, overburden thickness, aquifer resistivity, aquifer thickness, aquifer transmissivity and hydraulic conductivity) were synthesized by employing the probability-based algorithms of the model. The generated thematic maps of the seven hydrogeological parameters of the DRASTIC model were considered as pollution potential conditioning factors and were analysed with the developed ordered weighted average–DRASTIC index model algorithms to construct the GVPI map. Approximately 88.8 and 85.71% prediction accuracies for the Groundwater Potentiality and GVPI maps were established using the reacting operating characteristic curve method and water quality status–vulnerability zone relationship scheme, respectively. Finally, the area groundwater favourability map (GFM) model was produced by applying a GIS-based Boolean operator on the Groundwater Potentiality and GVPI maps. The GFM model reveals three distinct zones: ‘not suitable’, ‘less suitable’ and ‘very suitable’ zones. The area analysis of the GFM model indicates that more than 50% of the study area is covered by the ‘very suitable’ zones. Results produce a suitability map that can be used by local authorities for the exploitation and management of drinking water in the area. The study findings can also be applied as a tool to help increase public awareness of groundwater issues in developing countries.     

ArcGIS地统计分析在地下水脆弱性评价中的应用

地下水是水资源的重要组成部份,地下水污染危害人的健康,影响人们的生产和生活,查明某一地区地下水容易受污染的可能性即地下水脆弱性,能为管理决策部门提供合理开发地下水资源,防治地下水污染的科学规划和管理依据。在脆弱性评价工作中,应用当前国际上最先进的地理信息系统平台ArcGIS,并结合地统计分析原理,完成地下水系统脆弱性编...     

Statewide groundwater‐vulnerability assessment in nebraska using the drastic/GIS model

The paper summarizes a technique for implementing the “DRASTIC” groundwater‐vulnerability model within the context of an automated raster‐based geographic information system. Discussion focuses on a methodological development and a statewide project completed recently in Nebraska. The final products, a comprehensive flow chart illustrating procedures and a map of calculated potential pollution hazard, are presented. The methodology can be executed successfully with minimal training and experience. Areas of Nebraska considered vulnerable to groundwater pollution are identified.     

基于EVI的锡林郭勒生态脆弱性评价

本文基于综合指数法构建生态脆弱性模型,借助质心迁移、趋势分析对锡林郭勒近20年生态脆弱性的时空演变进行了分析,并探究了自然因素和社会因素对生态脆弱性的驱动影响及时空分异特征。结果表明：(1)时间上,锡林郭勒生态脆弱性整体有所改善、分区特征显著。矿区、牧区生态治理成效显著;农业区生态脆弱性出现退化,需加强治理。(2)空间上,锡林郭勒生态脆弱性呈阶梯式分布,生态脆弱性质心逐渐向东偏移。西部地区生态脆弱性持续改善;中、东部地区生态脆弱性相对平稳,局部地区出现退化。(3)自然因素对空间尺度下的生态脆弱性主导特征显著,社会因素对时间尺度下的生态脆弱性主导特征逐渐增强。     

Modeling Site Suitability for Oil Palm Plantations in Southern Thailand

This study developed a methodology for land evaluation, using a multifactor evaluation in GIS. The Analytical Hierarchical Processing (AHP) and the Pairwise Comparison Method were employed for factor ranking and weighting. The Pairwise Comparison Method required experience in rating relative preferences. An experimental design approach was applied to address this requirement. Proposed models were analyzed and extensively tested against ground reference data and the overall accuracy of the method was compared with the results from a conventional method adopted by the Department of Agriculture, Thailand. The results indicated that the new method is an improvement over the previous one in terms of accuracy in the suitability ranking. It can be employed to assist in the prediction and modeling of oil palm land suitability.     

Investigation of automatic feature weighting methods (Fisher,Chi-square and Relief-F) for landslide susceptibility mapping

In landslide susceptibility mapping, factor weights have been usually determined by expert judgements. A novel methodology for weighting landslide causative factors by integrating statistical feature weighting algorithms was proposed. The primary focus of this study is to investigate the effectiveness of automatic feature weighting algorithms, namely Fisher, Chi-square and Relief-F algorithms. Analytic hierarchy process (AHP) method was used as a benchmark method to compare the performances of the weighting algorithms. All weighted factors were tested using factor-weighted overlay method, and quality of these maps was assessed using overall accuracy, area under the ROC curve (AUC) and success rate curve. In addition, Wilcoxon’s signed-rank test was applied to evaluate statistical differences between both estimated overall accuracies and AUCs, respectively. Results showed that the weights determined by feature weighting methods outperformed the conventional AHP method by about 6% and this level of differences was found to be statistically significant.     

京津唐都市经济圈浅层地下水三氮含量评价的协克里格法

对京津唐地区浅层地下水240个样本点的氨氮和硝酸盐氮含量进行了测定,应用地统计学方法对数据进行了分析,结果表明地下水中氨氮和硝酸盐氮含量符合对数正态分布。采用简单协克里格方法对未观察点进行了估值,并绘制了氨氮和硝酸盐氮含量空间分布图。将氨氮和硝酸盐氮含量分成8个等级,利用GIS空间分析方法分别统计了各个等级的面积。结果...     

重力异常向上延拓严密改化模型及向下延拓应用

重力异常向上延拓全球积分模型在航空重力测量数据质量评估和向下延拓迭代计算等领域具有广泛的应用。为了消除积分核函数奇异性影响,需要对该模型进行基于积分恒等式的移去-恢复转换及全球积分域的分区改化处理。在此过程中,传统改化处理方法往往忽略了全球积分过渡到局域积分引起的积分恒等式偏差影响,从而导致不必要的计算模型误差,最终影响向上延拓计算结果的可靠性,甚至影响向下延拓迭代解算结果的稳定性。针对此问题,本文开展了重力异常向上延拓积分模型改化及向下延拓应用分析研究,依据实测数据保障条件和积分恒等式适用条件要求,导出了重力异常向上延拓积分模型的分步改化公式,提出了补偿传统改化模型缺陷的修正公式,并将最终的严密改化模型应用于重力异常向下延拓迭代解算。使用超高阶地球位模型EGM2008作为标准位场开展数值计算检验,分别对重力异常向上延拓分步改化模型的计算精度及在向下延拓迭代解算中的应用效果进行了检核评估,验证了采用严密改化模型的必要性和有效性。     

Influence of geological structures on groundwater accumulation and groundwater salinity in Musandam Peninsula,UAE and Oman

The hydrological setting and groundwater quality of Musandam Peninsula was studied to investigate the influence of geological structures on the groundwater accumulation and groundwater salinity. Five sets of modified morphometric maps were used to reduce errors and carry out the geological structures. The modification was modified by applying mean filter to the Digital Elevation Model (DEM) then applying Soble filter with 10% threshold and equalization enhancement. D8 and algorithms were used to reveal the drainage basins and drainage networks of the entire area. The algorithm determines into which neighbouring pixel any water in a central pixel will flow naturally. Flow direction in a DEM is calculated for every central pixel of input blocks of a 3 × 3 window, all the time comparing the value of the central pixel in the window with the value of its eight neighbours. The spatial association between geological structures and drainage networks was studied using 2D graph and rose diagrams. Flood basin model was applied to simulate the Arabian Gulf water intrusion into the coastal aquifer. The concentrations of solutes in groundwater samples collected from Wadi Al Bih well field and well locations were correlated with the geological structure trends and intersections. The results of the study reveal that the drainage basins, drainage network and groundwater quality are structurally controlled by subsurface geological structure displacements.     

Locally weighted linear combination in a vector geographic information system

Weighted linear combination is a multi-criteria decision analysis technique that can be used by decision-makers to select an optimal location from a collection of alternative locations. Its local form takes into account the range of attribute values within a user-defined neighbourhood in accordance with the range-sensitivity principle. This research explores locally weighted linear combination in a vector-based geographic information system. A custom application in ArcGIS 10 allows the user to select a neighbourhood definition from a standard set including contiguity, distance, and k-nearest neighbours, for which local weights are generated. A case study on vulnerability to heat-related illness in Toronto is used to illustrate the technique. The impact of local weighting on the heat vulnerability index is examined using visual analysis of the spatial patterns of heat vulnerability under the global and local approaches, as well as the sensitivity of the local approach to the selected neighbourhood definition. A trade-off analysis of the local weights is also presented. The combination of socio-demographic and environmental determinants in a locally weighted index results in patterns of heat vulnerability that could support targeted hot weather response at a micro-geographic level within urban neighbourhoods.     

利用高度角与MP组合随机模型提高PPP的收敛速度

在当前科学技术不断创新发展下,很多的技术都被应用到各个领域当中,其中精密单点定位技术(英文简称PPP)便是比较先进的技术,被当前地质勘察和航空测量广泛的应用。此次研究的目的是为了提高PPP收敛速度,这是由于当前PPP存在收敛速度比较慢的问题,不利于该项技术在科技发展领域的推广应用,针对这一问题,本文首先对多路径效应及MP组合观测值进行了实验分析,然后,对基于MP定权的GPS PPP方法进行详细的阐述,最后,通过实验分析,得出利用高度角与MP组合随机模型(ELE_MP模型)提高PPP的收敛速度。     

Integrating the multi-label land-use concept and cellular automata with the artificial neural network-based Land Transformation Model: an integrated ML-CA-LTM modeling framework

Cellular automata (CA) and artificial neural networks (ANNs) have been used by researchers over the last three decades to simulate land-use change (LUC). While conventional CA and ANN models assign a cell to only one land-use class, in reality, a cell may belong to several land-use classes simultaneously. The recently developed multi-label (ML) concept overcomes this limitation in land change science. Although the ML concept is a new paradigm with nonexclusive classes and has shown considerable merit in several applications, few studies in land change science have applied it. In addition, determining transition rules in conventional CA is difficult when the number of drivers is large. Since CA has been shown as a potential model to consider neighborhood effects and ANN has been shown effective in determining CA transition rules, we integrated both CA with an ANN model to overcome limitations of each tool. In this study, we specifically extended the ANN-based Land Transformation Model (LTM) with both a CA-based model and the ML concept to create an integrated ML-CA-LTM modeling framework. We also compared, using standard evaluation measures, differences between the proposed integrated model with a conventional CA-based LTM model (called the ml-CA-LTM). Parameterization was made using a learning and testing procedure common in machine learning. Results showed that the modified LUC model, ML-CA-LTM, produced consistently better goodness of fit calibration values compared to the ml-CA-LTM. The outcome of this modified model can be used by managers and decision makers for improved urban planning.     

Simple DEM-Based Methods to Delineate Channel Networks for Hydrogeomorphological Mapping

To delineate channel networks from DEMs regardless of landform type, this article proposes a new method using slope-weighted flow accumulation. To validate the method, SRTM-3, a global DEM dataset with a resolution of approximately 90 m, was used for analysis of the Loess Plateau, China. Channel networks delineated with and without slope-weighted flow accumulation were derived in both uplands and hilly lands for comparison. In the weighted flow accumulation method, the thresholds for delineating the channels were defined by detecting a turning point in the frequency distribution of the weighted flow accumulation function or by visual similarity with drainage channels extracted from topographic maps. The channel networks delineated with weighting showed closer correlation with a topographic map than the channel networks without weighting, despite the differences in thresholds. Moreover, the channel networks delineated with weighting represented the differences between landform types, while the channel networks without weighting did not. Weighting on the basis of the slope angle shows promise as a general channel delineation method which reflects the actual topography due to its hydrogeomorphological functions.     

Application of frequency ratio and likelihood ratio model for geo-spatial modelling of landslide hazard vulnerability assessment and zonation: a case study from the Sikkim Himalayas in India

The Likelihood Ratio (LR) Model has been applied as an improvement upon the Frequency Ratio (FR) that computes the ratio of the percentage of the landslide pixels to the percentage of the non-landslide pixels instead of the total number of pixels used in the denominator as in case of the FR. The comparative assessment of the two techniques is made through spatial modelling of GIS vector data using the ArcGIS software. Two different Landslide Information Values were computed for each polygon element of the study area employing the two FR techniques that categorized the study area into five classes of vulnerability using natural breaks (Jenks) technique. Subsequently, vulnerability zonation maps were prepared showing the different levels of landslide vulnerability. The LR technique yielded significantly higher vulnerability assessment accuracy (77%) as compared to the standard FR (71%).     

Optimum data weighting and error calibration for estimation of gravitational parameters

Summary A new approach has been developed for determining consistent satellite-tracking data weights in solutions for the satellite-only gravitational models. The method employs subset least-squares solutions of the satellite data contained within the complete solution and requires that the differences of the parameters of subset solutions and the complete solution to be in agreement with their error estimates by adjusting the data weights. GEM-T2 model was recently computed and adjusted through a direct application of this method. The estimated data weights are markedly smaller than the weights implied by the formal uncertainties of the measurements. Orbital arc tests as well as surface gravity comparisons show significant improvements for solutions when more realistic data weighting is achieved.     

利用水位修正模型精化潮位数值模拟

利用海洋动力模式FVCOM（finite volume coast and ocean model）模式建立了高分辨率的海州湾及其外海域潮汐数值模型,提取了整个场内各个网格点上天文潮位值的时间序列。利用该区域内分布的5个验潮站资料提取水位修正模型,对场内的网格点进行水位修正,得到高精度的格点水位时间序列,然后对修正前后的水位值进行比较。结果表明,基于水位修正后的潮位数值模拟结果水位精度提高了10 cm左右。     

分布式遥感空间在线分析的多维分析模型及其旱情应用

近年来,SOLAP技术已逐渐应用于遥感多维分析研究领域,但其计算性能仍面临大规模数据的挑战。本文借鉴数据密集型计算模式Map-Reduce在OLAP领域的相关应用研究,提出一种基于该模式扩展的遥感数据SOLAP立方体模型。在数据分级分块基础上,对现有模型在分布式环境下进行了适应性改进,并在Map-Reduce支持下,通过引入多维地图代数将该模型中的SOLAP计算转化为基于栅格块粒度的并行地图代数操作。以遥感旱情应用为例阐述了模型的构建与应用过程,并实现了原型。实验结果证明了该模型在大规模数据处理情况下具有较好加速性能与可扩展性。     

A spatially weighted degree model for network vulnerability analysis

Using degree distribution to assess network vulnerability represents a promising direction of network analysis.However,the traditional degree distribution model is inadequate for analyzing the vulnerability of spatial networks because it does not take into consideration the geographical aspects of spatial networks.This paper proposes a spatially weighted degree model in which both the functional class and the length of network links are considered to be important factors for determining the node degrees of spatial networks.A weight coefficient is used in this new model to account for the contribution of each factor to the node degree.The proposed model is compared with the traditional degree model and an accessibility-based vulnerability model in the vulnerabil-ity analysis of a highway network.Experiment results indicate that,although node degrees of spatial networks derived from the tra-ditional degree model follow a random distribution,node degrees determined by the spatially weighted model exhibit a scale-free distribution,which is a common characteristic of robust networks.Compared to the accessibility-based model,the proposed model has similar performance in identifying critical nodes but with higher computational efficiency and better ability to reveal the overall vulnerability of a spatial network.