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1.
In karst areas, accurately measuring and managing the spatial variability of soil water content (SWC) is very critical in settling numerous issues such as karst rocky desertification, ecosystem reconstruction, etc. In these areas, SWC exhibits strong spatial dependence, and it is a time and labor consuming procedure to measure its spatial variability. Therefore, estimation of this kind of soil property at an acceptable level of accuracy is of great significance. This study was conducted to evaluate and compare the spatial estimation of SWC by using ordinary kriging (OK) and cokriging (COK) methods with prime terrain variables, tending to predict SWC using limited available sample data for a 2,363.7 km2 study area in Mashan County, Guangxi Zhuang Autonomous Region, Southwest China. The measured SWC ranged from 3.36 to 26.69 %, with a mean of 17.34 %. The correlation analysis between SWC and prime terrain variables indicated that SWC showed significantly positive correlation with elevation (r is 0.46, P < 0.01), and significantly negative correlation with slope (r is ?0.30, P < 0.01); however, SWC was not significantly correlated with aspect in the study area. Therefore, elevation and slope were used as auxiliary data together for SWC prediction using COK method, and mean error (ME) and root mean square error were adopted to validate the prediction of SWC by these methods. Results indicated that COK with prime terrain variables data was superior to OK with relative improvement of 28.52 % in the case of limited available data, and also revealed that such elevation and slope data have the potential to improve the precision and reliability of SWC prediction as useful auxiliary variables.  相似文献   

2.
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3.
In this study, two distinct sets of analyses are conducted on a freshwater acidification critical load dataset, with the objective of assessing the quality of various models in estimating critical load exceedance data. Relationships between contextual catchment and critical load data are known to vary across space; as such, we cater for this in our model choice. Firstly, ordinary kriging (OK), multiple linear regression (MLR), geographically weighted regression (GWR), simple kriging with GWR-derived local means (SKlm-GWR), and kriging with an external drift (KED) are used to predict critical loads (and exceedances). Here, models that cater for space-varying relationships (GWR; SKlm-GWR; KED using local neighbourhoods) make more accurate predictions than those that do not (MLR; KED using a global neighbourhood), as well as in comparison to OK. Secondly, as the chosen predictors are not suited to providing useable estimates of critical load exceedance risk, they are replaced with indicator kriging (IK) models. Here, an IK model that is newly adapted to cater for space-varying relationships performs better than those that are not adapted in this way. However, when site misclassification rates are found using either exceedance predictions or estimates of exceedance risk, rates are intolerably high, reflecting much underlying noise in the data.  相似文献   

4.
This paper investigates the use of an artificial neural network (ANN) model to predict dissolved organic carbon (DOC) in a river network and evaluates the impacts of watershed characteristics on stream DOC. Samples and relevant environmental variables were obtained from field sampling at 28 hydrological response units (HRUs) and a MODIS/SRTM DEM satellite image. HRUs can provide reliable spatial interpolation for filling data gaps and incorporate potential spatial correlation among observations in each ANN neuron. The process and results of neural network modeling were assessed by deterministic and statistical methods and spatial regression kriging. The spatial prediction results show that ANN, using improved back propagation algorithms of 7-15-1 architecture, was the optimal network, by which predictions maintained most of the original spatial variation and eliminated smoothing effects of RK. The sum of the relative contributions of four sensitive variables, including soil organic carbon density, geographic longitude, surface runoff and Chl a in river water, was >75 %. A minor prediction error of ~6 % was found in HRUs of open shrublands, but HRUs of urban and croplands had an error of 24–30 %. This pattern exemplifies anthropogenic impacts in urban areas on stream DOC and agricultural activities in croplands. The usefulness of ANN modeling-based GIS in this study is demonstrated by depiction of spatial variation of stream DOC and indicates the benefits of understanding sensitive factors for watershed impact assessments.  相似文献   

5.
运用普通克里格、泛克里格、协同克里格和回归克里格4种方法,结合由DEM获取的高程因子以及土壤全氮和阳离子交换量（CEC）,预测了黑龙江省海伦市耕地有机质含量的空间分布。不同样点数量下海伦市土壤有机质含量的空间变异结构分析表明,样点数量多并不一定能够识别土壤有机质含量的结构性连续组分,最优化的布置采样点位置可能比单纯增加...  相似文献   

6.
Kriging of water levels in the Souss aquifer,Morocco   总被引：2，自引：0，他引：2  
Universal kriging is applied to water table data from the Souss aquifer in central Morocco. The procedure accounts for the spatial variability of the phenomenon to be mapped. With the use of measured elevations of the water table, an experimental variogram is constructed that characterizes the spatial variability of the measured water levels. Spherical and Gaussian variogram models are alternatively used to fit the experimental variogram. The models are used to develop contour maps of water table elevations and corresponding estimation variances. The estimation variances express the reliability of the kriged water table elevation maps. Universal kriging also provides a contour map of the expected elevation of the water table (drift). The differences between the expected and measured water table elevations are called residuals from the drift. Residuals from the drift are compared with residuals obtained by more traditional least-squares analysis.  相似文献   

7.
Geostatistical Mapping with Continuous Moving Neighborhood   总被引：1，自引：0，他引：1  
An issue that often arises in such GIS applications as digital elevation modeling (DEM) is how to create a continuous surface using a limited number of point observations. In hydrological applications, such as estimating drainage areas, direction of water flow is easier to detect from a smooth DEM than from a grid created using standard interpolation programs. Another reason for continuous mapping is esthetic; like a picture, a map should be visually appealing, and for some GIS users this is more important than map accuracy. There are many methods for local smoothing. Spline algorithms are usually used to create a continuous map, because they minimize curvature of the surface. Geostatistical models are commonly used approaches to spatial prediction and mapping in many scientific disciplines, but classical kriging models produce noncontinuous surfaces when local neighborhood is used. This motivated us to develop a continuous version of kriging. We propose a modification of kriging that produces continuous prediction and prediction standard error surfaces. The idea is to modify kriging systems so that data outside a specified distance from the prediction location have zero weights. We discuss simple kriging and conditional geostatistical simulation, models that essentially use information about mean value or trend surface. We also discuss how to modify ordinary and universal kriging models to produce continuous predictions, and limitations using the proposed models.  相似文献   

8.
Surface map of soil properties plays an important role in various applications in a watershed. Ordinary kriging (OK) and regression kriging (RK) are conventionally used to prepare these surface maps but generally need large number of regularly girded soil samples. In this context, REML-EBLUP (REsidual Maximum Likelihood estimation of semivariogram parameters followed by Empirical Best Linear Unbiased Prediction) shown capable but not fully tested in a watershed scale. In this study, REML-EBLUP approach was applied to prepare surface maps of several soil properties in a hilly watershed of Eastern India and the performance was compared with conventionally used spatial interpolation methods: OK and RK. Evaluation of these three spatial interpolation methods through root-mean-squared residuals (RMSR) and mean squared deviation ratio (MSDR) showed better performance of REML-EBLUP over the other methods. Reduction in sample size through random selection of sampling points from full dataset also resulted in better performance of REML-EBLUP over OK and RK approach. The detailed investigation on effect of sample number on performance of spatial interpolation methods concluded that a minimum sampling density of 4/km2 may successfully be adopted for spatial prediction of soil properties in a watershed scale using the REML-EBLUP approach.  相似文献   

9.
Using kriging has been accepted today as the most common method of estimating spatial data in such different fields as the geosciences. To be able to apply kriging methods, it is necessary that the data and variogram model parameters be precise. To utilize the imprecise (fuzzy) data and parameters, use is made of fuzzy kriging methods. Although it has been 30 years since different fuzzy kriging algorithms were proposed, its use has not become as common as other kriging methods (ordinary, simple, log, universal, etc.); lack of a comprehensive software that can perform, based on different fuzzy kriging algorithms, the related calculations in a 3D space can be the main reason. This paper describes an open-source software toolbox (developed in Matlab) for running different algorithms proposed for fuzzy kriging. It also presents, besides a short presentation of the fuzzy kriging method and introduction of the functions provided by the FuzzyKrig toolbox, 3 cases of the software application under the conditions where: 1) data are hard and variogram model parameters are fuzzy, 2) data are fuzzy and variogram model parameters are hard, and 3) both data and variogram model parameters are fuzzy.  相似文献   

10.
Soil salinity has been known to be problematic to land productivity and environment in the lower Yellow River Delta due to the presence of a shallow, saline water table and marine sediments. Spatial information on soil salinity has gained increasing importance for the demand of management and sustainable utilization of arable land in this area. Apparent electrical conductivity, as measured by electromagnetic induction instrument in a fairly quick manner, has succeeded in mapping soil salinity and many other soil physical and chemical properties from field to regional scales. This was done based on the correlation that existed between apparent electrical conductivity and many other soil properties. In this paper, four spatial prediction methods, i.e., local polynomial, inverse distance weighed, ordinary kriging and universal kriging, were employed to estimate field-scale apparent electrical conductivity with the aid of an electromagnetic induction instrument (type EM38). The spatial patterns estimated by the four methods using EM38 survey datasets of various sample sizes were compared with those generated by each method using the entire sample size. Spatial similarity was evaluated using difference index (DI) between the maps created using various sample sizes (i.e., target maps) and the maps generated with the entire sample size (i.e., the reference map). The results indicated that universal kriging had the best performance owing to the inclusion of residuals and spatial detrending in the kriging system. DI showed that spatial similarity between the target and reference maps of apparent electrical conductivity decreased with the reduction in sample size for each prediction method. Under the same reduction in sample size, the method retaining the most spatial similarity was universal kriging, followed by ordinary kriging, inverse distance weighed, and local polynomial. Approximately, 70 % of total survey data essentially met the need for retaining 90 % details of the reference map for universal kriging and ordinary kriging methods. This conclusion was that OK and UK were two most appropriate methods for spatial estimation of apparent electrical conductivity as they were robust with the reduction in sample size.  相似文献   

11.
Without a doubt, landslide is one of the most disastrous natural hazards and landslide susceptibility maps (LSMs) in regional scale are the useful guide to future development planning. Therefore, the importance of generating LSMs through different methods is popular in the international literature. The goal of this study was to evaluate the susceptibility of the occurrence of landslides in Zonouz Plain, located in North-West of Iran. For this purpose, a landslide inventory map was constructed using field survey, air photo/satellite image interpretation, and literature search for historical landslide records. Then, seven landslide-conditioning factors such as lithology, slope, aspect, elevation, land cover, distance to stream, and distance to road were utilized for generation LSMs by various models: frequency ratio (FR), logistic regression (LR), artificial neural network (ANN), and genetic programming (GP) methods in geographic information system (GIS). Finally, total four LSMs were obtained by using these four methods. For verification, the results of LSM analyses were confirmed using the landslide inventory map containing 190 active landslide zones. The validation process showed that the prediction accuracy of LSMs, produced by the FR, LR, ANN, and GP, was 87.57, 89.42, 92.37, and 93.27 %, respectively. The obtained results indicated that the use of GP for generating LSMs provides more accurate prediction in comparison with FR, LR, and ANN. Furthermore; GP model is superior to the ANN model because it can present an explicit formulation instead of weights and biases matrices.  相似文献   

12.
The Wuwei oasis, situated in the upper reaches of the Shiyang River basin in the arid inland of northwest China, is intensively cultivated using both groundwater and irrigation water originating from the Qilian Mountains. Groundwater levels are declining due to overuse of irrigation water. To estimate the decline over the entire Wuwei oasis, eight different interpolation methods were used for interpolating groundwater levels over 3 years, i.e. starting in 1983, followed by 1988 and ending with 1992. Cross-validation and orthogonal-validation were applied to evaluate the accuracy of the different methods. Root mean squared error and the correlation coefficient (R 2) were calculated for each of the interpolation methods and years. Three kriging methods (simply, ordinary, and universal) gave the best fit. Modified ordinary kriging was found better than simple and universal kriging methods with a smaller number of points having large differences (>50 m) between estimated and predicted values. Based on the groundwater surfaces determined by the ordinary kriging as modified by Yamamoto, the groundwater decline was found from 1983 to 1992 to be a modest 2.1 m in average.  相似文献   

13.
Global climate change has increased the frequency of abnormally high rainfall; such high rainfall events in recent years have occurred in the mountainous areas of Taiwan. This study identifies historical earthquake- and typhoon-induced landslide dam formations in Taiwan along with the geomorphic characteristics of the landslides. Two separate groups of landslides are examined which are classified as those that were dammed by river water and those that were not. Our methodology applies spatial analysis using geographic information system (GIS) and models the geomorphic features with 20?×?20 m digital terrain mapping. The Spot 6 satellite images after Typhoon Morakot were used for an interpretation of the landslide areas. The multivariate statistical analysis is also used to find which major factors contribute to the formation of a landslide dam. The objective is to identify the possible locations of landslide dams by the geomorphic features of landslide-prone slopes. The selected nine geomorphic features include landslide area, slope, aspect, length, width, elevation change, runout distance, average landslide elevation, and river width. Our four geomorphic indexes include stream power, form factor, topographic wetness, and elevation–relief ratio. The features of the 28 river-damming landslides and of the 59 non-damming landslides are used for multivariate statistical analysis by Fisher discriminant analysis and logistic regression analysis. The principal component analysis screened out eleven major geomorphic features for landslide area, slope, aspect, elevation change, length, width, runout distance, average elevation, form factor, river width, stream power, and topography wetness. Results show that the correctness by Fisher discriminant analysis was 68.0 % and was 70.8 % by logistic regression analysis. This study suggests that using logistic regression analysis as the assessment model for identifying the potential location of a landslide dam is beneficial. Landslide threshold equations applying the geomorphic features of slope angle, angle of landslide elevation change, and river width (H L/W R) to identify the potential formation of natural dams are proposed for analysis. Disaster prevention and mitigation measures are enhanced when the locations of potential landslide dams are identified; further, in order to benefit such measures, dam volume estimates responsible for breaches are key.  相似文献   

14.
Restricted kriging for mixture of grade models   总被引：2，自引：0，他引：2  
A modified type of kriging, referred to as restricted kriging (RK), is proposed in this study. The method incorporates constraints on different grade classes to restrict the influence of the samples having different likelihoods in estimation. RK is motivated by the estimation of mineral reserves when grades have highly skewed distributions. Ordinary kriging tends to produce an overly smoothed interpolated surface by underestimating high grades and overestimating low grades. The fact that ordinary kriging gives a uniform prior treatment to all samples independent of their values is a major factor associated with this smoothing effect. The new approach differentiates each grade portion by preselected cutoffs. RK is developed for a single cutoff and then extended into a general form for any finite number of cutoffs. Restricted cokriging (RCK) is also formulated to simultaneously estimate a set of random functions with restriction conditions. Methods are suggested for determination of the probabilities of occurrence of different grade portions. Finally, the new approach is demonstrated on a case study of an epithermal gold deposit.  相似文献   

15.
The objective of this study is to map landslide susceptibility in Zigui segment of the Yangtze Three Gorges area that is known as one of the most landslide-prone areas in China by using data from light detection and ranging (LiDAR) and digital mapping camera (DMC). The likelihood ratio (LR) and logistic regression model (LRM) were used in this study. The work is divided into three phases. The first phase consists of data processing and analysis. In this phase, LiDAR and DMC data and geological maps were processed, and the landslide-controlling factors were derived such as landslide density, digital elevation model (DEM), slope angle, aspect, lithology, land use and distance from drainage. Among these, the landslide inventories, land use and drainage were constructed with both LiDAR and DMC data; DEM, slope angle and aspect were constructed with LiDAR data; lithology was taken from the 1:250,000 scale geological maps. The second phase is the logistic regression analysis. In this phase, the LR was applied to find the correlation between the landslide locations and the landslide-controlling factors, whereas the LRM was used to predict the occurrence of landslides based on six factors. To calculate the coefficients of LRM, 13,290,553 pixels was used, 29.5 % of the total pixels. The logical regression coefficients of landslide-controlling factors were obtained by logical regression analysis with SPSS 17.0 software. The accuracy of the LRM was 88.8 % on the whole. The third phase is landslide susceptibility mapping and verification. The mapping result was verified using the landslide location data, and 64.4 % landslide pixels distributed in “extremely high” zone and “high” zone; in addition, verification was performed using a success rate curve. The verification result show clearly that landslide susceptibility zones were in close agreement with actual landslide areas in the field. It is also shown that the factors that were applied in this study are appropriate; lithology, elevation and distance from drainage are primary factors for the landslide susceptibility mapping in the area, while slope angle, aspect and land use are secondary.  相似文献   

16.
Mapping the occurrence and thickness of layers within a soil profile is a prerequisite for soil characterization. The objective of this paper is to compare the applicability of two statistical methods—discriminant analysis (DA) and logistic regression (LR)—used to calculate the thickness of Quaternary sediments in a formal way and to identify parameters controlling the occurrence of these sediments. The investigations were carried out in southern Bavaria in an area of about 150 ha presenting a large variability in relief and parent material (Tertiary material, Pleistocene loess, colluvial/alluvial sediments). Comparisons between the two statistical methods were carried out with a training dataset and an evaluation dataset. The results show that DA was preferable under the assumptions of normality and equal variance/covariance matrices. The analyses produced models with 80 % and 79 % correctly reclassified assignments and a canonical correlation coefficient of approximately 0.60. From the simulations, it was found (i) that the determining predictors were altitude, slope, and upslope catchment area (partly expressed as topographical wetness index), SAGA wetness index and specific catchment area; and (ii) that a disadvantage of LR was that trial and error was frequently necessary to find the optimal composition of variables. In this study, a hierarchical combination of binary and ordinal LR was used and revealed (iii) that when the probabilities in LR between adjacent categories were similar, the possibility of incorrect calculations increased and (iv) that visual inspections as well as RMSE showed that DA with weighted depths (5 cm-stepwise DA) provided the best prediction accuracy. This information can help improve soil surveys and the predictability of the spatial heterogeneity in landscapes.  相似文献   

17.
The numerical simulation of collapsible settlement in loess soil subjected to rising ground water table is presented in this paper. A coupled hydro-mechanical model is proposed. Comparisons between the results of numerical simulations and those of oedometer and in situ water immersion field test in Lanzhou, northwest China, reveal good agreement, which validates the proposed model formulation. Factors that influence the ground settlement of loess including initial elevation of ground water table, rising water height and velocity are then evaluated. The results of the analyses reveal that the most critical situation of largest possible ground settlement due to ground water rising in loess involves initial water table elevation of 10 m and rising water velocity of 0.5 m/year. Two upper bound lines of predicted maximum possible ground settlement are proposed to facilitate a preliminary quick evaluation of ground settlement due to rising water under different water table scenarios in loess.  相似文献   

18.
Landslides constitute the most widespread and damaging natural hazards in the Constantine city. They represent a significant constraint to development and urban planning. In order to reduce the risk related to potential landslide, there is a need to develop a comprehensive landslide hazard map (LHM) of the area for an efficient disaster management and for planning development activities. The purpose of this research is to prepare and compare the LHMs of the Constantine city, by applying frequency ratio (FR), weighting factor (Wf), logistic regression (LR), weights of evidence (WOE), and analytical hierarchy process (AHP) methods used in a framework of the geographical information system (GIS). Firstly, a landslide inventory map has been prepared based on the interpretation of aerial photographs, high resolution satellite images, fieldwork, and available literature. Secondly, eight landslide-conditioning factors such as lithology, slope, exposure, rainfall, land use, distance to drainage, distance to road, and distance to fault have been considered to establish LHMs using the FR, Wf, LR, WOE, and AHP models in GIS. For verification, the obtained LHMs have been validated comparing the LHMs with the known landslide locations using the receiver operating characteristics curves (ROC). The validated results indicate that the FR method provides more accurate prediction (86.59 %) of LHMs than the WOE (82.38 %), AHP (77.86 %), Wf (77.58 %), and LR (70.45 %) models. On the other hand, the obtained results showed that all the used models in this study provided a good accuracy in predicting landslide hazard in Constantine city. The established maps can be used as useful tools for risk prevention and land use planning in the Constantine region.  相似文献   

19.
Dwarka River basin in Birbhum, West Bengal (India), is an agriculture-dominated area where groundwater plays a crucial role. The basin experiences seasonal water stress conditions with a scarcity of surface water. In the presented study, delineation of groundwater potential zones (GWPZs) is carried out using a geospatial multi-influencing factor technique. Geology, geomorphology, soil type, land use/land cover, rainfall, lineament and fault density, drainage density, slope, and elevation of the study area were considered for the delineation of GWPZs in the study area. About 9.3, 71.9 and 18.8% of the study area falls within good, moderate and poor groundwater potential zones, respectively. The potential groundwater yield data corroborate the outcome of the model, with maximum yield in the older floodplain and minimum yield in the hard-rock terrains in the western and south-western regions. Validation of the GWPZs using the yield of 148 wells shows very high accuracy of the model prediction, i.e., 89.1% on superimposition and 85.1 and 81.3% on success and prediction rates, respectively. Measurement of the seasonal water-table fluctuation with a multiplicative model of time series for predicting the short-term trend of the water table, followed by chi-square analysis between the predicted and observed water-table depth, indicates a trend of falling groundwater levels, with a 5% level of significance and a p-value of 0.233. The rainfall pattern for the last 3 years of the study shows a moderately positive correlation (R 2 = 0.308) with the average water-table depth in the study area.  相似文献   

20.
Landslide susceptibility assessment using GIS has been done for part of Uttarakhand region of Himalaya (India) with the objective of comparing the predictive capability of three different machine learning methods, namely sequential minimal optimization-based support vector machines (SMOSVM), vote feature intervals (VFI), and logistic regression (LR) for spatial prediction of landslide occurrence. Out of these three methods, the SMOSVM and VFI are state-of-the-art methods for binary classification problems but have not been applied for landslide prediction, whereas the LR is known as a popular method for landslide susceptibility assessment. In the study, a total of 430 historical landslide polygons and 11 landslide affecting factors such as slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to rivers, distance to lineaments, and rainfall were selected for landslide analysis. For validation and comparison, statistical index-based methods and the receiver operating characteristic curve have been used. Analysis results show that all these models have good performance for landslide spatial prediction but the SMOSVM model has the highest predictive capability, followed by the VFI model, and the LR model, respectively. Thus, SMOSVM is a better model for landslide prediction and can be used for landslide susceptibility mapping of landslide-prone areas.  相似文献   

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