基于高光谱的桂东北峰丛洼地土壤有机碳含量预测

以桂东北寨底峰丛洼地土壤为研究对象,利用二阶微分和去除包络线二阶微分方法对土壤光谱进行处理,筛选出3种光谱指数与土壤有机碳(S O C)相关系数最高的特征波段,通过比较偏最小二乘回归、多元线性回归与多元逐步回归等模型的精度,确定SOC最佳估测模型.结果表明:(1)研究区土壤样品有机碳质量分数最小值为0.20％,最大值为...     

The comparison of the performance of ELM,BRNN, and SVM methods for the prediction of compression index of clays

The compression index (Cc) is a necessary parameter for the settlement calculation of clays. However, determination of the compression index from oedometer tests takes a relatively long time and leads to a very demanding experimental working program in the laboratory. Therefore, geotechnical engineering literature involves many studies based on indirect methods such as multiple regression analysis (MLR) and soft computing methods to determine the compression index. This study is aimed to predict the compression index by using extreme learning machine (ELM), Bayesian regularization neural network (BRNN), and support vector machine (SVM) methods. The selected variables for each method are the natural water content (w_n), initial void ratio (e₀), liquid limit (LL), and plasticity index (PI) of clay samples. Many trials were carried out in order to get the best prediction performance with each model. The application results obtained from the models were also compared based on the correlation coefficient (R), coefficient of efficiency (E), and mean squared error (MSE). The results indicate that the BRNN method has better success on estimation of the compression index compared to the ELM and SVM methods.     

基于主成分分析法的Q2黄土湿陷特性研究

Q2黄土由于埋藏深,结构相对致密,其湿陷性问题常常被忽视。湿陷系数作为评价黄土湿陷程度的定量指标,其影响因素众多,包括土的含水率、干密度、孔隙比等。由于各因素之间存在一定相关性,所建立的湿陷系数与物理指标之间相关关系往往准确度较低。为降低黄土湿陷指标多重相关性对数据回归分析结果的影响,提高预测精度,以彬州渭化乙二醇项目场地Q2黄土为研究对象,在统计分析场地地层物性指标及湿陷系数与物性单一指标之间相关性的基础上,筛选了7个与湿陷系数相关性较好的指标。采用主成分分析法,通过多元线性回归分析,建立了以累积方差贡献率为基础的Q2黄土湿陷系数计算模型。模型计算值与实测值对比结果表明,该方法有效较低了湿陷系数影响因子之间的多重相关性和相互影响问题,证实了所建立的Q2黄土湿陷系数与独立影响因子之间相关关系的合理性和准确性。     

Prediction of Uniaxial Compressive Strength,Tensile Strength and Porosity of Sedimentary Rocks Using Sound Level Produced During Rotary Drilling

The main purpose of the study is to develop a general prediction model and to investigate the relationships between sound level produced during drilling and physical properties such as uniaxial compressive strength, tensile strength and percentage porosity of sedimentary rocks. The results were evaluated using the multiple regression analysis taking into account the interaction effects of various predictor variables. Predictor variables selected for the multiple regression model are drill bit diameter, drill bit speed, penetration rate and equivalent sound level produced during rotary drilling (L _eq). The constructed models were checked using various prediction performance indices. Consequently, it is possible to say that the constructed models can be used for practical purposes.     

Artificial Neural Networks: A Solution to the Ambiguity in Prediction of Engineering Properties of Fine-Grained Soils

Determination of soaked california bearing ratio (CBR) and compaction characteristics of soils in the laboratory require considerable time and effort. To make a preliminary assessment of the suitability of soils required for a project, prediction models for these engineering properties on the basis of laboratory tests—which are quick to perform, less time consuming and cheap—such as the tests for index properties of soils, are preferable. Nevertheless researchers hold divergent views regarding the most influential parameters to be taken into account for prediction of soaked CBR and compaction characteristics of fine-grained soils. This could be due to the complex behaviour of soils—which, by their very nature, exhibit extreme variability. However this disagreement is a matter of concern as it affects the dependability of prediction models. This study therefore analyses the ability of artificial neural networks and multiple regression to handle different influential parameters simultaneously so as to make accurate predictions on soaked CBR and compaction characteristics of fine-grained soils. The results of simple regression analyses included in this study indicate that optimum moisture content (OMC) and maximum dry density (MDD) of fine-grained soils bear better correlation with soaked CBR of fine-grained soils than plastic limit and liquid limit. Simple regression analyses also indicate that plastic limit has stronger correlation with compaction characteristics of fine-grained soils than liquid limit. On the basis of these correlations obtained using simple regression analyses, neural network prediction models and multiple regression prediction models—with varying number of input parameters are developed. The results reveal that neural network models have more ability to utilize relatively less influential parameters than multiple regression models. The study establishes that in the case of neural network models, the relatively less powerful parameters—liquid limit and plastic limit can also be used effectively along with MDD and OMC for better prediction of soaked CBR of fine-grained soils. Also with the inclusion of less significant parameter—liquid limit along with plastic limit the predictions on compaction characteristics of fine-grained soils using neural network analysis improves considerably. Thus in the case of neural network analysis, the use of relatively less influential input parameters along with stronger parameters is definitely beneficial, unlike conventional statistical methods—for which, the consequence of this approach is unpredictable—giving sometimes not so favourable results. Very weak input parameters alone need to be avoided for neural network analysis. Consequently, when there is ambiguity regarding the most influential input parameters, neural network analysis is quite useful as all such influential parameters can be taken to consideration simultaneously, which will only improve the performance of neural network models. As soils by their very nature, exhibit extreme complexity, it is necessary to include maximum number of influential parameters—as can be determined easily using simple laboratory tests—in the prediction models for soil properties, so as to improve the reliability of these models—for which, use of neural networks is more desirable.     

Prediction of uniaxial compressive strength of sandstones using petrography-based models

The uniaxial compressive strength of intact rock is the main parameter used in almost all engineering projects. The uniaxial compressive strength test requires high quality core samples of regular geometry. The standard cores cannot always be extracted from weak, highly fractured, thinly bedded, foliated and/or block-in-matrix rocks. For this reason, the simple prediction models become attractive for engineering geologists. Although, the sandstone is one of the most abundant rock type, a general prediction model for the uniaxial compressive strength of sandstones does not exist in the literature. The main purposes of the study are to investigate the relationships between strength and petrographical properties of sandstones, to construct a database as large as possible, to perform a logical parameter selection routine, to discuss the key petrographical parameters governing the uniaxial compressive strength of sandstones and to develop a general prediction model for the uniaxial compressive strength of sandstones. During the analyses, a total of 138 cases including uniaxial compressive strength and petrographic properties were employed. Independent variables for the multiple prediction model were selected as quartz content, packing density and concavo–convex type grain contact. Using these independent variables, two different prediction models such as multiple regression and ANN were developed. Also, a routine for the selection of the best prediction model was proposed in the study. The constructed models were checked by using various prediction performance indices. Consequently, it is possible to say that the constructed models can be used for practical purposes.     

喀斯特山区不同复种指数条件下的土壤质量变化——以普定县猫洞小流域为例

选择贵州中部喀斯特山区普定县猫洞小流域地区,通过长期受不同生产行为影响的区域（低复种旱作区、高复种旱作区、高复种复合农作区）的土壤采样分析,结果表明:喀斯特山区小流域土壤在长期不同生产行为影响下,从低复种指数旱作区、高复种指数旱作区到高复种指数复合农作区,土壤容重、粘粒含量减小,而土壤有机质、腐殖酸含量、全氮、碱解氮、速效磷、速效钾、土壤呼吸强度、脲酶、磷酸酶、蛋白酶活性含量依次增大,土壤全钾含量和过氧化氢酶活性却依次减小。聚类分析结果进一步表明:高复种复合农作区土壤保肥、保水、供肥等状况最好;而低复种旱作区土壤质量和供肥、保肥、保水等性状最差。上述结果说明,农户的不同生产行为对土壤质量有一定的影响。      

Swell pressure prediction by suction methods

Soil suction is the most relevant soil parameter for characterization of the swell behavior. An attempt was made to predict swell pressures from soil suction measurements. In this study, Na-bentonite was mixed with kaolinite in the ratios of 5, 10, 15, 20 and 25% of dry kaolinite weight to obtain soils in a wide range of plasticity indices (i.e. 30, 50, 68, 84 and 97%). Suction measurements using thermocouple psychrometer technique were made on statically compacted specimens. The dependence of soil suction on water content, dry density and bentonite content was examined. Soil suction was correlated to the soil properties, namely, water content, plasticity index, dry density, cation exchange capacity and specific surface area using multiple regression analyses. The correlations revealed a simple regression equation for a quick prediction of soil suctions from easily determined soil properties. In order to investigate soil suction versus swell pressure behavior, the results of standard constant volume swell tests (ASTM, 1990) performed on statically compacted samples of these clay mixtures were used. A linear relationship was established between the logarithm soil suction and the swell pressure. It was also found that an experimental relationship which would directly relate the initial soil suction to the swell pressure can be established.     

东北典型黑土区有机碳遥感定量反演研究

利用多元逐步回归分析法,结合Landsat8 OLI遥感数据对该地区土壤有机碳进行定量反演.试验采集了164个土壤样品,通过3倍标准差准则对样品进行奇异点去除及数据集划分,其中120个样品作为训练集,44个样品作为验证集,建立土壤有机碳的多元逐步回归预测模型.结果表明：有机碳与Landsat8各波段反射率均显著相关;黑土有机碳光谱预测最优模型以倒数为自变量模型最优,决定系数R²=0.180,均方根误差RMSE=0.558,海伦地区适于C_org含量遥感反演,预测模型稳定性好,可以用于揭示黑土典型区C_org含量的空间分布特征.同时认为在不对土壤进行地面光谱测试的情况下,直接采用化学分析数据与遥感卫星相关联的方法预测模型拟合度有限,光谱对有机碳可解释性较低.     

Analysis of physical factors of climate and bioclimate and their effects on almonds production to increase the economy in Hebron area of Palestine

For the 1993–2009 period, we analyzed the relationship between almond yield and three climatic variables (mean annual temperature, soil water reserve, and precipitation), and four bioclimatic variables (annual ombrothermic index, water deficit, simple continentality index, and compensated thermicity index), for one major Hebron crop (soft and hard almonds). Moreover, we obtained data almond production from the Palestinian Central Bureau of Statistics, while the climate data from the Palestinian meteorological station during the study period from 1993 to 2009, and analysis is it by using bioclimatic classification of the Earth of Salvador Rivas-Martinez to study the relationship between the almond yield and climate and bioclimate factors (variables). The climatic and bioclimate variables of greatest importance to almond were used to develop regressions analysis relating yield to climatic conditions. Hebron was positively affected by annual ombrothermic index, simple continentality index, precipitation, water soil reserve, and mean annual temperature, but negatively affected by water deficit, with a large proportion of the variance explained by axis F1 (72.48%), F2 (22.38%), and axes F1and F2 (94. 86%). However, in order to produce a high amount of almonds and quality, it can be grown in the regions of the mesomediterranean region, with the value of annual ombrothemic index more than 3, compensated thermicity index between 220/220 to 350/350, simple continentality index between 14 and 20, and in areas where the average annual temperature is between 15 and 20 °C.     

昆仑山地区冻融土导热系数试验测试与预测模型研究

为探究青藏高原工程走廊带昆仑山地区冻融土导热系数基本特征,采用瞬态平面热源法对钻取的349组冻土试样和245组融土试样导热系数进行了测试,分析了五类土导热系数分布特征及天然含水率、干密度与导热系数的偏相关性,并以两者为变量因素建立了经验公式拟合、支持向量回归（SVR）和径向基（RBF）神经网络导热系数预测模型。结果表明:冻融土导热系数整体均呈粗颗粒土大于细颗粒土特征,且冻土和融土导热系数随土性分布规律存在差异;天然含水率、干密度与导热系数均呈正相关性,不同土类偏相关性结果差异明显,典型土导热系数二元经验回归方程表现为非线性拟合结果。对比三种预测模型下各典型土冻融土导热系数预测结果,全风化千枚岩、角砾及砾砂三种预测模型效果整体较佳,粉土的SVR及RBF神经网络预测精度较好;融土导热系数预测效果整体略优于冻土,SVR及RBF神经网络模型下角砾、粉土及全风化千枚岩融土导热系数预测精度较高。综合导热系数模型预测效果和误差结果分析可得,SVR和RBF神经网络模型预测效果显著优于经验方程拟合,后者针对部分土性拟合效果相对较好,可满足一般工程估算需求;SVR和RBF神经网络预测模型针对不同土性导热系数预测效果呈差异性变化,整体预测效果相当,且预测精度更高、应用土性范围更广。     

Simplified Regression Model to Predict the Strength of Reinforced Sand with Waste Polystyrene Plastic Type

A proposed regression model was developed based on experimental data using regression analysis method to predict the strength of sand reinforced with strips of waste polystyrene plastic type. Three different variables were studied to investigate the behavior and strength of reinforced sandy soil with waste plastic strips. These are the content, size and aspect ratio of plastic strips. For this purpose, a series of unconfined compression and splitting tensile tests were conducted on unreinforced and reinforced sand specimens. Test results showed that using strips of waste plastic polystyrene type improved the strength of the tested soil. Increasing content of waste plastic has a more significant effect on the enhancement of splitting tensile strength compared to the enhancement of compressive strength. Content, size and aspect ratio of waste plastic strips have significant effects on the improvement of strength. Utilization of such waste plastic type, which polystyrene, in this way will help in reducing the quantity of solid waste as well as reducing the cost of ground improvement. Results showed that multiple linear regression models can accurately predict the strength of sand reinforced with waste plastic strips within the range of the studied variables in this paper. Consequently, using such regression models will save time as well as reduce laboratory costs.     

RS & GIS-based landslide susceptibility mapping of the Balason River basin,Darjeeling Himalaya,using logistic regression (LR) model

The present study deals with the preparation of a landslide susceptibility map of the Balason River basin, Darjeeling Himalaya, using a logistic regression model based on Geographic Information System and Remote Sensing. The landslide inventory map was prepared with a total of 295 landslide locations extracted from various satellite images and intensive field survey. Topographical maps, satellite images, geological, geomorphological, soil, rainfall and seismic data were collected, processed and constructed into a spatial database in a GIS environment. The chosen landslide-conditioning factors were altitude, slope aspect, slope angle, slope curvature, geology, geomorphology, soil, land use/land cover, normalised differential vegetation index, drainage density, lineament number density, distance from lineament, distance to drainage, stream power index, topographic wetted index, rainfall and peak ground acceleration. The produced landslide susceptibility map satisfied the decision rules and ?2 Log likelihood, Cox &; Snell R-Square and Nagelkerke R-Square values proved that all the independent variables were statistically significant. The receiver operating characteristic curve showed that the prediction accuracy of the landslide probability map was 96.10%. The proposed LR method can be used in other hazard/disaster studies and decision-making.     

Simple and multiple regression models for relationship between electrical resistivity and various soil properties for soil characterization

Precise determination of engineering properties of soil is essential for proper design and successful construction of any structure. The conventional methods for determination of engineering properties are invasive, costly and time-consuming. Electrical resistivity survey is an attractive tool for delineate subsurface properties without soil disturbance. Reliable correlations between electrical resistivity and other soil properties will enable us to characterize the subsurface soil without borehole sampling. This paper presents the correlations of electrical resistivity with various properties of soil. Soil investigations, field electrical resistivity survey and laboratory electrical resistivity measurements were conducted. The results from electrical resistivity tests (field and laboratory) and laboratory tests were analyzed together to understand the interrelation between electrical resistivity and various soil properties. The test results were evaluated using simple and multiple regression analysis. From the data analysis, significant quantitative and qualitative correlations have been obtained between resistivity and moisture content, friction angle and plasticity index. Weaker correlations have been observed for cohesion, unit weight of soil and effective size (D ₁₀).     

Predicting the production rate of diamond wire saw using statistical analysis

Performance prediction of diamond wire saws is important in the cost estimation and the planning of the stone quarries. An accurate estimation of sawability helps to make the planning of the rock cutting projects more efficient. In this paper, the performance prediction of diamond wire saws in cutting carbonate rocks was studied on 14 different carbonate rocks in stone quarries located in Iran. Rock samples were collected from the quarries for laboratory tests. Uniaxial compressive strength, Brazilian tensile strength, Schmidt hammer value, and Los Angeles abrasion were determined in the laboratory. Performance prediction was evaluated using simple and multiple regression analyses. Finally, a new model was proposed for predicting the production rate of diamond wire saw. It was concluded that the production rate of carbonate rock using diamond wire saw can reliably be estimated using the developed model.     

遥感提取植物生理参数LAI/FPAR的研究进展与应用

植物生理参数LAI/FPAR是2个重要的陆地特征参量。利用遥感光谱模型并结合地面验证是提取区域尺度的LAI/FPAR最有效的途径。提取LAI/FPAR的模型主要有光谱指数模型和辐射传输模型两类,经过精确的辐射标定和大气纠正的遥感数据可以得到较高精度的LAI/FPAR数据。影响LAI/FPAR精度的因素很多,其中主要因素是像元的异质性、植被类型和物候期等。LAI/FPAR与作物产量有更直接的关系,也是大量作物生长模型的基础,利用这些参数可以实现真实的作物产量预测,特别是开展全球尺度的单产预测。     

Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks

The unconfined compressive strength (UCS) of intact rocks is an important geotechnical parameter for engineering applications. Determining UCS using standard laboratory tests is a difficult, expensive and time consuming task. This is particularly true for thinly bedded, highly fractured, foliated, highly porous and weak rocks. Consequently, prediction models become an attractive alternative for engineering geologists. The objective of study is to select the explanatory variables (predictors) from a subset of mineralogical and index properties of the samples, based on all possible regression technique, and to prepare a prediction model of UCS using artificial neural networks (ANN). As a result of all possible regression, the total porosity and P-wave velocity in the solid part of the sample were determined as the inputs for the Levenberg–Marquardt algorithm based ANN (LM-ANN). The performance of the LM-ANN model was compared with the multiple linear regression (REG) model. When training and testing results of the outputs of the LM-ANN and REG models were examined in terms of the favorite statistical criteria, which are the determination coefficient, adjusted determination coefficient, root mean square error and variance account factor, the results of LM-ANN model were more accurate. In addition to these statistical criteria, the non-parametric Mann–Whitney U test, as an alternative to the Student’s t test, was used for comparing the homogeneities of predicted values. When all the statistics had been investigated, it was seen that the LM-ANN that has been developed, was a successful tool which was capable of UCS prediction.     

碎石土滑坡稳定性一元多重属性回归模型分析

为了能在地质勘查和试验的基础上对碎石土滑坡稳定性进行评价,构建了一元多重属性回归模型。基于官家滑坡的14个工程地质剖面的实测资料,选取滑体重量、滑面倾角、滑面长度、水力坡度、浸水面积、内聚力、内摩擦角7个影响因素,采用模型对影响因素和稳定性系数进行回归分析和影响因素显著性研究,得到计算稳定性系数的回归方程,并利用新昌下山滑坡进行模型准确性验证。研究结果表明：根据模型建立的线性回归方程回归性显著,能够用模型对滑坡进行稳定性计算分析;通过模型得出对稳定性系数有显著性影响的因素,综合滑坡实际地质状况确定地下水对稳定性有显著的影响,有助于开展滑坡灾害预警预报工作和采取有效的工程治理措施;新昌下山滑坡介于稳定与较不稳定状态之间,在降雨量比较大的时段应加强监测。     

Modeling of soil penetration resistance using multiple linear regression (MLR)

In agricultural areas, the use of machinery leads to improved yields. Nevertheless, its inadequate implementation and excessive utilization can seriously affect the soil efficiency. In fact, latter can be generated by increasing the penetration resistance and subsequently, it results in the compaction phenomenon. This problem becomes considerable with the increasing report wheel/soil. The aim of this work was to evaluate the efficiency through the prediction of soil penetration resistance (Rp) using a statistical model based on moisture content, density, tractor weight, number of passes, and the wheel inflation pressure. Experimental works (211 measurements) were analyzed and the penetration resistance was modeled using multiple linear regressions (MLR). Besides, the developed model elucidates the variables affecting the accentuation of soil Rp and allows the investigation of equations for novel sampled soils. Our results showed that the parameters related to soil and tractors were significant to explain Rp. The adopted model in the MLR analysis emphasizes that the mechanical parameters of ground measurements are statistically significant in estimating and evaluating Rp. The statistical calculation of the R ² expresses 83% of the variance in Rp generated by the various parameters related to soil and tractor. In view of the importance of estimating the penetration resistance (Rp), the regression equation shows that the weight of the tractor and the number of passages contributed the most to the proposed model for the soil.     

考虑环境变量作用的滑坡变形动态灰色-进化神经网络预测研究

滑坡变形预测对于指导灾害的预防工作、保护人民的生命和财产安全具有重大实用价值。从系统论观点出发,结合岩土体流变理论和时序分析原理,在深入研究影响滑坡变形的主控环境变量基础上,将位移时序分解为趋势项和偏离项。采用灰色系统模型提取位移时序趋势项,结合遗传算法和人工神经网络建立起进化神经网络模型,逼近主控环境变量与位移偏离项之间的非线性关系。根据蠕变阶段和变形对环境变量响应情况,实时调整模型,建立起滑坡变形预测的动态灰色-进化神经网络（GM-ENN）模型。将此预测思路和方法应用于三峡库区某滑坡变形预测研究中,证实了模型的有效性和实用性,显示了动态预测的重要性。