滑坡泥石流信息遥感获取与空间预测

On the issue of geological hazard evaluation（GHE）, taking remote sensing and GIS systems as experimental environment, assisting with some programming development, this thesis combines multi-knowledges of geo-hazard mechanism, statistic learning, remote sensing (RS), high-spectral recognition, spatial analysis, digital photogrammetry as well as mineralogy, and selects geo-hazard samples from Hong Kong and Three Parallel River region as experimental data, to study two kinds of core questions of GHE, geo-hazard information acquiring and evaluation model. In the aspect of landslide information acquiring by RS, three detailed topics are presented, image enhance for visual interpretation, automatic recognition of landslide as well as quantitative mineral mapping. As to the evaluation model, the latest and powerful data mining method, support vector machine (SVM), is introduced to GHE field, and a serious of comparing experiments are carried out to verify its feasibility and efficiency. Furthermore, this paper proposes a method to forecast the distribution of landslides if rainfall in future is known baseing on historical rainfall and corresponding landslide susceptibility map. The details are as following: (a) Remote sensing image enhancing methods for geo-hazard visual interpretation. The effect of visual interpretation is determined by RS data and image enhancing method, for which the most effective and regular technique is image merge between high-spatial image and multi-spectral image, but there are few researches concerning the merging methods of geo-hazard recognition. By the comparing experimental of six mainstream merging methods and combination of different remote sensing data source, this thesis presents merits of each method ,and qualitatively analyzes the effect of spatial resolution, spectral resolution and time phase on merging image. (b) Automatic recognition of shallow landslide by RS image. The inventory of landslide is the base of landslide forecast and landslide study. If persistent collecting of landslide events, updating the geo-hazard inventory in time, and promoting prediction model incessantly, the accuracy of forecast would be boosted step by step. RS technique is a feasible method to obtain landslide information, which is determined by the feature of geo-hazard distribution. An automatic hierarchical approach is proposed to identify shallow landslides in vegetable region by the combination of multi-spectral RS imagery and DEM derivatives, and the experiment is also drilled to inspect its efficiency. (c) Hazard-causing factors obtaining. Accurate environmental factors are the key to analyze and predict the risk of regional geological hazard. As to predict huge debris flow, the main challenge is still to determine the startup material and its volume in debris flow source region. Exerting the merits of various RS technique, this thesis presents the methods to obtain two important hazard-causing factors, DEM and alteration mineral, and through spatial analysis, finds the relationship between hydrothermal clay alteration minerals and geo-hazards in the arid-hot valleys of Three Parallel Rivers region. (d) Applying support vector machine (SVM) to landslide susceptibility mapping. Introduce the latest and powerful statistical learning theory, SVM, to RGHE. SVM that proved an efficient statistic learning method can deal with two-class and one-class samples, with feature avoiding produce ‘pseudo’ samples. 55 years historical samples in a natural terrain of Hong Kong are used to assess this method, whose susceptibility maps obtained by one-class SVM and two-class SVM are compared to that obtained by logistic regression method. It can conclude that two-class SVM possesses better prediction efficiency than logistic regression and one-class SVM. However, one-class SVM, only requires failed cases, has an advantage over the other two methods as only "failed" case information is usually available in landslide susceptibility mapping. (e) Predicting the distribution of rainfall-induced landslides by time-series analysis. Rainfall is the most dominating factor to bring in landslides. More than 90% losing and casualty by landslides is introduced by rainfall, so predicting landslide sites under certain rainfall is an important geological evaluating issue. With full considering the contribution of stable factors (landslide susceptibility map) and dynamic factors (rainfall), the time-series linear regression analysis between rainfall and landslide risk mapis presented, and experiments based on true samples prove that this method is perfect in natural region of Hong Kong. The following 4 practicable or original findings are obtained: 1) The RS ways to enhance geo-hazards image, automatic recognize shallow landslides, obtain DEM and mineral are studied, and the detailed operating steps are given through examples. The conclusion is practical strongly. 2) The explorative researching about relationship between geo-hazards and alteration mineral in arid-hot valley of Jinshajiang river is presented. Based on standard USGS mineral spectrum, the distribution of hydrothermal alteration mineral is mapped by SAM method. Through statistic analysis between debris flows and hazard-causing factors, the strong correlation between debris flows and clay minerals is found and validated. 3) Applying SVM theory (especially one-class SVM theory) to the landslide susceptibility mapping and system evaluation for its performance is also carried out, which proves that advantages of SVM in this field. 4) Establishing time-serial prediction method for rainfall induced landslide distribution. In a natural study area, the distribution of landslides induced by a storm is predicted successfully under a real maximum 24h rainfall based on the regression between 4 historical storms and corresponding landslides.

Estimation of soil organic partition coefficients: from retention factors measured by soil column chromatography with water as eluent

The retention factors (k) of 104 hydrophobic organic chemicals (HOCs) were measured in soil column chromatography (SCC) over columns filled with three naturally occurring reference soils and eluted with Milli-Q water. A novel method for the estimation of soil organic partition coefficient (K-oc) was developed based on correlations with k in soil/water systems. Strong log K-oc versus log k correlations (r>0.96) were found. The estimated K-oc values were in accordance with the literature values with a maximum deviation of less than 0.4 log units. All estimated K-oc values from three soils were consistent with each other. The SCC approach is promising for fast screening of a large number of chemicals in their environmental applications. (C) 2002 Elsevier Science B.V. All rights reserved.

Linear solvation energy relationships regarding sorption and retention properties of hydrophobic organic compounds in soil leaching column chromatography

The capacity factors of a series of hydrophobic organic compounds (HOCs) were measured in soil leaching column chromatography (SLCC) on a soil column, and in reversed-phase liquid chromatography on a C-18 column with different volumetric fractions (phi) of methanol in methanol-water mixtures. A general equation of linear solvation energy relationships, log(XYZ) = XYZ(0) + mV(1)/100 + spi* + bbeta(m) + aalpha(m), was applied to analyze capacity factors (k'), soil organic partition coefficients (K-oc) and octanol-water partition coefficients (P). The analyses exhibited high accuracy. The chief solute factors that control log K-oc, log P, and log k' (on soil and on C-18) are the solute size (V-1/100) and hydrogen-bond basicity (beta(m)). Less important solute factors are the dipolarity/polarizability (pi*) and hydrogen-bond acidity (alpha(m)). Log k' on soil and log K-oc have similar signs in four fitting coefficients (m, s, b and a) and similar ratios (m:s:b:a), while log k' on C-18 and log P have similar signs in coefficients (m, s, b and a) and similar ratios (m:s:b:a). Consequently, log k' values on C-18 have good correlations with log P (r > 0.97), while log k' values on soil have good correlations with log K-oc (r > 0.98). Two K-oc estimation methods were developed, one through solute solvatochromic parameters, and the other through correlations with k' on soil. For HOCs, a linear relationship between logarithmic capacity factor and methanol composition in methanol-water mixtures could also be derived in SLCC. (C) 2002 Elsevier Science Ltd. All rights reserved.

A column method for determination of soil organic partition coefficients of eight pesticides

A column method was developed to conveniently and reliably determine the soil organic partition coefficients (K-oc) of three insecticides (methiocarb, azinphos-methyl, fenthion), four fungicides (triadimenol, fuberidazole, tebuconazole, pencycuron), and one herbicide (atrazine), in which real soil acted as a stationary phase and the water solution of pesticide as an eluent. The processes of sorption equilibrium were directly shown through a breakthrough curve(BTC). The log K-oc values are 1.69, 1.95, 2.25, 2.55, 2.69, 2.67, 3.10, and 3.33 for atrazine, triadimenol, methiocarb, fuberidazole, azinphos-methyl, tebuconazole, fenthion and pencycuron, respectively.

Soil column chromatography for correlation between capacity factors and soil organic partition coefficients for eight pesticides

A soil column chromatographic method was developed to measure the capacity factors (k') of pesticides, in which soil acted as a stationary phase and methanol-water mixture as an eluent. The k' values of eight pesticides, including three insecticides (methiocarb, azinphos-methyl, fenthion), four fungicides (triadimenol, fuberidazole, tebuconazole, pencycuron), and one herbicide (atrazine), were found to be well fitted to a retention equation, ln k'=ln k(w)'-S-phi. Due to similar interactions of solutes with soil and solvent in both sorption determination and retention experiment, log k' has a good linear correlation with log K-oc for the eight pesticides from different classes, in contrast with poor correlation between log k' from C-18 column and log K-oc. So the method provides a tool for rapid estimation of K-oc from experimental k'. (C) 1999 Elsevier Science Ltd. All rights reserved.

社会形势预警的几种建模方法比较

Based on social survey data conducted by local research group in some counties executed in the nearly past five years in China, the author proposed and solved two kernel problems in the field of social situation forecasting: i) How can the attitudes’ data on individual level be integrated with social situation data on macrolevel; ii) How can the powers of forecasting models’ constructed by different statistic methods be compared? Five integrative statistics were applied to the research: 1) algorithm average (MEAN); 2) standard deviation (SD); 3) coefficient variability (CV); 4) mixed secondary moment (M2); 5) Tendency (TD). To solve the former problem, the five statistics were taken to synthesize the individual and mocrolevel data of social situations on the levels of counties’ regions, and form novel integrative datasets, from the basis of which, the latter problem was accomplished by the author: modeling methods such as Multiple Regression Analysis (MRA), Discriminant Analysis (DA) and Support Vector Machine (SVM) were used to construct several forecasting models. Meanwhile, on the dimensions of stepwise vs. enter, short-term vs. long-term forecasting and different integrative (statistic) models, meta-analysis and power analysis were taken to compare the predicting power of each model within and among modeling methods. Finally, it can be concluded from the research of the dissertation: 1) Exactly significant difference exists among different integrative (statistic) models, in which, tendency (TD) integrative models have the highest power, but coefficient variability (CV) ones have the lowest; 2) There is no significant difference of the power between stepwise and enter models as well as short-term and long-term forecasting models; 3) There is significant difference among models constructed by different methods, of which, support vector machine (SVM) has the highest statistic power. This research founded basis in all facets for exploring the optimal forecasting models of social situation’s more deeply, further more, it is the first time methods of meta-analysis and power analysis were immersed into the assessments of such forecasting models.

中国企业员工组织承诺研究

The research objectives were to investigate the psychological structure of employees' organizational commitments(OCs), and its antecedents, and to examine the relative effects of employees' OCs to their performances. In order to deeply uncover the nature of OCs, some standard methods, such as in-depth interview, focus-group, semi-open questionnaire, standard questionnaire etc., were employed. In data analysis, not only some common statistical methods, such as multivariate analysis of variance, cross-table analysis, factor analysis, but also some forefront ones, such as confirmatory factor analysis and path analysis of SEM, were used. The paper covers six chapters: 1) In the first chapter, Firstly some previous empirical studies, which examined structures, antecedents, correlates, and/or consequences of organizational commitment in China and Western countries, were summarized. This summary covers most of the respectable researchers' works of this field, such as H.S.Becker, B.Buchanan, L.W.Porter, G. Ritzer, H.M.Trice, J.A.Alutto, L.G.Hrebiniak, R.T.Mowday, J.P.Meyer, N.J.Allen, G.W.McGee, R.C.Ford, R.Eisenberger, etc. Then three theoretical hypothesis were put forward as following: ① In China, OCs should be multidimensional psychological structures, which means there should exist more than one type of OCs; ② There should be some different antecedents to different OCs; ③ Employees with different types of OC should perform differently in their works. Finally the theoretical and practical significance were discussed. 2) In the second chapter, great efforts were made to investigate the OC types. Firstly, in-depth interview with managers and employees, semi-open questionnaire, and some other methods were used in the pilot research to gather much qualitative material. Then OC questionnaire was designed to get quantitative data in about 20 enterprises, including state-owned, collective-owned, wholly foreign-funded, and joint-ventures. During revising of this questionnaire, there were about 5000 samples surveyed. after factor analysis, the data shows that there should be 5 types of OCs in China, which were respectively named as Affective Commitment, Normative commitment, Ideal Commitment, Economic Commitment, Choice Commitment. Thirdly, confirmatory factor analysis method was used to successfully confirm this 5-factor model. Finally, Cronbach a and test-retest correlate indicate that this questionnaire is reliable. Since factor analysis result has show its construct validity, a simple criterion-related validity research was conducted. 3) In order to investigate the correlation between different OC and employee performance and different antecedents of OC, 5 other questionnaires, such as Employee Satisfaction Questionnaire, Perceived Organizational Support Questionnaire, Social Exchange Questionnaire, Altruism Scale, and Leader Confidence Scale were revised in the third chapter. 4)In the fourth chapter, a lot of correlates, cross-table analysis were conducted to show the correlation between different OCs and 10 performances, which indicate employees with different OCs will show different performance in 10 variables, such as altruism, etc. 5) In the fifth chapter, correlate analysis, multivariate of analysis, and path analysis of SEM were used to investigate the antecedents of OC. A satisfactory model showing the correlation between OC and their antecedents was confirmed. 6) In the last chapter, all researches about OC, and its limitations were summarized.

Prediction of soil organic partition coefficients by a soil leaching column chromatographic method

The soil organic partition coefficient (K-oc) is one of the most important parameters to depict the transfer and fate of a chemical in the soil-water system. Predicting K-oc by using a chromatographic technique has been developing into a convenient and low-cost method. In this paper, a soil leaching column chromatograpy (SLCC) method employing the soil column packed with reference soil GSE 17201 (obtained from Bayer Landwirtschaftszentrum, Monheim, Germany) and methanol-water eluents was developed to predict the K-oc of hydrophobic organic chemicals (HOCs), over a log K-oc range of 4.8 orders of magnitude, from their capacity factors. The capacity factor with water as an eluent (k(w)') could be obtained by linearly extrapolating capacity factors in methanol-water eluents (k') with various volume fractions of methanol (phi). The important effects of solute activity coefficients in water on k(w)' and K-oc were illustrated. Hence, the correlation between log K-oc and log k(w)' (and log k') exists in the soil. The correlation coefficient (r) of the log K-oc vs. log k(w)' correlation for 58 apolar and polar compounds could reach 0.987, while the correlation coefficients of the log K-oc-log k' correlations were no less than 0.968, with phi ranging from 0 to 0.50. The smaller the phi, the higher the r. Therefore, it is recommended that the eluent of smaller phi, such as water, be used for accurately estimating K-oc. Correspondingly, the r value of the log K-oc-log k(w)' correlation on a reversed-phase Hypersil ODS (Thermo Hypersil, Kleinostheim, Germany) column was less than 0.940 for the same solutes. The SLCC method could provide a more reliable route to predict K-oc indirectly from a correlation with k(w)' than the reversed-phase liquid chromatographic (RPLC) one.