Geological data are usually of the characteristics of multi-source, large amount and multi-scale. The construction of Spatial Information Grid overcomes the shortages of personal computers when dealing with geological...Geological data are usually of the characteristics of multi-source, large amount and multi-scale. The construction of Spatial Information Grid overcomes the shortages of personal computers when dealing with geological data. The authors introduce the definition, architecture and flow of mineral resources assessment by weights of evidence model based on Spatial Information Grid (SIG). Meanwhile, a case study on the prediction of copper mineral occurrence in the Middle-Lower Yangtze metallogenic belt is given. The results show that mineral resources assessement based on SIG is an effective new method which provides a way of sharing and integrating distributed geospatial information and improves the efficiency greatly.展开更多
We consider n observations from the GARCH-type model: Z = UY, where U and Y are independent random variables. We aim to estimate density function Y where Y have a weighted distribution. We determine a sharp upper boun...We consider n observations from the GARCH-type model: Z = UY, where U and Y are independent random variables. We aim to estimate density function Y where Y have a weighted distribution. We determine a sharp upper bound of the associated mean integrated square error. We also make use of the measure of expected true evidence, so as to determine when model leads to a crisis and causes data to be lost.展开更多
The objective of prospectivity modeling is prediction of the conditional probability of the presence T = 1 or absence T = 0 of a target T given favorable or prohibitive predictors B, or construction of a two classes {...The objective of prospectivity modeling is prediction of the conditional probability of the presence T = 1 or absence T = 0 of a target T given favorable or prohibitive predictors B, or construction of a two classes {0,1} classification of T. A special case of logistic regression called weights-of-evidence （WofE） is geolo- gists＇ favorite method of prospectivity modeling due to its apparent simplicity. However, the numerical simplicity is deceiving as it is implied by the severe mathematical modeling assumption of joint conditional independence of all predictors given the target. General weights of evidence are explicitly introduced which are as simple to estimate as conventional weights, i.e., by counting, but do not require conditional independence. Complementary to the regres- sion view is the classification view on prospectivity modeling. Boosting is the construction of a strong classifier from a set of weak classifiers. From the regression point of view it is closely related to logistic regression. Boost weights-of-evidence （BoostWofE） was introduced into prospectivity modeling to counterbalance violations of the assumption of conditional independence even though relaxation of modeling assumptions with respect to weak classifiers was not the （initial） purpose of boosting. In the original publication of BoostWofE a fabricated dataset was used to ＂validate＂ this approach. Using the same fabricated dataset it is shown that BoostWofE cannot generally compensate lacking condi- tional independence whatever the consecutively proces- sing order of predictors. Thus the alleged features of BoostWofE are disproved by way of counterexamples, while theoretical findings are confirmed that logistic regression including interaction terms can exactly com- pensate violations of joint conditional independence if the predictors are indicators.展开更多
Landslide susceptibility mapping is a very important tool to identify potential landslide-prone areas. In this work, weight of evidence method is applied to obtain landslide susceptibility assessment. Weight of eviden...Landslide susceptibility mapping is a very important tool to identify potential landslide-prone areas. In this work, weight of evidence method is applied to obtain landslide susceptibility assessment. Weight of evidence model is commonly applied in the landslide study as it is widely acceptable and easy to use. The objective of this paper is to prepare the landslide susceptibility map of Lung Khola catchment, Pyuthan District of Nepal. Altogether, 84 landslides were identified after landslide inventory. The thematic layers of all causative factors and existing landslides are prepared in Arc GISsoftware. Mainly, Digital Elevation Model (DEM) based causative factors and field data were used to prepare the data layers of the causative factors. In this research, 8 intrinsic factors were used for the landslide assessment. South East and East facing aspects, slope >60 degrees, elevation ranges 1300 - 1700 m, phylitic rocks and agricultural land followed by forest are the major contributors of landslide hazard in the study area. The weight of evidence model was validated by using area under curve method. The success rate curve showed the accuracy of 73.16%. It can be concluded that weight of evidence model is suitable model for landslide susceptibility analysis and the area is highly susceptible to landslide occurrence.展开更多
基金Supported by the National High Technology Research and Development Programof China(863 Program) Nos .2002AA134010 and 2002AA131010
文摘Geological data are usually of the characteristics of multi-source, large amount and multi-scale. The construction of Spatial Information Grid overcomes the shortages of personal computers when dealing with geological data. The authors introduce the definition, architecture and flow of mineral resources assessment by weights of evidence model based on Spatial Information Grid (SIG). Meanwhile, a case study on the prediction of copper mineral occurrence in the Middle-Lower Yangtze metallogenic belt is given. The results show that mineral resources assessement based on SIG is an effective new method which provides a way of sharing and integrating distributed geospatial information and improves the efficiency greatly.
文摘We consider n observations from the GARCH-type model: Z = UY, where U and Y are independent random variables. We aim to estimate density function Y where Y have a weighted distribution. We determine a sharp upper bound of the associated mean integrated square error. We also make use of the measure of expected true evidence, so as to determine when model leads to a crisis and causes data to be lost.
文摘The objective of prospectivity modeling is prediction of the conditional probability of the presence T = 1 or absence T = 0 of a target T given favorable or prohibitive predictors B, or construction of a two classes {0,1} classification of T. A special case of logistic regression called weights-of-evidence （WofE） is geolo- gists＇ favorite method of prospectivity modeling due to its apparent simplicity. However, the numerical simplicity is deceiving as it is implied by the severe mathematical modeling assumption of joint conditional independence of all predictors given the target. General weights of evidence are explicitly introduced which are as simple to estimate as conventional weights, i.e., by counting, but do not require conditional independence. Complementary to the regres- sion view is the classification view on prospectivity modeling. Boosting is the construction of a strong classifier from a set of weak classifiers. From the regression point of view it is closely related to logistic regression. Boost weights-of-evidence （BoostWofE） was introduced into prospectivity modeling to counterbalance violations of the assumption of conditional independence even though relaxation of modeling assumptions with respect to weak classifiers was not the （initial） purpose of boosting. In the original publication of BoostWofE a fabricated dataset was used to ＂validate＂ this approach. Using the same fabricated dataset it is shown that BoostWofE cannot generally compensate lacking condi- tional independence whatever the consecutively proces- sing order of predictors. Thus the alleged features of BoostWofE are disproved by way of counterexamples, while theoretical findings are confirmed that logistic regression including interaction terms can exactly com- pensate violations of joint conditional independence if the predictors are indicators.
文摘Landslide susceptibility mapping is a very important tool to identify potential landslide-prone areas. In this work, weight of evidence method is applied to obtain landslide susceptibility assessment. Weight of evidence model is commonly applied in the landslide study as it is widely acceptable and easy to use. The objective of this paper is to prepare the landslide susceptibility map of Lung Khola catchment, Pyuthan District of Nepal. Altogether, 84 landslides were identified after landslide inventory. The thematic layers of all causative factors and existing landslides are prepared in Arc GISsoftware. Mainly, Digital Elevation Model (DEM) based causative factors and field data were used to prepare the data layers of the causative factors. In this research, 8 intrinsic factors were used for the landslide assessment. South East and East facing aspects, slope >60 degrees, elevation ranges 1300 - 1700 m, phylitic rocks and agricultural land followed by forest are the major contributors of landslide hazard in the study area. The weight of evidence model was validated by using area under curve method. The success rate curve showed the accuracy of 73.16%. It can be concluded that weight of evidence model is suitable model for landslide susceptibility analysis and the area is highly susceptible to landslide occurrence.
