學(xué)術(shù)會(huì)議
國(guó)際學(xué)術(shù)報(bào)告:Statistical Framework for Simultaneous Handling of Input, Parameter, Model and Observational Error in Spatial Uncertainty Analysis
文章來源： | 發(fā)布時(shí)間：2016-10-12 | 【打印】 【關(guān)閉】
報(bào)告人：Gerard B.M. Heuvelink,
內(nèi)容簡(jiǎn)介：Spatial uncertainty propagation analysis is usually concerned with analysis of how uncertainties in the inputs to spatial environmental models propagate to the model output. However, input uncertainty is only one of several sources of uncertainty that affect the accuracy of the model output. Often there are also uncertainties about the parameters and structure of the model. In many cases model uncertainty may even be the dominant source of uncertainty, which implies that it cannot be ignored if a realistic assessment of the overall output uncertainty is to be made. In this work we present an uncertainty analysis framework that integrates all sources of uncertainty. The framework assumes that the uncertainties about the model inputs have been derived externally, while uncertainties about model parameters and model structure are obtained using Bayesian calibration. In a first stage, model structural uncertainty is represented by an additive stochastic residual. Prior distributions for the parameter and structural uncertainty are defined, and next observations of the model output are used to calculate posterior distributions. The Bayesian calibration procedure takes into account that part of the discrepancies between observed and predicted model output is caused by input uncertainty and by observational error. Once the calibration is completed and all error sources have been characterized by probability distributions, the uncertainty propagation analysis is done using a straightforward Monte Carlo approach. The framework is illustrated with a simple example in which a multiple regression model is used to predict the moisture content at wilting point from soil porosity and moisture content at field capacity for a study area in the floodplain of the Allier river, France.
報(bào)告人簡(jiǎn)介：Gerard Heuvelink博士目前是世界土壤信息中心（ISRIC World Soil Information）計(jì)量土壤學(xué)與數(shù)字土壤制圖領(lǐng)域的高級(jí)研究員，同時(shí)也是荷蘭Wageningen大學(xué)土壤地理和景觀研究組的副教授和中國(guó)科學(xué)院地理科學(xué)與資源研究所的客座教授。他從博士期間開始從事空間數(shù)據(jù)不確定性及誤差傳遞模型研究，經(jīng)過多年工作現(xiàn)已發(fā)表250余篇關(guān)于地統(tǒng)計(jì)、空間不確定性分析和計(jì)量土壤學(xué)的相關(guān)論文和著作。論文具有較高引用率，其中多篇論文被評(píng)為期刊年度最佳論文。Gerard Heuvelink博士曾在六個(gè)歐洲研究項(xiàng)目（如，UNCERSDSS, HarmoniRiB, Intamap, UncertWeb）中擔(dān)任過主席或成員，同時(shí)還是 European Journal of Soil Science 和 Spatial Statistics 的副主編，Geoderma, Environmental and Ecological Statistics, International Journal of Applied Earth Observation and Geoinformation 和 Geographical Analysis等雜志的編委，并在多個(gè)與GIS和spatial accuracy相關(guān)的國(guó)際會(huì)議中出任指導(dǎo)委員會(huì)成員。
主 持 人：葛詠 研究員
報(bào)告時(shí)間：2014年5月23日（星期五）上午9:00
報(bào)告地點(diǎn)：中科院地理資源所2209會(huì)議室
主辦單位：資源與環(huán)境信息系統(tǒng)國(guó)家重點(diǎn)實(shí)驗(yàn)室
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