遥感数据专题分类不确定性评价研究：进展、问题与展望

从遥感数据中提取专题类别信息是当前遥感数据最主要的应用领域之一。由于遥感分类专题信息广泛应用于各种领域，其数据质量受到越来越多的关注。不确定性是评价分类专题类别数据质量最主要的方面。回顾了遥感数据专题分类不确定性评价方法的历史，总结了当前各种评价方法及其指标体系，将这些方法归结为基于误差矩阵的方法、模糊评价方法、像元尺度上的不确定性评价方法和其它方法四大类。对每一类不确定性评价方法及其指标体系的优点和缺点进行了分析和总结，指出从理论方法研究方面，需要优先发展独立于分类方法的像元尺度上的遥感分类不确定性评价模型与指标体系，以及统一的遥感数据分类不确定性评价模型体系研究；在应用研究方面，需要加强优化空间采样设计和不确定性评价过程标准化研究。     

自适应模糊规则分类方法及在TM土地覆盖分类中的应用研究

根据自组织网络和模糊逻辑推理,实现土地覆盖自适应模糊规则分类方法。该方法通过网络的节点和权值提取出模糊规则,调整网络中节点个数(即相应增加规则节点数)和权值向量,使模糊规则自动生成,并利用模糊逻辑推理,完成TM土地覆盖分类。对拒分类的像元,自适应增加K值使其可分。该方法所得分类精度及Kapp系数与最大似然分类方法结果相比分别提高了2.7%和2.9%;与自组织网络相比,总精度相差不大,而Kapp系数低1%。实验证明,如何提取和表示非光谱知识,从而解决类别混淆等问题,是提高自适应模糊规则分类性能的关键     

地图质量的模糊综合评价

地图质量评价是判定地图质量的重要方面 ,文中在分析地图质量评价的内容、标准的基础上 ,引进模糊综合评价的方法评价地图质量 ,并给出了实例。     

基于结构分析的点状地图符号识别

通过对点状地图符号特点及其识别难度的分析,提出了结构分析为主要识别方法,在结构分析中用模糊编号算法对组成符号的基元进行编码,有效地表示出基元的空间位置关系,大大消除了细化和轻微旋转对提取基元的影响,从而提高了识别率。     

模糊分维理论在地下流体资料分析中的应用

本文采用模糊集理论与分形理论相结合的模糊分维方法,计算了聊城１井水氡和豫０１井水位动态资料,发现在１９８１年河北宁晋Ｍｓ５．８,１９８３年山东菏泽Ｍｓ５．９和１９８５年河北任县Ｍｓ５．０地震前出现了明显的降维特征。选出了两台项的模糊分维基值为０．６０和０．７５。分析结果认为,模糊分维值反映了地震前兆时序观测资料的结构变化特征,具有一定物理意义。做为地下流体前兆观测资料提取地震异常信息的新方法,有一     

FORECAST OF PREFERRED FAULT BASED ON NEURAL NETWORK

基于优势面区域稳定性评价理论和人工神经网络 (ANN)的原理和方法 ,探讨了基于 ANN的优势断裂预报神经网络算法及模型 ,并结合实例检验表明应用反传 (BP)神经网络模型判定优势断裂的新方法是有效的 ,且取得了理想的结果。     

河北蔚县崔家寨井田构造复杂程度定量评价

地质构造复杂程度定量评价是当前矿井地质工作的研究热点之一,其关键技术体现在评价指标和评价数学模型两方面。在地质分析和勘探资料处理的基础上,选取5个非连续性变形参数和5个连续性变形参数,作为地质构造复杂性评价的指标,分别采用统计综合评价和模糊综合评价数学模型,对冀西北蔚县矿区崔家寨井田主采煤层的构造复杂程度进行定量评价,取得良好效果。对比分析表明,两种评价模型在矿井构造复杂程度定量评价方面各有优劣,比较而言,模糊综合评价模型更加客观合理。      

基于GIS重庆岩溶地区生态环境脆弱度评价

重庆岩溶区属典型的生态环境脆弱区,宜耕地资源不足,土地退化严重,承受自然灾害能力低,使岩溶地区的社会经济发展和生态环境的协调性差,可持续发展能力弱。本文以重庆市岩溶地区为例,选择碳酸盐岩出露面积、山地面积、旱坡耕地面积、石漠化程度(包括轻度、中度和高度)、森林覆盖率、水土流失面积、土壤侵蚀模数、滑坡体积密度、垦殖指数、人均耕地面积、农业人口密度等13项生态环境脆弱度影响因子作为评价指标,利用层次分析法赋予指标权重,然后构建模糊数学模型对岩溶地区生态环境脆弱度进行评价研究,基于GIS技术对评价结果进行等级划分。结果表明: 重庆25个岩溶区县中潜在脆弱区县3个,轻度脆弱区县10个,中度脆弱区县6个,重度脆弱区县6个。      

Comparisons of Two Ensemble Mean Methods in Measuring the Average Error Growth and the Predictability

In this paper,taking the Lorenz system as an example,we compare the influences of the arithmetic mean and the geometric mean on measuring the global and local average error growth.The results show that the geometric mean error (GME) has a smoother growth than the arithmetic mean error (AME) for the global average error growth,and the GME is directly related to the maximal Lyapunov exponent,but the AME is not,as already noted by Krishnamurthy in 1993.Besides these,the GME is shown to be more appropriate than the AME in measuring the mean error growth in terms of the probability distribution of errors.The physical meanings of the saturation levels of the AME and the GME are also shown to be different.However,there is no obvious difference between the local average error growth with the arithmetic mean and the geometric mean,indicating that the choices of the AME or the GME have no influence on the measure of local average predictability.     

Application of a fuzzy inference system for the prediction of longshore sediment transport

A fuzzy inference system (FIS) and a hybrid adaptive network-based fuzzy inference system (ANFIS), which combines a fuzzy inference system and a neural network, are used to predict and model longshore sediment transport (LST). The measurement data (field and experimental data) obtained from Kamphuis [1] and Smith et al. [2] were used to develop the model. The FIS and ANFIS models employ five inputs (breaking wave height, breaking wave angle, slope at the breaking point, peak wave period and median grain size) and one output (longshore sediment transport rate). The criteria used to measure the performances of the models include the bias, the root mean square error, the scatter index and the coefficients of determination and correlation. The results indicate that the ANFIS model is superior to the FIS model for predicting LST rates. To verify the ANFIS model, the model was applied to the Karaburun coastal region, which is located along the southwestern coast of the Black Sea. The LST rates obtained from the ANFIS model were compared with the field measurements, the CERC [3] formula, the Kamphuis [1] formula and the numerical model (LITPACK). The percentages of error between the measured rates and the calculated LST rates based on the ANFIS method, the CERC formula (K_sig = 0.39), the calibrated CERC formula (K_sig = 0.08), the Kamphuis [1] formula and the numerical model (LITPACK) are 6.5%, 413.9%, 6.9%, 15.3% and 18.1%, respectively. The comparison of the results suggests that the ANFIS model is superior to the FIS model for predicting LST rates and performs significantly better than the tested empirical formulas and the numerical model.