基于GIS的天山公路地质灾害危险性评价

结合天山公路实际,引入模糊理论中的多因素综合评判模型,对天山公路地质灾害的危险性进行了研究,开发了地质灾害危险性评价系统,并以专题图的形式显示评价结果。通过对结果分析,地质灾害危险性评价图中的危险等级分布,大致与野外调查一致,表明该系统对地质灾害危险性评价切实可行。     

山西省大同市农业生态气候适宜度评价

文章选择黄土高原边缘,颇具生态地理意义的山西省大同市及所辖7个县区为研究对象,依据该区域1971~2000年的长期实测气象资料,在借鉴前人建立的农业生态气候适宜度动态模型的基础上,经过多次作物实验,尝试性拟合出符合区域特点的典型气候要素适宜度隶属函数;分区计算了农业生态资源指数 、效能指数及利用系数,总结出区域气温及降水量适宜度曲线大致呈 "∩"型分布规律,峰值出现在每年的6~8月之间;日照百分率适宜度曲线呈 "∪"型分布规律,谷值出现在每年的7月。在SPSS统计软件环境中,以各区域累年逐月平均效能指数为分类实体,进行模糊动态聚类,提出研究区农业生态气候适宜度的三种等级模式。     

台湾以东黑潮路径识别与变化规律

为研究对中国台湾以东海域黑潮路径及其变化,本文基于法国空间局AVISO中心提供的1993—2015年的卫星遥感海表流场逐日资料,对121°—125°E,22.4°—25°N海域黑潮路径进行了逐日识别,得到了共计23年累计8400天的台湾以东黑潮流轴的逐日路径,并研究其在不同纬度的流轴位置及其对应的表面黑潮流量的时空变化规律。主要结论如下:(1)采用模糊C-均值聚类法对台湾以东黑潮流轴路径进行聚类分析,发现台湾以东黑潮流轴在24°N以南出现明显摆动,形成正常和偏东两种路径;黑潮流轴存在明显的时间变化,流轴偏东现象年平均大约出现25次,大致每隔3年出现一次偏东较少的现象,各月流轴偏东次数以4、5月最少,10月至次年3月较多;(2)台湾以东黑潮表面流量大小在6.2—8.3×104m2/s之间;总体上来说,纬度越高流量越大,在23.5°N左右范围内存在一个流量低值中心;在24.3°N以北流量总体较大,且增长趋势稳定,同时表面流量大小具有较强的季节和年际变化特征。     

Using advanced soft computing techniques for regional shoreline geoid model estimation and evaluation

This study aims at evaluating the global geoid model for a regional shoreline fitting using advanced soft computing techniques and global navigation satellite system/leveling measurements. Artificial neural networks, fuzzy logic, and least square support vector machine models are developed and used to fit the global geoid model for the north coastal Egyptian line. In addition, a novel estimation geoid model is designed and evaluated based on the latest global geoid models. The results of the three estimation models show that they can be used to correct the shoreline geoid model, in terms of root mean square error that ranges from 1.7 to 8.5?cm. Moreover, it is found that the least square vector machine model is a competitive approach with certain advantage in solving complex problems represented by missing data.     

综合评价方法在水质评价应用中的比较研究

以黄河某取水口监测点19981999年的水质为评价对象,利用灰色关联分析法、模糊综合评价法和物元分析法分别进行了综合评价,结果表明,模糊综合评价和物元分析法的评价结果一致,"中心化"灰色关联法评价结果与上述两方法的评价结果一致.     

运用模糊数学法评价项城地下水水质

以频率统计法选择Hg、COD等评价因子,从隶属度、权重的计算以及模糊模型的选择等方面,详细介绍模糊数学法运用于项城市浅层地下水水质现状评价的过程,并结合综合指数评价结果对其进行简析.     

Fuzzy Optimization Neural Network Approach for Ice Forecast in the Inner Mongolia Reach of the Yellow River/Approche d'Optimisation Floue de Réseau de Neurones pour la Prévision de la Glace Dans le Tronçon de Mongolie Intérieure du Fleuve Jaune

Abstract

In ice forecasting, a key problem is the forecast of freeze-up and break-up dates. Ice-water mechanics and the principle of heat-exchange were mainly adopted in previous research. However, the mathematical models in these studies are complex and many parameters are required in relation to upstream and/or downstream gauging stations. Moreover, too many assumptions or simplifications for these parameters and constraints directly lead to low accuracy of the models and limitations as to their practical applications. This paper develops a fuzzy optimization neural network approach for the forecast of freeze-up date and break-up date. The Inner Mongolia reach lies in the top north of the Yellow River, China. Almost every year ice floods occur because of its special geographical location, hydrometeorological conditions and river course characteristics. Therefore, it is of particular importance for ice flood prevention to forecast freeze-up date and break-up date accurately. A case study in this region shows that the proposed methodology may allow obtaining useful results.     

An integrated procedure to evaluate hydrological models

The growing concern for health‐related problems deriving from pollutants leaching is driving national and international administrations to support the development of tools for evaluating the effects of alternate management scenarios and identifying vulnerable areas. Cropping systems models are powerful tools for evaluating leachates under different environmental, social, and management conditions. As percolating water is the transport vehicle for pollutants transport in soil, a reliable evaluation of water balance models is a fundamental prerequisite for investigating pesticides and nitrate fate. As specific approaches for the evaluation of multi‐layer evolution of state variables are missing, we propose a fuzzy‐based, integrated indicator (I_SWC: 0, best; 1, worst) for a comprehensive evaluation of soil water content (SWC) simulations. We aggregated error metrics with others quantifying the homogeneity of errors across different soil layers, the capability of models to reproduce complex dynamics function of both time and soil depth, and model complexity. We tested I_SWC on a sample dataset where the models CropSyst and CERES‐Wheat were used to simulate SWC for winter wheat systems. I_SWC revealed that, in the explored conditions, the global assessment of the two models' performances allowed identification of CropSyst as the best (average I_SWC = 0·441, with a value of 0·537 obtained by CERES‐Wheat), although each model prevailed for some of the metrics. CropSyst presented the highest accuracy (average agreement module = 0·400), whereas CERES‐Wheat's accuracy was slightly worse, although achieved with a simplified modelling approach (average Akaike Information Criterion = − 230·44), thereby favouring large‐area applicability. The non‐univocal scores achieved by the models for the different metrics support the use of multi‐metric evaluation approaches for quantifying the different aspects of water balance model performances. Copyright © 2010 John Wiley & Sons, Ltd.     

Rangeland production: use of models incorporating aggregated knowledge and fuzzy construction

This work reports on a method using fuzzy membership functions to construct an aggregated interaction matrix in which the summation of variables is scaled according to the way rainfall and soil variables affect water availability to plants and hence influence rangeland productivity. Aggregation of the variables gives a comprehensive value which can be used to predict production. The model increases the predicability of production to 81% compared to models using rainfall alone and a multiplicative parametric one which give predictibilities of 61 and 76% respectively. The results showed that (1) the importance of rainfall in determining production was most important at lower rainfalls i.e. <350 mm; (2) soil texture and particularly slope were important through out the rainfall range (149–700 mm) investigated; and (3) soil depth was only important at the higher >350 mm rainfalls. The aggregated interaction matrix gives a measure of land productive capability.