Indian summer monsoon rainfall (ISMR) forecasting using time series data: A fuzzy-entropy-neuro based expert system

This study presents a model to forecast the Indian summer monsoon rainfall(ISMR)(June-September)based on monthly and seasonal time scales. The ISMR time series data sets are classified into two parts for modeling purposes, viz.,(1) training data set(1871-1960), and(2) testing data set(1961-2014).Statistical analyzes reflect the dynamic nature of the ISMR, which couldn't be predicted efficiently by statistical and mathematical based models. Therefore, this study suggests the usage of three techniques,viz., fuzzy set, entropy and artificial neural network(ANN). Based on these techniques, a novel ISMR time series forecasting model is designed to deal with the dynamic nature of the ISMR. This model is verified and validated with training and testing data sets. Various statistical analyzes and comparison studies demonstrate the effectiveness of the proposed model.     

基于GIS和专家知识的滇西南地区滑坡敏感性模糊逻辑推理方法

为了充分识别和有效减轻滑坡灾害风险,对滇西南南涧(约470 km²)和凤庆—昌宁(约2300 km²)两个研究区开展了基于GIS和专家知识的滑坡敏感性模糊逻辑评价研究。通过检查模型计算得到的历史滑坡点敏感性值与整个研究区域的滑坡敏感性平均值是否不同来评价本方法的性能,用Z值检查来测试差异的统计显著性。计算结果显示,南涧地区的Z值为4.1,相应的P值小于0.001,表明通过模型计算得到的滑坡敏感性值是该区域滑坡事件发生的良好指标;凤庆—昌宁地区的Z值为8.93,相应的P值小于0.001。在此基础上,采用自然断点法对滑坡敏感性值进行分类,根据分类结果将滑坡敏感性水平划分成5个等级:极低(0.0~0.001)、较低(0.001~0.051)、中等(0.051~0.394)、较高(0.394~0.557)和极高(0.557~1.0)。敏感性极低和较低的地区没有发现历史滑坡记录;敏感性极高地区的历史滑坡密度约是敏感性较高地区的4倍,约为敏感性中等地区的10倍。凤庆—昌宁地区的研究结果表明,从区域专家群中提取的滑坡敏感性与环境因子关系的知识可以外延到滇西南其它地区。     

Assessing traffic noise impact based on probabilistic and fuzzy approaches under uncertainty

Traffic noise can cause severe sound pollution for human communities. This paper proposes a hybrid approach to assess traffic noise impact under uncertainty. There are many factors influencing traffic noise level, but only three traffic parameters, namely, traffic flow, traffic speed and traffic component, are highly uncertain. These uncertain parameters are represented by probability distributions, and Monte Carlo simulations are performed to generate a noise distribution after considering about other certain influencing factors. Fuzzy set and binary fuzzy relations as well as probability analysis method are applied to identify the predicted traffic noise impacts in qualitatively and quantitatively. The applicability of this proposed technique is demonstrated using a case study.     

An integrated fuzzy-stochastic modeling approach for assessing health-impact risk from air pollution

High concentrations of air pollutants in the ambient environment can result in breathing problems with human communities. Effective assessment of health-impact risk from air pollution is important for supporting decisions of the related detection, prevention, and correction efforts. However, the quality of information available for environmental/health risk assessment is often not satisfactory enough to be presented as deterministic numbers. Stochastic method is one of the methods for tackling those uncertainties, by which uncertain information can be presented as probability distributions. However, if the uncertainties can not be presented as probabilities, they can then be handled through fuzzy membership functions. In this study, an integrated fuzzy-stochastic modeling (IFSM) approach is developed for assessing air pollution impacts towards asthma susceptibility. This development is based on Monte Carlo simulation for the fate of SO₂ in the ambient environment, examination of SO₂ concentrations based on the simulation results, quantification of evaluation criteria using fuzzy membership functions, and risk assessment based on the combined fuzzy-stochastic information. The IFSM entails (a) simulation for the fate of pollutants in ambient environment, with the consideration of source/medium uncertainties, (b) formulation of fuzzy air quality management criteria under uncertain human-exposure pathways, exposure dynamics, and SPG-response variations, and (c) integrated risk assessment under complexities of the combined fuzzy/stochastic inputs of contamination level and health effect (i.e., asthma susceptibility). The developed IFSM is applied to a study of regional air quality management. Reasonable results have been generated, which are useful for evaluating health risks from air pollution. They also provide support for regional environmental management and urban planning.     

不同菠萝蜜株系果实品质分析

为选育出果实品质优良的菠萝蜜株系,对6个特色菠萝蜜株系(E2、E5、E6、E7、E9、E10)的果实品质指标进行分析,并依据主要指标进行模糊综合评判,评价各株系品质优劣。结果表明:株系E2、E5、E6、E7总体外观品质各有优势;株系E6有机酸、维生素C及可溶性固形物质量分数最高,显著高于其他株系(p值<0.01),糖分和可溶性蛋白质量分数也处于较高水平;其次为株系E5,除了维生素C和可溶性蛋白质量分数偏低外,其糖分、有机酸和可溶性固形物质量分数均较高;模糊综合评判结果表明,株系E6和E5属于果实品质较好的株系。     

优选泥浆配方的模糊数学方法

泥浆试验属于多因素多指标评价试验。应用模糊数学理论中的多目标模糊决策法,根据不同地层的钻进需要,借助计算机编程计算优选泥浆配方是一个行之有效的方法。      

模糊-统计分析方法在新西兰地震危险性估计中的应用

把模糊聚类分析、模糊地震活动平静异常识别、模糊分维、b值谱分析等方法应用于新西兰南、北两区的地震危险性估计当中.结果表明,这5种方法在此处均可得到有效的应用.     

基于GIS农田土地质量评价与立地分析——以京郊房山区良乡为例

为揭示农田利用可行性 ,提出农田立地概念 ,建立农田评价体系 ,以京郊房山区良乡为例 ,基于GIS技术评价农田质量 ,分析农田立地 ,综合评价农田 ,为划定基本农田提供客观依据 ;根据农田评价评分数值分布 ,通过模糊稀疏度算法模型 ,实现农田自动分等定级 ,减少了界定农田等级指标的主观性。     

南海大鹏湾海洋褐胞藻赤潮及其成因

于１９９１年３月２０－２１日在大鹏湾首次发生海洋褐胞藻赤潮。对此分析了赤潮发生前后海洋环境因素的变化及其与赤潮的关系；以风速，气压，盐度，温度，磷酸盐，硝酸盐，铵盐，铁离子等８项指标为基础，改变参数组合，对采自１９９１年３－５月特定站位（Ｓｏ）的各样方进行聚类分析和比较。结果表明，在盐度为３１－３２和水温为２０℃的适宜条件下，铁离子和风速是形成本次赤潮的主要环境要素；大量营养盐尤其是硝酸盐度的增加     

多雷达反演参量联合的短时强降水识别方法研究

借鉴美国ANC(NCAR Auto-Nowcast System)基于模糊逻辑原理的临近预报技术思路,采用SWAN(Severe Weather Automatic Nowcast System)系统中雷达基本产品及其反演产品,研发了多参量联合的短时强降水落区识别算法,重点研究因子选取、误差分析及参数设置。基于历史资料分析结果筛选出1h定量降水预报QPF(Quantity Precipitation Forecast)、雨强RI(Rain Intensity)、垂直累积液态水含量VIL(Vertically Integrated Liquid Water)、回波顶高ET(Echo Top)预报量作为识别因子,并设计出相应的隶属函数。结果表明,多因子联合识别短时强降水算法的CSI(Critical Success Index)指数为0.18,性能高于QPF判别法及仅使用单因子识别的模糊逻辑方法。强降水指数HRI(Heavy RainIndex)阈值设置测试表明,选择合适的HRI阈值有助于发挥算法性能,统计结果表明取0.5～0.6比较合理。同时,较高的空报率是影响算法性能的主要因素,包括地物杂波、因子场定常外推假设、不合适的Z-R关系、局地性强降水以及多种因素的共同作用。