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.     

Fuzzy factorial analysis of snow avalanches

The mutual influence of 21 factors pertaining to terrain, weather, forest and snowpack have been discussed by 10 experts. The semantic (nil, weak, medium, high) evaluations are translated as membership degrees of fuzzy sets, and averaged between the experts by taking their fuzzy expectation value, yielding a 21 × 21 fuzzy matrix for direct interactions. Fuzzy successive multiplications and additions of the matrix give the indirect interactions. The activity and the passivity of the factors with respect to avalanches is represented by directed weighted graphs, and the average semantic values by a 4 × 4 matrix: the activity (passivity) is weak (strong) for tree damage; medium (nil) for altitude; medium (weak) for ground shape; medium (medium) for vegetation, soil, wind, microclimate, tree type, vertical distribution and mechanics of trees, snowpack distribution and snow gliding; strong (nil) for ground inclination and exposition, weather; strong (medium) for heat, precipitation, horizontal distribution of trees, snowpack constitution and stability.     

栅格图形数据叠置分析方法及其在城镇规划中应用

本文介绍用栅格数据进行图形叠置分析的方法,提出矢量数据转换为栅格数据的链拓扑法、减少栅格数据存储量的编码方法、根据模糊综合评判原理建立多因素综合评价数学模型以及叠置评价结果的类型格网合并的边界跟踪算法,最后给出一个实例说明栅格图形数据叠置分析方法在城镇规划中的应用。     

苏鲁交界及南黄海地区地震波速比的变化特征

本文主要应用多台波速比和单台波速比的方法，计算了苏鲁交界及南黄海（北纬３５°３５′至３６°００′，东经１１７°００′至１２１°００′）范围内，１９８４到１９９１年７月发生在该区域的１６３次ＭＬ≥１．７地震的波速比值，并对波速比结果进行ｔ检验和模糊数学分析识别。分析结果发现，１９８５年１月至１１月出现了不连续的波速比低值异常时段，模糊数学识别的异常从属函数在这段时间也呈现高值。异常恢复后，１９８７年     

模糊数学方法在分析日本东海地区地震前兆中的应用

本文把模糊模式识别的直接方法用于检测日本东海地区地震活动性、体积应变、地下水位、地下水氡含量、地倾斜和潮位中的前兆变化。此方法可消除诸如降雨和气温变化等干扰,使我们能更清晰地识别出地震前兆。此外,还把模糊相似方法和模糊聚类方法应用于识别地震空区及检验地震活动随时间变化的相似性。上述方法在日本其他地区同样有效,即将编入日本气象研究所板内地震预报计划中的计算机系统内。     

多因素模糊综合评价预测油气田远景区的研究

在ＧＩＳ支持下，本文设计了“分层建模，逐层评价，综合预测”的新思路，新方法，研究和建立了物、化探数字异常模型、多因素直接加权模型，多因素模糊综合评价预测模型。这对全面揭示各种异常分布特征与隐伏油气田和地质构造的，对指导生产、辅决策、确定油气田远景提供了很有前景的手段。     

动态聚类-模糊模式识别及其应用

本文以模糊子集论为指导,在迪戴的DCM法的基础上提出了动态聚类-模糊模式识别方法。实际上,它是一种新的模糊聚类的方法。该方法以DCM法的动态聚类部分做为原始数据变换的手段,而将迪戴的强模式概念推广为模糊模式,将强模式识别推广为模糊模式识别。此外,还提出了模糊模式间联系度的概念,从而解决了类间联系、类内典型个体、类间过渡个体、个体对各类的隶属程度以及内部有某种演化趋势的类别的划分等问题。文章还给出了该方法的数学模型和算法概要。最后,通过一个在地质学中的应用实例,说明了该方法的应用效果。     

模糊数学分析方法在水环境评价中的应用

本文应用模糊数学原理，以淮河上游长台关站水质监测资料为依据，选择5个评价参数，用模糊概念进行推理，经过运算得出综合评价结果，与一般评价方法相比更接近客观。