Sources of bias in weighted average inferences of environmental conditions

Weighted averaging is widely used for inferring environmental conditions from an observed species assemblage. However, weighted average inferences are known to be systematically biased, and linear corrections (i.e., deshrinking functions) are commonly applied to adjust for this bias. In this analysis, the magnitude of the biases in weighted average inferences (and therefore the values of the deshrinking coefficients) are shown to depend upon the range of conditions sampled in the calibration data set and the true optima and niche breadths of the species observed in the calibration data set. Since the range of conditions and the observed species can differ between the calibration data set and the new data set for which environmental conditions are inferred, the coefficients for the deshrinking function derived using the calibration data may not be applicable to inferences computed using a new data set. Thus, environmental inferences may still exhibit systematic errors even after application of the linear correction. The findings from the theoretical analysis are demonstrated using stream temperature and macroinvertebrate data collected from wadeable streams in the western United States.     

基于GIS的贝叶斯统计推理技术在印度野牛生境概率评价中的应用

首先给出ＣＯ２倍增下遥感－光合作物产量的概念模型,之后分析未受ＣＯ２倍增的遥感－光合作物产量估测模型;在考虑ＣＯ２倍增对作物产量的影响后,对影响干物质累积的作物光合速率的模型进行修正,进而修正遥感－光合作物产量估测模型。建立ＣＯ２倍增下作物产量影响模型,求取各参数,并在ＣＯ２倍增下对我国华北地区冬小麦产量影响进行填图,表明模型的估测结果有良好的可比性。     

基于GIS的贝叶斯统计推理技术在印度野牛生境概率评价中的应用

目前,ＧＩＳ技术已被广泛应用在野生动物生境研究中。但是,作为空间数据分析和处理工具,ＧＩＳ缺乏进行启发式推理的能力。因此,与擅长于此的贝叶斯统计推理技术相结合则是解决这一问题的重要途径。以西双版纳纳板河流域生物圈保护区为试验区,综合应用ＧＩＳ技术和多元统计技术建立印度野牛生境的两个逻辑斯蒂多元回归模型：趋势表面模型和环境模型,第一个模型的自变量是位置坐标,第二个模型的自变量是一组环境因子,然后应用贝叶斯统计合并这两个模型产生贝叶斯综合模型。结果表明,贝叶斯综合模型优于环境模型,可应用于野生动物生境概率评价。     

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.     

中上扬子地区震旦纪末期古海洋环境研究的若干问题

本文通过调研大量文献,系统梳理了前人关于中上扬子地区震旦纪灯影期古海洋环境特征的研究方法和主要认识,特别是关于古海水的氧化还原条件、古温度和古盐度等3个要素。结果表明,震旦纪灯影组沉积时期,中上扬子地区的古海洋是一个水深较浅、并逐渐趋于氧化的浅海水体环境;古海水温度为7.0 ℃～38.6 ℃,且在灯影组晚期升高,主要为亚热带气候;海水盐度较新元古代早期有降低的趋势,但仍为海相环境,且灯影组二段和灯影组四段时期盐度相对更大。震旦纪灯影组沉积时期的古海洋环境在地质演化历史中具有重要的时空意义,能够为解释当时地球的气候条件和生物演化等方面的重大变化提供依据。现阶段研究中的问题包括：1）缺乏同时期古海洋氧化还原环境的区域对比;2）古海水绝对深度的研究十分薄弱;3）缺乏古海水盐度的直接指示依据。建议开展高分辨率古环境指示参数垂向序列和多剖面横向对比的研究,尝试定量分析氧气浓度随古海水绝对深度的变化规律,探索指示古海水盐度的直接依据。     

突发性水污染事件溯源方法

为快速准确地求解突发性水污染溯源问题,在微分进化与蒙特卡罗基础上提出了一种新的溯源方法。该方法将溯源问题视为贝叶斯估计问题,推导出污染源强度、位置和排放时刻等未知参数的后验概率密度函数;结合微分进化和蒙特卡罗模拟方法对后验概率分布进行采样,进而估计出这些未知参数,确定污染源项。通过算例与贝叶斯-蒙特卡罗方法进行对比,结果表明:该方法可使迭代次数有效缩减3/4,污染源强度、位置和排放时刻的平均相对误差分别减少1.23%、2.23%和4.15%,均值误差分别降低0.39%、0.83%和1.49%,其稳定性和可靠性明显高于贝叶斯-蒙特卡罗方法,能较好地识别突发性水污染源,为解决突发水污染事件中的追踪溯源难点问题提供了新的思路和方法。     

山东东营凹陷幕式成藏的流体包裹体证据

利用流体包裹体综合分析技术,研究东营凹陷不同构造区带油气成藏方式。结果证明,不同构造区带成藏期古压力及成藏方式具差异性:北部陡坡带沙四段烃源岩上储层油气成藏主要分3期,成藏方式以间歇的幕式充注为主;中央背斜主要分2期,成藏方式以突发式的幕式充注为主;南部缓坡带仅1期,早期成藏方式以持续性充注为主。在凹陷南部缓坡带孔店组的储层中发现沸腾包裹体,分析认为成藏晚期发生过幕式充注,推测在沙四烃源岩下存在新的成藏体系。东营凹陷不同区带成藏模式的确定,可为在高勘探程度地区寻找隐蔽油气藏提供理论依据。     

Multiple linear regression,multi-layer perceptron network and adaptive neuro-fuzzy inference system for forecasting precipitation based on large-scale climate signals

ABSTRACT

Nowadays, mathematical models are widely used to predict climate processes, but little has been done to compare the models. In this study, multiple linear regression (MLR), multi-layer perceptron (MLP) network and adaptive neuro-fuzzy inference system (ANFIS) models were compared for precipitation forecasting. The large-scale climate signals were considered as inputs to the applied models. After selecting the most effective climate indices, the effects of large-scale climate signals on the seasonal standardized precipitation index (SPI) of the Maharlu-Bakhtaran catchment, Iran, simultaneously and with a delay, was analysed using a cross-correlation function. Hence, the SPI time series was forecasted up to four time intervals using MLR, MLP and ANFIS. The results showed that most of the indices were significant with SPI of different lag times. Comparison of the SPI forecast results by MLR, MLP and ANFIS models showed better performance for the MLP network than the other two models (RMSE = 0.86, MAE = 0.74 for the first step ahead of SPI forecasting).

Editor D. Koutsoyiannis; Associate editor F. Pappenberger     

物化探在新疆西昆仑铅锌矿预查中的应用

以已知矿体上物化探异常特征为依据,对区内其他物化探异常进行了分类与解释推断,圈定了四条矿化体.