青岛海雾的自适应神经模糊推理系统建模

基于自适应神经模糊推理系统(ANFIS),通过对历史观测资料的训练和规则提取,建立了青岛海雾(能见度)与该站及其上游测站的风向、风速、湿度等要素的逻辑映射关系和模糊推理模型,进而能够较为客观、定量地描述和诊断青岛海雾的发生发展,对改进和提高青岛海雾预报有一定的参考应用意义。     

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.     

Statistical estimation of the relative efficiency of natural attenuation mechanisms in contaminated aquifers

Using monitored natural attenuation is an increasingly popular strategy for dealing with contaminated aquifers. This paper provides a statistical methodology for the estimation of the relative efficiency of natural attenuation mechanisms. The methodology provides estimates, with associated measures of uncertainty, of the relative efficiency of four types of bio-degradation (oxidation using oxygen as the electron-acceptor, denitrification, iron reduction and sulfate reduction). A data set from Trecate, Italy, is analysed using the methodology. The analysis shows that sulfate is the main cause of hydrocarbon removal on this site. It is also seen that oxidation using oxygen seems to be more preferential than the other reactions, in the sense that this reaction is relatively more efficient than other reactions at locations where the hydrocarbon concentration is low.     

地下水位预测的模糊推理

本文采用模糊逻辑推理方法对地下水位进行预测,收到较好的效果,该方法与其它方法相比提高了预测精度,简化了计算方法。     

由我国历史飞蝗北界记录得到的古气候推断

本文利用我国古代有关飞蝗的文献记录,整理出近1000年来飞蝗记录地域北界变动资料,根据飞蝗的生态习性,推断出飞蝗发生在我国北纬41°以北地区的年份的气温条件指出1162～1177年、1265～1280年和1763～1773年是我国东北地区气候温暖的时段,这分别为南宋气候、中世纪温暖期和小冰期的回暖期提出新的佐证。     

Systematic increase in model complexity helps to identify dominant streamflow mechanisms in two small forested basins

This study shows how the use of increasing model complexity allows us to hypothesize about dominant streamflow mechanisms in two small Brazilian forested basins. Nine different structures from SUPERFLEX, an objective framework to systematically increase hydrological model complexity, were tested and we extended the flexible modelling methodology to error models as well. We show that applying a rigorous methodology in a model evaluation framework, with residual analysis and control of model complexity, is essential for testing a model as a hypothesis for dominant hydrological controls. Our results indicate that the model architecture was more important than the increase in the number of model parameters. Better performing models were those with a parallel structure, which confirms our a priori belief about the dominant runoff mechanisms of the studied catchments, characterized by a rapid response to rainfall, but also a constant river discharge fed by water storage on the thick soil layer.     

Sediment source apportionment in Laurel Hill Creek,PA, using Bayesian chemical mass balance and isotope fingerprinting

A Bayesian chemical mass balance (CMB) approach was used to assess the contribution of potential sources for fluvial samples from Laurel Hill Creek in southwest Pennsylvania. The Bayesian approach provides joint probability density functions of the sources' contributions considering the uncertainties due to source and fluvial sample heterogeneity and measurement error. Both elemental profiles of sources and fluvial samples and ¹³C and ¹⁵N isotopes were used for source apportionment. The sources considered include stream bank erosion, forest, roads and agriculture (pasture and cropland). Agriculture was found to have the largest contribution, followed by stream bank erosion. Also, road erosion was found to have a significant contribution in three of the samples collected during lower‐intensity rain events. The source apportionment was performed with and without isotopes. The results were largely consistent; however, the use of isotopes was found to slightly increase the uncertainty in most of the cases. The correlation analysis between the contributions of sources shows strong correlations between stream bank and agriculture, whereas roads and forest seem to be less correlated to other sources. Thus, the method was better able to estimate road and forest contributions independently. The hypothesis that the contributions of sources are not seasonally changing was tested by assuming that all ten fluvial samples had the same source contributions. This hypothesis was rejected, demonstrating a significant seasonal variation in the sources of sediments in the stream. Copyright © 2014 John Wiley & Sons, Ltd.     

Seismic safety of high concrete dams

Peak ground acceleration(PGA) estimation is an important task in earthquake engineering practice.One of the most well-known models is the Boore-Joyner-Fumal formula,which estimates the PGA using the moment magnitude,the site-to-fault distance and the site foundation properties.In the present study,the complexity for this formula and the homogeneity assumption for the prediction-error variance are investigated and an effi ciency-robustness balanced formula is proposed.For this purpose,a reduced-order Monte Carlo simulation algorithm for Bayesian model class selection is presented to obtain the most suitable predictive formula and prediction-error model for the seismic attenuation relationship.In this approach,each model class(a predictive formula with a prediction-error model) is evaluated according to its plausibility given the data.The one with the highest plausibility is robust since it possesses the optimal balance between the data fi tting capability and the sensitivity to noise.A database of strong ground motion records in the Tangshan region of China is obtained from the China Earthquake Data Center for the analysis.The optimal predictive formula is proposed based on this database.It is shown that the proposed formula with heterogeneous prediction-error variance is much simpler than the attenuation model suggested by Boore,Joyner and Fumal(1993).