Application of Bayesian regularized BP neural network model for analysis of aquatic ecological data-A case study of chlorophyll-a prediction in Nanzui water area of Dongting Lake

Bayesian regularized BP neural network（BRBPNN） technique was applied in the chlorophyll-α prediction of Nanzui water area in Dongting Lake. Through BP network interpolation method, the input and output samples of the network were obtained. After the selection of input variables using stepwise/multiple linear regression method in SPSS i1.0 software, the BRBPNN model was established between chlorophyll-α and environmental parameters, biological parameters. The achieved optimal network structure was 3-11-1 with the correlation coefficients and the mean square errors for the training set and the test set as 0.999 and 0.000？8426, 0.981 and 0.0216 respectively. The sum of square weights between each input neuron and the hidden layer of optimal BRBPNN models of different structures indicated that the effect of individual input parameter on chlorophyll- α declined in the order of alga amount 〉 secchi disc depth（SD） 〉 electrical conductivity （EC）. Additionally, it also demonstrated that the contributions of these three factors were the maximal for the change of chlorophyll-α concentration, total phosphorus（TP） and total nitrogen（TN） were the minimal. All the results showed that BRBPNN model was capable of automated regularization parameter selection and thus it may ensure the excellent generation ability and robustness. Thus, this study laid the foundation for the application of BRBPNN model in the analysis of aquatic ecological data（chlorophyll-α prediction） and the explanation about the effective eutrophication treatment measures for Nanzui water area in Dongting Lake.     

Recent developments of surface complexation models applied to environmental aquatic chemistry

1　ＢａｃｋｇｒｏｕｎｄＩｎｇｅｏｃｈｅｍｉｃａｌｃｉｒｃｕｌａｔｉｏｎ ,ｖａｒｉｏｕｓｐｏｌｌｕｔａｎｔｓｗｉｔｈａｄｖｅｒｓｅｉｍｐａｃｔｓ (ｅ .ｇ .,ｔｏｘｉｃｉｔｙ ,ｍｕｔａｇｅｎｉｃｉｔｙａｎｄｌｅｔｈａｌｉｔｙ)ａｒｅｕｓｕａｌｌｙａｓｓｏｃｉａｔｅｄｗｉｔｈｈｅｔｅｒｏｇｅｎｅｏｕｓｐａｒｔｉｃｌｅｓ,ｐｒｅｄｏｍｉｎａｎｔｌｙｃｌａｙｍｉｎｅｒａｌｓ .Ｇｅｎｅｒａｌｌｙ ,ｔｈｅｐｒｏｐｅｒｔｉｅｓｏｆｔｈｅｓｅｃｏｍｐｌｅｘｐａｒｔｉｃｌｅｓｓｈｏｕｌｄｂｅｄｅｓｃｒｉｂｅｄｕｓｉｎ…     

活性污泥数学模型中进水易降解有机物的测定

ASM系列模型 (ASMs)自推出以来在欧美得到广泛应用。要想利用ASMs对特定污水处理过程进行准确的模拟 ,首先必须得到该过程污水的水质特性参数。介绍了连续式OUR计量法、序批式OUR计量法和絮凝过滤物理化学法等 3种测定ASMs中进水易降解有机物的方法 ,通过分析比较指出了各自的优缺点及适用情况。     

农药杀虫单的稻田流失规律研究

通过测坑、人工降雨等手段，对水溶性农药杀虫单的稻田消解动态、渗漏流失和径流流失等进行了研究。结果表明，(1)无水稻时，杀虫单在稻田水中消解的半衰期为0．76d；分蘖初期，杀虫单在稻田水中降解的半衰期为1．02d；平均为0．89d。(2)杀虫单的渗漏流失量在用药后8d内可达总用药量的7％—10％，平均为8．48％。(3)极端情况下，用药当天如果遇暴雨(50mm雨量)，杀虫单的径流流失量将达到用药量的30％左右。杀虫单的渗漏损失不可避免，故其流失控制应从径流损失着手，尽量避免在可能有暴雨的当天或雨前1—2d用药。     

SIMULATION OF TEMPORAL CHANGES IN RAINFALL-RUNOFF CHARACTERISTICS,COON CREEK BASIN,WISCONSIN1

ABSTRACT: Streamflow for 67 years was simulated for Coon Creek at Coon Valley, Wisconsin, for three conditions in the drainage basin: (1) conditions in the 1930s; (2) conditions in the 1970s, excluding flood-detention reservoirs; and (3) conditions in the 1970s, including flood-detention reservoirs. These simulations showed that the changes in agricultural practices over 40 years (1940–80) reduced the 100-year flood by 53 percent (from 38,900 to 18,300 cubic feet per second). The flood-detention reservoirs reduced the 100-year flood by an additional 17 percent (to 15,100 cubic feet per second). The simulation was accomplished by calibrating a precipitation-runoff model to observed rainfall and runoff during two separate periods (1934–40 and 1978–81). Comparisons of model simulations showed that differences between the model calibrations for the two periods were statistically significant at the 95 percent confidence level.     

A COMPARATIVE STUDY OF A SWEDISH AND A CHINESE HYDROLOGICAL MODEL1

ABSTRACT: There are a large number of conceptual hydrological models available today. It is not easy to immediately identify the similarities and differences between the different models. The Swedish HBV model and the Chinese Xinanjiang model are two examples of conceptual, semi-distributed, rainfall-runoff models. The Xinanjiang model was designed for use in humid and semi-humid regions, with no routine for the snowmelt runoff, whereas the snow routine is an important part of the HBV model in many applications. The model structures of the two models may be described in four routines, compared in this paper. The integral structures of them are similar, but there are some differences, especially in the runoff production routine. The physical significance and physical definitions of some model parameters were analyzed. Both models were tested in two basins. Both models gave similar results, and both models performed well in the application. The similarity of the results obtained by different model structures leads to the following two conclusions. First, more effort should probably be spent on the improvement of input data quality and coverage than on the development of more detailed model structures only. Second, inference about basin behavior and characteristics from the values of calibrated model parameters must be made with great caution.     

抽出式通风煤巷掘进过程中粉尘浓度分布规律的数值模拟

根据气固两相流理论，针对矿井掘进工作面的特点，采用计算流体力学的离散相模型（DPM）对掘进工作面通风过程中粉尘浓度进行数值模拟，总结抽出式通风掘进巷道中粉尘浓度的沿程分布及变化规律。     

TEMPORAL CHANGES IN THE YADKIN RIVER FLOW VERSUS SUSPENDED SEDIMENT CONCENTRATION RELATIONSHIP1

ABSTRACT: Dynamic linear models (DLM) and seasonal trend decomposition (STL) using local regression, or LOESS, were used to analyze the 50‐year time series of suspended sediment concentrations for the Yadkin River, measured at the U.S. Geological Survey station at Yadkin College, North Carolina. A DLM with constant trend, seasonality, and a log₁₀ streamflow regressor provided the best model to predict monthly mean log₁₀ suspended sediment concentrations, based on the forecast log likelihood. Using DLM, there was evidence (odds approximately 69:1) that the log₁₀ streamflow versus log₁₀ suspended sediment concentration relationship has changed, with an approximate 20 percent increase in the log₁₀ streamflow coefficient over the period 1981 to 1996. However, sediment concentrations in the Yadkin River have decreased during the decade of the 1990s, which has been accompanied by a concomitant increase in streamflow variability. Although STL has been shown to be a versatile trend analysis technique, DLM is shown to be more suitable for discovery and inference of structural changes (trends) in the model coefficient describing the relationship between flow and sediment concentration.     

Using long-term data for a reintroduced population to empirically estimate future consequences of inbreeding

Inbreeding depression is an important long-term threat to reintroduced populations. However, the strength of inbreeding depression is difficult to estimate in wild populations because pedigree data are inevitably incomplete and because good data are needed on survival and reproduction. Predicting future population consequences is especially difficult because this also requires projecting future inbreeding levels and their impacts on long-term population dynamics, which are subject to many uncertainties. We illustrate how such projections can be derived through Bayesian state-space modeling methods based on a 26-year data set for North Island Robins (Petroica longipes) reintroduced to Tiritiri Matangi Island in 1992. We used pedigree data to model increases in the average inbreeding level (F ) over time based on kinship of possible breeding pairs and to estimate empirically N_e/N (effective/census population size). We used multiple imputation to model the unknown components of inbreeding coefficients, which allowed us to estimate effects of inbreeding on survival for all 1458 birds in the data set while modeling density dependence and environmental stochasticity. This modeling indicated that inbreeding reduced juvenile survival (1.83 lethal equivalents [SE 0.81]) and may have reduced subsequent adult survival (0.44 lethal equivalents [0.81]) but had no apparent effect on numbers of fledglings produced. Average inbreeding level increased to 0.10 (SE 0.001) as the population grew from 33 (0.3) to 160 (6) individuals over the 25 years, giving a ratio of 0.56 (0.01). Based on a model that also incorporated habitat regeneration, the population was projected to reach a maximum of 331–1144 birds (median 726) in 2130, then to begin a slow decline. Without inbreeding, the population would be expected stabilize at 887–1465 birds (median 1131). Such analysis, therefore, makes it possible to empirically derive the information needed for rational decisions about inbreeding management while accounting for multiple sources of uncertainty.