Evaluation of total load sediment transport formulas using ANN

The calculated results from various sediment transport formulas often differ from each other and from measured data. Some parameters in the sediment transport formulas are more effective than others to estimate total sediment load. In this study, an Artificial Neural Network （ANN） model is trained using four dominant parameters of sediment transport formulas. ANN models are able to reveal hidden laws of natural phenomena such as sediment transport process. The results of ANN and some total bed material load sediment transport formulas have been compared to indicate the importance of variables which can be used in developing sediment transport formulas. To train ANN, average flow velocity, water surface slopes, average flow depth, and median particle diameter are used as dominant parameters to estimate total bed material load. Two hundreds and fifty samples are used to train the ANN model. Twenty-four sets of field data not used in the training nor calibration of ANN are used to compare or verify the accuracy of ANN and some well-known total bed material load formulas. The test results show that the ANN model developed in this study using minimum number of dominant factors is a reliable and uncomplicated method to predict total sediment transport rate or total bed material load transport rate. Results show that the accuracy of formulas in descending order are those by Yang （1973）, Laursen （1958）, Engelund and Hansen （1972）, Ackers and White （1973）, and Toffaleti （1969）. These results are similar to those made by ASCE （1982） based on laboratory and field data not used in this paper. Study results also show that the formulas based on physical laws of sediment transport, like those formulas that were developed based on power concept, are more accurate than other formulas for estimating total bed material sediment load in rivers.     

平台地形对地震地面运动特征周期值的影响

本文人工合成了36条代表不同频谱特性的地震动,构造了简单的平台地形,并利用人工合成地震动作为平台地形计算输入地震波,获得了地表观测点的时程和反应谱.在此基础上,分析了具有不同高度、侧向坡降和介质阻尼等的平台地形对地震动特征周期值的影响.研究的结果表明:当平台高度与入射地震波优势波长相比较小时,平台的高度、侧向坡降、阻尼比等对地震动特征周期值的影响不大,此时单个平台地形地表地震动的特征周期主要依赖于入射地震动的特征周期,而且一般比入射地震动的特征周期略有增大;当平台高度与入射地震波优势波长相比较大时,平台高度对地表地震动特征周期影响较大.     

Estimation of river flow by artificial neural networks and identification of input vectors susceptible to producing unreliable flow estimates

Reliable river flow estimates are crucial for appropriate water resources planning and management. River flow forecasting can be conducted by conceptual or physical models, or data-driven black box models. Development of physically-based models requires an understanding of all the physical processes which impact a natural process and the interactions among them. Since identification of the relationships among these physical processes is very difficult, data-driven approaches have recently been utilized in hydrological modeling. Artificial neural networks are one of the widely used data-driven approaches for modeling hydrological processes. In this study, estimation of future monthly river flows for Guvenc River, Ankara is conducted using various artificial neural network models. Success of artificial neural network models relies on the availability of adequate data sets. A direct mapping from inputs to outputs without consideration of the complex relationships among the dependent and independent variables of the hydrological process is identified. In this study, past precipitation, river flow data, and the associated month are used to predict future river flows for Guvenc River. Impacts of various input patterns, number of training cycles, and initial values assigned to the weights of the connections are investigated. One of the major weaknesses of artificial neural networks is that they may fail to generate good estimates for extreme events, i.e. events that do not occur at all or often enough in the training data set. It is very important to be able to identify such unlikely events. A fuzzy c-means algorithm is used in this study to cluster the training and validation input vectors into regular and extreme events so that the user will have an idea about the risk of the artificial neural network model to generate unreliable results.     

河南省数字地震台网自动量算的震级与人工量算的震级的偏差分析

对2007年12月-2008年9月河南省数字地震台网资料自动量算的震级与人工量算的震级进行了比较和分析。结果表明：由MSDP软件自动量算的震级与人工量算的震级偏差较小,且自动量算的震级可以满足地震定位精度的要求,在地震速报时可采用自动量算的震级。     

Artificial Neural Network Prediction of Tropospheric Ozone Concentrations in Istanbul,Turkey

Tropospheric (ground‐level) ozone has adverse effects on human health and environment. In this study, next day's maximum 1‐h average ozone concentrations in Istanbul were predicted using multi‐layer perceptron (MLP) type artificial neural networks (ANNs). Nine meteorological parameters and nine air pollutant concentrations were utilized as inputs. The total 578 datasets were divided into three groups: training, cross‐validation, and testing. When all the 18 inputs were used, the best performance was obtained with a network containing one hidden layer with 24 neurons. The transfer function was hyperbolic tangent. The correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), and index of agreement or Willmott's Index (d₂) for the testing data were 0.90, 8.78 µg/m³, 11.15 µg/m³, and 0.95, respectively. Sensitivity analysis has indicated that the persistence information (current day's maximum and average ozone concentrations), NO concentration, average temperature, PM₁₀, maximum temperature, sunshine time, wind direction, and solar radiation were the most important input parameters. The values of R, MAE, RMSE, and d₂ did not change considerably for the MLP model using only these nine inputs. The performances of the MLP models were compared with those of regression models (i.e., multiple linear regression and multiple non‐linear regression). It has been found that there was no significant difference between the ANN and regression modeling techniques for the forecasting of ozone concentrations in Istanbul.     

Prediction of Density Flow Plunging Depth in Dam Reservoirs: An Artificial Neural Network Approach

Experimental findings and observations indicate that plunging flow is related to the formation of bed load deposition in dam reservoirs. The sediment delta begins to form in the plunging region where the inflow river water meets the ambient reservoir water. Correct estimation of dam reservoir flow, plunging point, and plunging depth is crucial for dam reservoir sedimentation and water quality issues. In this study, artificial neural network (ANN), multi‐linear regression (MLR), and two‐dimensional hydrodynamic model approaches are used for modeling the plunging point and depth. A multi layer perceptron (MLP) is used as the ANN structure. A two‐dimensional model is adapted to simulate density plunging flow through a reservoir with a sloping bottom. In the model, nonlinear and unsteady continuity, momentum, energy, and k–ε turbulence equations are formulated in the Cartesian coordinates. Density flow parameters such as velocity, plunging points, and plunging depths are determined from the simulation and model results, and these are compared with previous experimental and model works. The results show that the ANN model forecasts are much closer to the experimental data than the MLR and mathematical model forecasts.     

Distribution and reproductive behavior of penguins on Ardley Island and their environmental impact factors

During the 2006/07 Antarctic summer, the species population, distribution and reproductive behavior of penguins in areas near the Great Wall Station were investigated. Five species of penguin were recorded: gentoo penguin (Pygoscelis papua), adelie penguin (P. adeliae), chinstrap penguin (P. antarctica), King penguin (Aptenodytes patagonicus) and Emperor penguin (A. forsteri). The first three species bred locally, while the other two species were observed occasionally. Ardley Island is one of the most important breeding areas for penguins. After the breeding season of 2006/07, there were a total of about 17 234 penguins and the breeding success rate was 0.40-141. Comparing with historical data, changes in penguin species populations and distribution were analyzed, and their relationships with the environment, climate change and human activity were investigated.     

基于人工鱼群算法和Pearson相关系数的裂缝属性识别

将人工鱼群算法和Pearson相关系数结合,引入到进行裂缝属性识别的横波分裂方法中。结果表明,本文使用的方法能快速准确地识别裂缝属性,相比于模型空间扫描、粒子群算法及遗传算法等识别方法,收敛速度增快了约3倍,稳定性上也有所提高。     

Evaluation of the applicability of pier local scour formulae using laboratory and field data

The reliable estimation of the local scour depth at a bridge pier is essential for proper design and maintenance of bridge piers. Most local scour formulae have been developed based on the results of laboratory experiments. The formulae based on laboratory data do not often produce reasonable predictions for field piers because laboratory investigations are apt to oversimplify or ignore many of the complexities of the flow fields around the bridge piers. Validation of the formulae is necessary in order to ascertain which of the formulae are able to provide reasonable estimates of the local scour depth. In this study, six commonly cited formulae based on laboratory data or field data were selected for validation using 180 laboratory data sets gathered from the literature and 446 field data sets collected from four countries. The six formulae validated in this paper are the Colorado State University (CSU), Neill, Froehlich, Breuser, Laursen, and simplified Chinese formulae. Comparisons between the predicted and measured depths were performed using scour from the laboratory and field data. An artificial neural network technique was also applied in order to compare the tendencies between the field and laboratory data sets.     

Experimental prediction of the noncontact jet Trencher’s excavation depth in clay

Trenching depth calculation of traveling jet is a very important field in both pipeline burying and dredging engineering. The trenching ability of a noncontact control flow jet trencher was predicted by experimental method according to the geometry similarity. In the test, the jet pressure, the shear strength of clay, and the translational speed of nozzle were the same with the design parameters, while the nozzle was a reduced scale model. The trenching depth was found about seven to nine times of the nozzle’s diameter. The width of the trench was about four times of the nozzle’s diameter. Based on the test results the noncontact jet trencher was produced and succeeded in practical usage.