Artificial Neural Networks in Proglacial Discharge Simulation: Application and Efficiency Analysis in Comparison to the Multivariate Regression; A Case Study of Waldemar River (Svalbard)

Artificial neural networks were applied to simulate runoff from the glacierized part of the Waldemar River catchment (Svalbard) based on hydrometeorological data collected in the summer seasons of 2010, 2011 and 2012. Continuous discharge monitoring was performed at about 1 km from the glacier snout, in the place where the river leaves the marginal zone. Averaged daily values of discharge and selected meteorological variables in a number of combinations were used to create several models based on the feed‐forward multilayer perceptron architecture. Due to specific conditions of melt water storing and releasing, two groups of models were established: the first is based on meteorological inputs only, while second includes the preceding day's mean discharge. Analysis of the multilayer perceptron simulation performance was done in comparison to the other black‐box model type, a multivariate regression method based on the following efficiency criteria: coefficient of determination (R²) and its adjusted form (adj. R²), weighted coefficient of determination (wR²), Nash–Sutcliffe coefficient of efficiency, mean absolute error, and error analysis. Moreover, the predictors' importance analysis for both multilayer perceptron and multivariate regression models was done. The performed study showed that the nonlinear estimation realized by the multilayer perceptron gives more accurate results than the multivariate regression approach in both groups of models.     

Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation / Prévision et estimation de la concentration en matières en suspension avec des perceptrons multi-couches et l’algorithme d’apprentissage de Levenberg-Marquardt

Abstract

Abstract The prediction and estimation of suspended sediment concentration are investigated by using multi-layer perceptrons (MLP). The fastest MLP training algorithm, that is the Levenberg-Marquardt algorithm, is used for optimization of the network weights for data from two stations on the Tongue River in Montana, USA. The first part of the study deals with prediction and estimation of upstream and down-stream station sediment data, separately, and the second part focuses on the estimation of downstream suspended sediment data by using data from both stations. In each case, the MLP test results are compared to those of generalized regression neural networks (GRNN), radial basis function (RBF) and multi-linear regression (MLR) for the best-input combinations. Based on the comparisons, it was found that the MLP generally gives better suspended sediment concentration estimates than the other neural network techniques and the conventional statistical method (MLR). However, for the estimation of maximum sediment peak, the RBF was mostly found to be better than the MLP and the other techniques. The results also indicate that the RBF and GRNN may provide better performance than the MLP in the estimation of the total sediment load.     

鄂尔多斯盆地东缘桑峨区煤层气分压合采可行性分析

在煤层层数较多的地区进行煤层气开采时,如果实施分层压裂、合层排采的技术,可以有效的降低煤层气的勘探开发成本,还可以提高单井产气量。研究分析此项技术的使用条件可以有效的提高多煤层合采的成功率。桑峨区内煤层层多,煤层结构较简单,厚度变化较小,是煤层气开发的较有利区块,在对直井产气特征研究的基础上,系统分析了主要煤层储层压力、压力梯度、临界解吸压力、储层渗透率、上下围岩特征等因素对合采的影响,认为研究区三套主力煤层基本符合分压合采的要求,可以考虑采用合层排采的方法来降低煤层气开采成本。     

A comparative study of three neural network forecast combination methods for simulated river flows of different rainfall—runoff models

Abstract

The performances of three artificial neural network (NN) methods for combining simulated river flows, based on three different neural network structures, are compared. These network structures are: the simple neural network (SNN), the radial basis function neural network (RBFNN) and the multi-layer perceptron neural network (MLPNN). Daily data of eight catchments, located in different parts of the world, and having different hydrological and climatic conditions, are used to enable comparisons of the performances of these three methods to be made. In the case of each catchment, each neural network combination method synchronously uses the simulated river flows of four rainfall—runoff models operating in design non-updating mode to produce the combined river flows. Two of these four models are black-box, the other two being conceptual models. The results of the study show that the performances of all three combination methods are, on average, better than that of the best individual rainfall—runoff model utilized in the combination, i.e. that the combination concept works. In terms of the Nash-Sutcliffe model efficiency index, the MLPNN combination method generally performs better than the other two combination methods tested. For most of the catchments, the differences in the efficiency index values of the SNN and the RBFNN combination methods are not significant but, on average, the SNN form performs marginally better than the more complex RBFNN alternative. Based on the results obtained for the three NN combination methods, the use of the multi-layer perceptron neural network (MLPNN) is recommended as the appropriate NN form for use in the context of combining simulated river flows.     

一井多层地下水监测井施工关键技术与设备

一井多层地下水监测井引入了westbay分层监测系统,实现了一井多层地下水监测,推动了地下水监测技术发展。一井多层监测井,在施工材料和施工工艺方面有所创新。     

预应力锚索挡墙在多层顺倾滑坡治理中的应用——以大红山铁矿1~#滑坡为例

边坡病害与坡体的地质条件密切相关,坡体的地质条件不同,决定了边坡具有不同的地质特征,边坡所具有的岩性、构造、水文特征及临空面的组合,定义为高边坡的坡体结构,不同的坡体结构会出现不同的坡体病害,其中,以软硬相间、顺倾层状结构最易产生变形破坏[1]。目前,对边(滑)坡的治理有多种成熟的方法,如抗滑挡墙、抗滑桩、预应力锚索等。预应力锚索因其主动受力的工作原理、施工造价较低、施工工期短、施工安全性好等特点,已在各类建设工程中得到广泛的应用。文章以大红山铁矿1#滑坡治理为例,从滑坡成因分析、治理方案比选、施工工程控制等多个角度对多层顺倾结构滑坡治理,进行了详细分析与介绍。结果表明预应力锚索挡墙对多层顺倾结构类型滑坡有良好的治理效果。     

变水层大网目拖网试验与推广

报道了 2 86 .0 0m× 14 4.4 0m( 99.50m)变水层大网目拖网生产和网模试验情况 ,对网目尺寸、网口周长、配纲系数、网位调整、拖速与捕捞对象等方面进行适渔性分析 ,并分析了该网具的捕捞效果。结果表明 ,变水层大网目拖网具有阻力小、网型轻巧、网口扩张性能良好、网口高、破网率低等优点。试验船与对比船相比 ,产量增长 2 4 .53% ,产值增长 2 8.35% ,日创值增长 2 3.83% ,平均网产增长 19.2 3%。生产对比期间 ,试验船产量 131.5t ,产值 2 7.6 2万元 ,日创值 0 .95万元 ,平均网产 1.55t ,分别比对比船增长 2 4 .53%、2 8.35%、2 3.83%和 19.2 3%。该网型已在茂名市大面积推广 ,推广面达 10 0 %。     

A three-dimensional nonlinear reduced-order predictive joint model

Mechanical joints can have significant effects on the dynamics of assembled structures. However, the lack of efficacious predictive dynamic models for joints hinders accurate prediction of their dynamic behavior. The goal of our work is to develop physics-based, reduced-order, finite element models that are capable of replicating the effects of joints on vibrating structures. The authors recently developed the so-called two-dimensional adjusted Iwan beam element (2-D AIBE) to simulate the hysteretic behavior of bolted joints in 2-D beam structures. In this paper, 2-D AIBE is extended to three-dimensional cases by formulating a three-dimensional adjusted Iwan beam element (3-D AIBE). Impulsive loading experiments are applied to a jointed frame structure and a beam structure containing the same joint. The frame is subjected to excitation out of plane so that the joint is under rotation and single axis bending. By assuming that the rotation in the joint is linear elastic, the parameters of the joint associated with bending in the frame are identified from acceleration responses of the jointed beam structure, using a multi-layer feed-forward neural network (MLFF). Numerical simulation is then performed on the frame structure using the identified parameters. The good agreement between the simulated and experimental impulsive acceleration responses of the frame structure validates the efficacy of the presented 3-D AIBE, and indicates that the model can potentially be applied to more complex structural systems with joint parameters identified from a relatively simple structure.     

Identification of a surface layer structure and analysis of humidity data in two weather situations at Jodhpur (26°18′N, 73°04′E), India,during MONTBLEX 1990

The Monsoon Trough Boundary Layer Experiment held in 1990 was a multi-institutional effort to probe the atmospheric boundary layer over the monsoon trough over northern India. For this experiment, four micrometeorological towers were set up at four different locations along the normal position of the trough. One such tower of 30m height was located at Jodhpur (26‡18′N, 73‡04′E), Rajasthan. The fast and slow response data available during the experiment have been used in the present study to determine a suitable layer-structure of the surface layer for evaluation of sensible heat flux using the multilayer hypothesis of Kramm (1989).