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     

Precipitation-runoff simulation for a Himalayan River Basin, India using artificial neural network algorithms

In the Himalayan regions, precipitation-runoff relationships are amongst the most complex hydrological phenomena, due to varying topography and basin characteristics. In this study, different artificial neural networks (ANNs) algorithms were used to simulate daily runoff at three discharge measuring sites in the Himalayan Kosi River Basin, India, using various combinations of precipitation-runoff data as input variables. The data used for this study was collected for the monsoon period (June to October) during the years of 2005 to 2009. ANNs were trained using different training algorithms, learning rates, length of data and number of hidden neurons. A comprehensive multi-criteria validation test for precipitation-runoff modeling has been undertaken to evaluate model performance and test its validity for generating scenarios. Global statistics have demonstrated that the multilayer perceptron with three hidden layers (MLP-3) is the best ANN for basin comparisons with other MLP networks and Radial Basis Functions (RBF). Furthermore, non-parametric tests also illustrate that the MLP-3 network is the best network to reproduce the mean and variance of observed runoff. The performance of ANNs was demonstrated for flows during the monsoon season, having different soil moisture conditions during period from June to October.     

A hybrid neural network model for typhoon-rainfall forecasting

A hybrid neural network model is proposed in this paper to forecast the typhoon rainfall. Two different types of artificial neural networks, the self-organizing map (SOM) and the multilayer perceptron network (MLPN), are combined to develop the proposed model. In the proposed model, a data analysis technique is developed based on the SOM, which can perform cluster analysis and discrimination analysis in one step. The MLPN is used as the nonlinear regression technique to construct the relationship between the input and output data. First, the input data are analyzed using a SOM-based data analysis technique. Through the SOM-based data analysis technique, input data with different properties are first divided into distinct clusters, which can help the multivariate nonlinear regression of each cluster. Additionally, the topological relationships among data are discovered from which more insight into the typhoon-rainfall process can be revealed. Then, for each cluster, the individual relationship between the input and output data is constructed by a specific MLPN. For evaluating the forecasting performance of the proposed model, an application is conducted. The proposed model is applied to the Tanshui River Basin to forecast the typhoon rainfall. The results show that the proposed model can forecast more precisely than the model developed by the conventional neural network approach.     

Cumulative gain and lift charts for model performance assessment in mineral potential mapping

Model performance assessment is a key procedure for mineral potential mapping, but the corresponding research achievements are seldom reported in literature.Cumulative gain and lift charts are well known in the data mining community specialized in marketing and sales applications and widely used in customer churn prediction for model performance assessment.In this paper, they are introduced into the field of mineral potential mapping for model performance assessment.These two charts can be viewed as a graphic representation of the advantage of using a predictive model to choose mineral targets.A cumulative gain curve can represent how much a predictive model is superior to a random guess in mineral target prediction.A lift chart can express how much more likely the mineral targets predicted by a model are deposit-bearing ones than those by a random selection.As an illustration, the cumulative gain and lift charts are applied to measure the performance of weights of evidence, logistic regression, restricted Boltzmann machine, and multilayer perceptron in mineral potential mapping in the Altay district in northern Xinjiang in China.The results show that the cumulative gain and lift charts can visually reveal that the first three models perform well while the last one performs poorly.Thus, the cumulative gain and lift charts can serve as a graphic tool for model performance assessment in mineral potential mapping.     

钢管混凝土框架-核心筒混合结构连续倒塌非线性动力分析

为研究钢管混凝土框架-核心筒混合结构在局部构件失效后的连续倒塌机制,基于ABAQUS纤维梁单元和分层壳单元,采用课题组开发的材料本构子程序iFiberLUT,进行了一栋33层钢管混凝土框架-核心筒混合结构在1、17、33层柱和核心筒墙体失效工况下的连续倒塌非线性动力分析,研究了典型柱和剪力墙失效后剩余结构的抗连续倒塌机制。结果表明:33层构件失效时上部节点位移反应最大,17层次之,1层最小,相比核心筒墙体失效,柱失效时上部节点竖向位移更大,震荡更明显;各工况作用对核心筒影响均较小,且核心筒的存在增强了楼板的薄膜效应,提高了结构抗倒塌能力,失效位置距核心筒越近提高越显著;典型构件失效后结构的传力路径遵循"就近原则"向周围构件传递,楼板和核心筒有力的提高了结构的冗余传递路径和整体性。     

中国大陆地壳“镶嵌与叠覆”的结构特征及其演化

初步探讨了中国大陆地壳“块带镶嵌多层叠覆”的结构特征和多阶段的构造演化过程。中国大陆地壳新元古代中期以来的一级构造单元有中朝、塔里木、扬子、敦煌4个陆块和中央、西北、东北、西南、东南5个造山区(带)。中朝陆块的形成源于古元古代期间发生的古大陆裂解;扬子、塔里木和敦煌陆块的形成源于新元古代早期发生的古大陆裂解。西北造山区的形成源于古生代晚期洋盆关闭、大陆碰撞并叠加新生代陆内再造山;东北造山带的形成过程包括古生代碰撞造山及中生代增生、碰撞造山;中央造山带至三叠纪大陆碰撞才最后形成并叠加有新生代再造山;东南造山带的形成经历了古生代至新生代的多次造山作用;西南造山带主要是中—新生代造山作用的产物。这些单元都具有“块带镶嵌多层叠覆”的结构特征和多阶段构造演化的特点。中国大陆地壳的形成与演化可以划分为太古宙—古元古代、中元古代—新元古代早期、新元古代中期—古新世和始新世以来4个构造阶段,每个阶段都对应不同的超大陆裂解-聚合旋回。其中新元古代中期以来的地壳形成演化与全球洋陆格局中的古亚洲洋、古特提斯洋、古太平洋、特提斯洋和太平洋5个动力学体制有关,相应地可以归结为古亚洲、古特提斯、古太平洋、特提斯和太平洋5个造山域。正是这些多阶段的超大     

Three-Dimensional Modelling of a Multi-Layer Sandstone Reservoir: the Sebei Gas Field, China

Multi-layer sandstone reservoirs occur globally and are currently in international production. The 3D characteristics of these reservoirs are too complicated to be accurately delineated by general structural-facies-reservoir modelling. In view of the special geological features, such as the vertical architecture of sandstone and mudstone interbeds, the lateral stable sedimentation and the strong heterogeneity of reservoir poroperm and fluid distribution, we developed a new three-stage and six-phase procedure for 3D characterization of multi-layer sandstone reservoirs. The procedure comprises two-phase structural modelling, two-phase facies modelling and modelling of two types of reservoir properties. Using this procedure, we established models of the formation structure, sand body structure and microfacies, reservoir facies and properties including porosity, permeability and gas saturation and provided a 3D fine-scale, systematic characterization of the Sebei multi-layer sandstone gas field, China. This new procedure, validated by the Sebei gas field, can be applied to characterize similar multi-layer sandstone reservoirs.     

基于知识的分层综合分类法在土地利用/土地覆盖遥感信息提取中的应用

土地利用/土地覆盖数据的获取是研究LUCC的重要基础工作。随着遥感技术的飞速发展,通过遥感提取土地利用/土地覆盖专题信息已成为LUCC研究必不可少的一步。目前遥感专题信息提取水平相对滞后于遥感数据获取,为了提高遥感数据在土地利用/土地覆盖的应用,寻找一种较好的、具有相对适用性的方法是目前遥感应用的一个迫切要求。本文比较了目前比较常用的几种土地利用/土地覆盖遥感信息提取方法,分别以西部干旱区(柴达木盆地)和东部地区(鄱阳湖地区)为例,提出在GIS支持下基于知识的分层综合分类方法,并通过和其他几种常用方法进行比较分析,得到如下结果:在自然环境相差较大的柴达木盆地和鄱阳湖地区,采用了GIS支持下基于知识的分层综合分类方法的提取精度均要比单独采用最大似然法、纹理分析法、神经网络分类法等方法的总体精度高出25%,Kappa系数高出0.2。由此可以说明了该方法对于土地利用/土地覆盖专题信息的提取是可行的,同时它也具有一定的适用性。     

The effect of imposing ‘fractional abundance constraints’ onto the multilayer perceptron for sub-pixel land cover classification

To be physically interpretable, sub-pixel land cover fractions or abundances should fulfill two constraints, the Abundance Non-negativity Constraint (ANC) and the Abundance Sum-to-one Constraint (ASC). This paper focuses on the effect of imposing these constraints onto the MultiLayer Perceptron (MLP) for a multi-class sub-pixel land cover classification of a time series of low resolution MODIS-images covering the northern part of Belgium. Two constraining modes were compared, (i) an in-training approach that uses ‘softmax’ as the transfer function in the MLP’s output layer and (ii) a post-training approach that linearly rescales the outputs of the unconstrained MLP. Our results demonstrate that the pixel-level prediction accuracy is markedly increased by the explicit enforcement, both in-training and post-training, of the ANC and the ASC. For aggregations of pixels (municipalities), the constrained perceptrons perform at least as well as their unconstrained counterparts. Although the difference in performance between the in-training and post-training approach is small, we recommend the former for integrating the fractional abundance constraints into MLPs meant for sub-pixel land cover estimation, regardless of the targeted level of spatial aggregation.     

空气潜孔锤与风冷牙轮组合钻进在穿采空区大直径抽水孔中的应用

在穿多层采空区大直径抽水孔施工中,通过引入空气潜孔锤与风冷牙轮组合钻进工艺,解决了常规泥浆回转钻进钻遇多层采空区因孔内漏失无法成孔、空气潜孔锤钻进成孔孔径受限的问题。采用该工艺完成的2个φ550 mm抽水井工程均穿越4层采空区,φ350 mm潜孔锤冲击钻进平均钻速3.31 m/h,φ550 mm风冷牙轮扩孔钻进平均钻速2.27 m/h,在复杂地层中保持了较高的钻进效率,取得了明显的应用效果。介绍了该工艺在大同矿区φ550 mm大直径抽水井工程中的应用实例,经过实践验证了工艺的可行性,可为类似钻孔的施工提供参考。