ARIMA和ANN模型的干旱预测适用性研究

开展干旱预测是有效应对干旱风险的前提基础,根据1960-2016年三江平原7个站点逐日降水和气温数据,利用ARIMA和ANN模型对不同时间尺度标准化降水蒸散指数（SPEI）序列进行分析建模预测。借助相关系数R、纳什效率系数NSE、Kendall秩相关系数τ、均方误差MSE和Kolmogorov-Smirnov （K-S）检验对模型的有效性进行了判定,然后分别用ARIMA和ANN模型进行12步预测,并将预测值与实际值进行比较。结果表明：（1） ARIMA模型和ANN模型对SPEI的预测能力都随时间尺度的增加而逐渐提高。（2）两种模型对3、6个月尺度SPEI的预测精度偏低,9、12、24个月的SPEI的预测精度在70%以上;（3）SPEI-9、SPEI-12、SPEI-24三个时间尺度ANN模型的预测精度优于ARIMA模型。     

Predicting longitudinal dispersion coefficient in natural streams by artificial intelligence methods

In this study, three artificial neural network methods, i.e. feed forward back propagation, the radial basis function neural network, and the generalized regression neural network are employed to compute the longitudinal dispersion coefficient in order to evaluate its behaviour in predicting dispersion characteristics in natural streams. These methods, which use hydraulic and geometrical data to predict dispersion coefficients, can easily be applied to natural streams and are proven to be superior in explaining their dispersion characteristics more precisely than existing equations. This method of predicting the longitudinal dispersion coefficient in river flows was tested on 65 data sets, obtained by researchers from 30 rivers in the USA. Results using the models are compared with results obtained in many other studies, and are shown to be more accurate than the other methods considered. Copyright © 2008 John Wiley & Sons, Ltd.     

基于人工神经网络的地下水状态方程—以山东莱州湾南岸地下水研究为例

山东莱州湾南岸地下水密度受多种因素影响,传统方法难以拟合,利用人工神经网络（简称ANN）高效的自组织学习能力和抗实验噪音能力及应用神经网络工具箱设计了三层反馈式神经网络模型,得出映射莱州湾南岸地下水密度与含盐量之间相关关系的基于ANN的地下水状态性关系的性质,基于ANN的地下水状态方程具有形式简单,易于使用等特点。     

Objective forecasting of frost using locally‐observed meteorological parameters

Abstract

An objective scheme for forecasting frost is derived using scatter diagrams of meteorological parameters observed at 20 h local time at a station in the western Jezreel Valley of Zsrael. Verifîcation based on independent data was highly consistent with probabilities of frost versus no‐frost predicted by the suggested procedure.     

http://www.sciencedirect.com/science/article/pii/S1674987112000254

The applications of intelligent techniques have increased exponentially in recent days to study most of the non-linear parameters.In particular,the behavior of earth resembles the non-linearity applications.An efficient tool is needed for the interpretation of geophysical parameters to study the subsurface of the earth.Artificial Neural Networks(ANN) perform certain tasks if the structure of the network is modified accordingly for the purpose it has been used.The three most robust networks were taken and comparatively analyzed for their performance to choose the appropriate network.The single-layer feed-forward neural network with the back propagation algorithm is chosen as one of the well-suited networks after comparing the results.Initially,certain synthetic data sets of all three-layer curves have been taken for training the network,and the network is validated by the Held datasets collected from Tuticorin Coastal Region(78°7′30″E and 8°48′45″N),Tamil Nadu.India.The interpretation has been done successfully using the corresponding learning algorithm in the present study.With proper training of back propagation networks,it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data concerning the synthetic data trained earlier in the appropriate network.The network is trained with more Vertical Electrical Sounding(VES) data,and this trained network is demonstrated by the field data.Groundwater table depth also has been modeled.     

基于GIS与ANN模型的地震滑坡易发性区划

基于遥感数据、地理信息系统(GIS)技术和人工神经网络(ANN)模型,开展地震滑坡易发性区划研究.2010年4月14日玉树地震后,基于航片与卫星影像目视解译,并辅以野外调查的方法,在地震区圈定了2036处地震诱发滑坡.选择高程、坡度、坡向、斜坡曲率、坡位、与水系距离、地层岩性、与断裂距离、与公路距离、归一化植被指数(NDVI)、与同震地表破裂距离、地震动峰值加速度(PGA)共12个因子作为地震滑坡易发性评价因子.这些因子均是应用GIS技术与遥感影像处理技术,基于地形数据、地质数据、遥感数据得到.训练样本中的滑动样本有两组,一组是滑坡区整个单滑坡体的质心位置,另一组是滑坡滑源区滑前的坡体高程最高的位置.应用这12个影响因子,分别采用这两组评价样本,基于ANN模型建立地震滑坡易发性索引图,基于GIS工具建立地震滑坡易发性分级图.分别应用训练样本中滑坡分布的点数据去检验各自的结果正确率,正确率分别为81.53％与81.29％,表明ANN模型是一种高效科学的地震滑坡易发性区划模型.     

Neural network modelling for mean velocity and turbulence intensities of steep channel flows

The main purpose of this study is to evaluate the potential of simulating the profiles of the mean velocity and turbulence intensities for the steep open channel flows over a smooth boundary using artificial neural networks. In a laboratory flume, turbulent flow conditions were measured using a fibre‐optic laser doppler velocimeter (FLDV). One thousand and sixty‐four data sets were collected for different slopes and aspect ratios at different locations. These data sets were randomly split into two subsets, i.e. training and validation sets. The multi‐layer functional link network (MFLN) was used to construct the simulation model based on the training data. The constructed MFLN models can almost perfectly simulate the velocity profile and turbulence intensity. The values of correlation coefficient (γ) are close to one and the values of root mean square error (RMSE) are close to zero in all conditions. The results demonstrate that the MFLN can precisely simulate the velocity profiles, while the log law and Reynolds stress model (RSM) are less effective when used to simulate the velocity profiles close to the side wall. The simulated longitudinal turbulence intensities yielded by the MFLN were also fairly consistent with the measured data, while the simulated vertical turbulence intensities by the RSM were not consistent with the measured data. Copyright © 2007 John Wiley & Sons, Ltd.     

BP神经网络在建模中的参数优化问题研究

神经网络方法已经在过去很多年中得到了大量研究,特别是基于误差反向传播算法的人工神经网络(简称BP-ANN)在很多天气预报业务上发挥了重要作用.对于BP-ANN训练有这样的一个问题,在同一个样本模型、同样的网络结构和同样的输入参数情况下,每次训练得到的权重和最终的误差结果几乎都是不一样的,有的会很好,有的会较差.在利用BP-ANN建模训练中,希望都寻找到因子模型的局部最优解,使它具有较好的泛化能力.为了提高BP-ANN在业务预报中的建模和预测能力,将对BP-ANN进行改进试验.利用2009-2010年每年5月15日至9月15日的T639模式预测数据和北京地区4个站点的最高温度实况资料作为建模样本数据,对4个站点进行数值模拟试验.通过对4个模型样本的拟合建模试验发现:BP-ANN的随机初始权重场服从高斯分布,或者初始权重场进行多次初始化,或者采用动态的隐层神经元网络结构都能让BP-ANN对样本的拟合命中率有一定的提高.最后选择2011年5月15日至9月15日115天的资料作为预报测试数据,集成3种改进方法于一个BP-ANN中,和未改进前的BP-ANN进行比较,对比后发现优化后的BP-ANN训练出的模型预测得到的验证样本预测命中率要高于未优化的BP-ANN训练出的模型得到的验证样本预测命中率,优化后的BP-ANN具有更好的泛化能力.     

Using statistical and artificial neural network models to forecast potentiometric levels at a deep well in South Texas

Reliable forecasts of monthly and quarterly fluctuations in groundwater levels are necessary for short- and medium-term planning and management of aquifers to ensure proper service of seasonal demands within a region. Development of physically based transient mathematical models at this time scale poses considerable challenges due to lack of suitable data and other uncertainties. Artificial neural networks (ANN) possess flexible mathematical structures and are capable of mapping highly nonlinear relationships. Feed-forward neural network models were constructed and trained using the back-percolation algorithm to forecast monthly and quarterly time-series water levels at a well that taps into the deeper Evangeline formation of the Gulf Coast aquifer in Victoria, TX. Unlike unconfined formations, no causal relationships exist between water levels and hydro-meteorological variables measured near the vicinity of the well. As such, an endogenous forecasting model using dummy variables to capture short-term seasonal fluctuations and longer-term (decadal) trends was constructed. The root mean square error, mean absolute deviation and correlation coefficient (R) were noted to be 1.40, 0.33 and 0.77 m, respectively, for an evaluation dataset of quarterly measurements and 1.17, 0.46, and 0.88 m for an evaluative monthly dataset not used to train or test the model. These statistics were better for the ANN model than those developed using statistical regression techniques.     

Rainfall‐runoff modelling using artificial neural networks: comparison of network types

Growing interest in the use of artificial neural networks (ANNs) in rainfall‐runoff modelling has suggested certain issues that are still not addressed properly. One such concern is the use of network type, as theoretical studies on a multi‐layer perceptron (MLP) with a sigmoid transfer function enlightens certain limitations for its use. Alternatively, there is a strong belief in the general ANN user community that a radial basis function (RBF) network performs better than an MLP, as the former bases its nonlinearities on the training data set. This argument is not yet substantiated by applications in hydrology. This paper presents a comprehensive evaluation of the performance of MLP‐ and RBF‐type neural network models developed for rainfall‐runoff modelling of two Indian river basins. The performance of both the MLP and RBF network models were comprehensively evaluated in terms of their generalization properties, predicted hydrograph characteristics, and predictive uncertainty. The results of the study indicate that the choice of the network type certainly has an impact on the model prediction accuracy. The study suggests that both the networks have merits and limitations. For instance, the MLP requires a long trial‐and‐error procedure to fix the optimal number of hidden nodes, whereas for an RBF the structure of the network can be fixed using an appropriate training algorithm. However, a judgment on which is superior is not clearly possible from this study. Copyright © 2004 John Wiley & Sons, Ltd.