Forecasting flows in Apalachicola River using neural networks

Forecasting river flow is important to water resources management and planning. In this study, an artificial neural network (ANN) model was successfully developed to forecast river flow in Apalachicola River. The model used a feed‐forward, back‐propagation network structure with an optimized conjugated training algorithm. Using long‐term observations of rainfall and river flow during 1939–2000, the ANN model was satisfactorily trained and verified. Model predictions of river flow match well with the observations. The correlation coefficients between forecasting and observation for daily, monthly, quarterly and yearly flow forecasting are 0·98, 0·95, 0·91 and 0·83, respectively. Results of the forecasted flow rates from the ANN model were compared with those from a traditional autoregressive integrated moving average (ARIMA) forecasting model. Results indicate that the ANN model provides better accuracy in forecasting river flow than does the ARIMA model. Copyright © 2004 John Wiley & Sons, Ltd.     

Multiple‐station neural network for modelling tidal currents across Shinnecock inlet,USA

This paper presents the development of a multiple‐station neural network for predicting tidal currents across a coastal inlet. Unlike traditional hydrodynamic models, the neural network model does not need inputs of coastal topography and bathymetry, grids, surface and bottom frictions, and turbulent eddy viscosity. Without solving hydrodynamic equations, the neural network model applies an interconnected neural network to correlate the inputs of boundary forcing of water levels at a remote station to the outputs of tidal currents at multiple stations across a local coastal inlet. Coefficients in the neural network model are trained using a continuous dataset consisting of inputs of water levels at a remote station and outputs of tidal currents at the inlet, and verified using another independent input and output dataset. Once the neural network model has been satisfactorily trained and verified, it can be used to predict tidal currents at a coastal inlet from the inputs of water levels at a remote station. For the case study at Shinnecock Inlet in the southern shore of New York, tidal currents at nine stations across the inlet were predicted by the neural network model using water level data located from a station about 70 km away from the inlet. A continuous dataset in May 2000 was used for the training, and another dataset in July 2000 was used for the verification of the neural network model. Comparing model predictions and observations indicates correlation coefficients range from 0·95 to 0·98, and the root‐mean‐square error ranges from 0·04 to 0·08 m s^?1 at the nine current locations across the inlet. Copyright © 2007 John Wiley & Sons, Ltd.     

基于Gamma Test的非线性降雨径流回归模型研究

Gamma Test是一个与模型无关的数据分析方法,可以解决建立回归模型时面对的模型精度评价、划分率定和验证的数据及模型输入因子选择这三个问题.本文以英国的River Tone流域为例,应用Gamma Test方法分析数据,指导建立双层BP神经网络降雨-径流模型.结果表明,Gamma Test可以指导优化输入因子,精简模型结构,防止过拟合.     

成矿预测理论与方法新进展

成矿预测研究的历史与人类进行矿产勘查的历史紧密联系,随着找矿难度的增大,成矿预测对勘查工作的指导作用越来越重要。现代地质科学的重要特征是冲破学科的界线,立足于整个地质学的高度进行跨学科的联合攻关,方能在理论上和技术方法上有所创新。论述与总结了一些现有成矿预测新方法的特点,包括美国“三步式”固体矿产资源潜力评价方法、成矿系列缺位预测法、基于GIS的综合信息预测法及人工神经网络、多重分形等非线性数学预测方法。这为今后进行成矿预测时在方法选择上提供了一些有益的建议和参考。     

基于ANN的苏锡常地裂缝预测研究

伴随着地面沉降灾害的发生,地裂缝作为一种新的地质灾害出现在苏锡常平原上,已有十多年历史,给地区发展造成严重危害。作者在较详细地阐述区域地质背景基础上,着重分析了地下水位和地面沉降在地裂缝形成中的作用。确定了“起伏的基底外加地下水位和地面沉降作用”这一地裂缝成灾模式。研究认为地裂缝的发生与地下水及地面沉降之间不存在简单的线性关系。而是二者共同作用的结果,同时需要有量的配合。初步确定了水位埋深50m,地面沉降量达500mm这样一个苏锡常地区地裂缝的易发环境。通过文章的研究,使得苏锡常地区地裂缝的产生机制更加清晰。文中一些定性和半定量的分析结论将对该地区地裂缝防治区划产生指导作用。     

Uncertainty analysis of statistical downscaling methods using Canadian Global Climate Model predictors

Three downscaling models, namely the Statistical Down‐Scaling Model (SDSM), the Long Ashton Research Station Weather Generator (LARS‐WG) model and an artificial neural network (ANN) model, have been compared in terms of various uncertainty attributes exhibited in their downscaled results of daily precipitation, daily maximum and minimum temperature. The uncertainty attributes are described by the model errors and the 95% confidence intervals in the estimates of means and variances of downscaled data. The significance of those errors has been examined by suitable statistical tests at the 95% confidence level. The 95% confidence intervals in the estimates of means and variances of downscaled data have been estimated using the bootstrapping method and compared with the observed data. The study has been carried out using 40 years of observed and downscaled daily precipitation data and daily maximum and minimum temperature data, starting from 1961 to 2000. In all the downscaling experiments, the simulated predictors of the Canadian Global Climate Model (CGCM1) have been used. The uncertainty assessment results indicate that, in daily precipitation downscaling, the LARS‐WG model errors are significant at the 95% confidence level only in a very few months, the SDSM errors are significant in some months, and the ANN model errors are significant in almost all months of the year. In downscaling daily maximum and minimum temperature, the performance of all three models is similar in terms of model errors evaluation at the 95% confidence level. But, according to the evaluation of variability and uncertainty in the estimates of means and variances of downscaled precipitation and temperature, the performances of the LARS‐WG model and the SDSM are almost similar, whereas the ANN model performance is found to be poor in that consideration. Further assessment of those models, in terms of skewness and average dry‐spell length comparison between observed and downscaled daily precipitation, indicates that the downscaled daily precipitation skewness and average dry‐spell lengths of the LARS‐WG model and the SDSM are closer to the observed data, whereas the ANN model downscaled precipitation underestimated those statistics in all months. Copyright © 2006 John Wiley & Sons, Ltd.     

利用人工神经网络原理对钻井中钻速的预测

在对各种钻速预测模式进行分析的基础上，提出利用改进后的人工神经网络BP算法理论完成对钻井过程中机械钻速的预测。     

人工神经网络在流域水环境承载力中的应用研究

在分析水环境承载力概念的基础上,建立了基于BP人工神经网络的流域水环境承载力评价模型,并对其进行了实证研究.结果表明,将人工神经网络方法应用于水环境承载力的研究,具有建模方便、操作简单的特点,为流域水环境承载力研究的提供了一种新途径.     

黄河断流严重程度分级与判别方法

根据1972年以来有关黄河断流的观测数据，选择与断流特点有关的特征因子组合，给出了能够反映断流状况的综合指标，并据此结合聚类分析得出了关于黄河断流严重程度的分类结果，指出了相应的分级原则。基于断流级别与河流径流量及其年内径流变差系数之间可能存在的密切关系，建立了反映三者关系的人工神经网络模型，用以判别已知河流来水特性条件下的黄河下游断流严重程度。此方法为定量描述和预测黄河断流的程度奠定了基础，其可行性通过与实际观测结果的比较得到了较为满意的论证。