Wind speed prediction using statistical regression and neural network

Prediction of wind speed in the atmospheric boundary layer is important for wind energy assessment, satellite launching and aviation, etc. There are a few techniques available for wind speed prediction, which require a minimum number of input parameters. Four different statistical techniques, viz., curve fitting, Auto Regressive Integrated Moving Average Model (ARIMA), extrapolation with periodic function and Artificial Neural Networks (ANN) are employed to predict wind speed. These methods require wind speeds of previous hours as input. It has been found that wind speed can be predicted with a reasonable degree of accuracy using two methods, viz., extrapolation using periodic curve fitting and ANN and the other two methods are not very useful.     

应用一种改进BP神经网络算法预测密度曲线

利用测井数据与地震数据二者相结合进行综合分析,是地震勘探工作者的重要工作。可以通过分析井位处的地震数据与测井数据,提取地震的多个属性,建立一个与测井属性的统计关系。选取已改进的三层网络结构BP神经网络算法,在应用一个实际例子后表明,该算法的主要特点是收敛速度快、计算简单,同时还具有跳出局部最小的能力。应用此神经网络算法对某油田的二维地震数据进行了处理,提取了多种地震属性,并在井位置建立了地震属性与密度曲线的非线性关系,成功预测了剖面密度曲线,为了解储层状况提供了有益的资料。     

Storm surge prediction using an artificial neural network model and cluster analysis

In this study, an artificial neural network model was developed to predict storm surges in all Korean coastal regions, with a particular focus on regional extension. The cluster neural network model (CL-NN) assessed each cluster using a cluster analysis methodology. Agglomerative clustering was used to determine the optimal clustering of 21 stations, based on a centroid-linkage method of hierarchical clustering. Finally, CL-NN was used to predict storm surges in cluster regions. In order to validate model results, sea levels predicted by the CL-NN model were compared with results using conventional harmonic analysis and the artificial neural network model in each region (NN). The values predicted by the NN and CL-NN models were closer to observed data than values predicted using harmonic analysis. Data such as root mean square error and correlation coefficient varied only slightly between CL-NN and NN model results. These findings demonstrate that cluster analysis and the CL-NN model can be used to predict regional storm surges and may be used to develop a forecast system.     

Real-time prediction of shield moving trajectory during tunnelling using GRU deep neural network

Acta Geotechnica - This paper establishes an intelligent framework for real-time prediction of trajectory deviations in the process of earth pressure balance (EPB) tunnelling. A hybrid model was...     

Performance of artificial neural network and convolutional neural network on slope failure prediction using data from the random finite element method

Acta Geotechnica - The random finite element method has been widely used to evaluate slope uncertainty and reliability. To determine the probability of failure, the safety factor sampling often...     

基于BP神经网络的瓦斯含量预测

以淮南矿区潘一矿13-1煤层为研究对象,在分析勘探钻孔资料的基础上,确定了煤层埋深及厚度、顶底板岩性、地质构造和煤变质程度是影响煤层瓦斯含量的主要因素;使用BP神经网络方法建立了瓦斯含量预测模型;结合实际数据,对预测模型进行训练和检验。预测结果表明:该模型比使用多元线性回归预测能获得更高的精度,说明预测模型可靠。     

人工神经网络在海浪数值预报中的应用

探讨将人工神经网络技术和传统的数值模式相结合,以期得到一个更有效的海浪预报方法.以第3代海浪模式的模拟结果作为输入,浮标观测资料作为输出,采用人工神经网络进行训练,训练的初步结果显示,人工神经网络可以改进海浪数值模式的预报精度,但在波高比较大时,改进的效果并不令人满意.为此,对观测值大于1.5m时的有效波高进行再训练,从而结果有了进一步的改善.研究结果证明人工神经网络技术可以提高海浪数值预报的精度.     

Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment

The potential of multiple linear regression (MLR) and artificial neural network (ANN) techniques in predicting transient water levels over a groundwater basin were compared. MLR and ANN modeling was carried out at 17 sites in Japan, considering all significant inputs: rainfall, ambient temperature, river stage, 11 seasonal dummy variables, and influential lags of rainfall, ambient temperature, river stage and groundwater level. Seventeen site-specific ANN models were developed, using multi-layer feed-forward neural networks trained with Levenberg-Marquardt backpropagation algorithms. The performance of the models was evaluated using statistical and graphical indicators. Comparison of the goodness-of-fit statistics of the MLR models with those of the ANN models indicated that there is better agreement between the ANN-predicted groundwater levels and the observed groundwater levels at all the sites, compared to the MLR. This finding was supported by the graphical indicators and the residual analysis. Thus, it is concluded that the ANN technique is superior to the MLR technique in predicting spatio-temporal distribution of groundwater levels in a basin. However, considering the practical advantages of the MLR technique, it is recommended as an alternative and cost-effective groundwater modeling tool.     

Earthquake-induced landslide prediction using back-propagation type artificial neural network: case study in northern Iran

Natural Hazards - Landslides can cause extensive damage, particularly those triggered by earthquakes. The current study used back propagation of an artificial neural network (ANN) to conduct risk...     

An objective prediction model for typhoon rainstorm using particle swarm optimization: neural network ensemble

A nonlinear ensemble prediction model for typhoon rainstorm has been developed based on particle swarm optimization-neural network (PSO-NN). In this model, PSO algorithm is employed for optimizing the network structure and initial weight of the NN with creating multiple ensemble members. The model input of the ensemble member is the high correlated grid point factors selected from the rainfall forecast field of Japan Meteorological Agency numerical prediction products using the stepwise regression method, and the model output is the future 24 h rainfall forecast of the 89 stations. Results show that the objective prediction model is more accurate than the numerical prediction model which is directly interpolated into the stations, so it can better been implemented for the interpretation and application of numerical prediction products, indicating a potentially better operational weather prediction.     

基于BP神经网络的薄互层储层预测

根据地震属性来进行储层预测的研究,对于寻找油气具有十分重要的意义。而由于地下地质情况千变万化,不确定的因素太多,对这种多自变量与因变量的复杂关系模拟问题,神经网络技术是目前较成熟、实际应用效果也较好的方法之一。用BP神经网络来预测薄互层储层厚度,它可以建立属性参数与预测目标之间的高度非线性映射,并应用于具体的实例,为薄互层储层预测提供了新的思路。     

人工神经网络在边坡滑移预测中的应用

利用一项实际边坡工程的多年实测滑移资料，基于人工神经网络方法，建立了非线性人工神经网络分析模型，从而能够较精确地对该边坡的滑移进行预测，通过本工程实际的应用实践，发现它可避开复杂的本构模型而成为解决岩土工程问题的新途径。     

模糊神经网络在矿震预测中的应用

矿震同天然地震一样会给矿山生产及人身安全等带来重大灾难。也是目前尚不能准确预测的矿山灾害现象之一。根据现有的研究成果可知，矿震是一个多输入、多干扰、单输出的复杂系统。由于干扰项的存在，使利用建模、神经网络等手段对系统进行预测时会导致很大误差。模糊神经网络系统在建立对象输入、输出关系时与传统数学方法不同。即可以建立在无模型基础上，并利用其较强的学习训练特性，自动获取对象的输入、输出关系表达；可以将专家的评价语言作为系统的干扰项引入。这在一定程度上缓解了人为因素对预测结果的影响，且平滑了观测数据的随机性。文章利用改进的模糊神经网络及抚顺老虎台矿的矿震资料，对矿震最大震级的预测方法进行了探索。‘初步探讨了改进的模糊神经网络在矿震预测中的应用。得出在运用模糊神经网络进行预测时，为减小预测误差，应综合多种因素并提高专家评判语言的精确度的结论。指出在建立矿震系统预测模型时，利用干扰项将人为因素引入系统是必需的。通过实际应用证明其可行性。     

采矿地表沉陷的神经网络预测

矿山开采引起的地表沉陷受地质条件和采矿条件等诸多因素的影响,这些因素又具有非线性关系,很难用数学模型加以描述,因而在求解问题时遇到困难.利用神经网络系统对求解非线性问题的优点,对地表沉陷问题进行预测,其结果和实测值基本一致,从而证明了它对地表沉陷预测的可行性和实用性.     

基于滑坡监测数据的Elman神经网络动态预测

滑坡在我国是一种极为频发的地质灾害,且其积累位移监测曲线有着复杂的非线性特性,对此各研究者建立过许多预测模型,然而这些模型的预测精度不尽如人意。基于Elman神经网络可以任意精度逼近任意非线性函数的特征,并以sigmoid为方程的核函数,在选择隐含层数时用了试用法,通过\     

基于LSTM神经网络的煤矿突水预测

针对煤层底板突水预测问题,在总结现有突水预测方法和理论的基础上,通过特征选择实验得出水压、距工作面距离、砂岩段厚度、煤层厚度、煤层倾角、断层落差、裂隙带、开采面积、采高、走向长度是影响突水发生的主要因素,这些因素具有复杂、非线性的特点。提出基于长短时记忆（LSTM）神经网络构建的突水预测模型,将煤矿突水实例的数据作为样本数据对模型进行训练。最后,将LSTM神经网络模型与遗传算法-反向传播（GA-BP）神经网络模型和反向传播（BP）神经网络模型进行对比实验。实验结果表明,LSTM神经网络模型在测试集上的预测正确率更高,稳定性更好,更适用于煤层底板突水预测。     

有监督的人工神经网络油气预测

有监督的人工神经网络预测是20世纪90年代发展起来的一种新兴技术，由于它具有算法快、精度高的特点，在油气预测中得到广泛使用。本文介绍了该方法的原理及其与地震属性的关系，应用实际资料进行了计算，证明效果良好，是一种油气预测有利工具。     

BP网络并行预测地下水位研究

提出了适用于孔隙承压水水源地的BP-遗传元胞自动机方法,这种方法的原理来源于地理元胞自动机的方法框架。把每个水源地作为一个元胞,每个水源地的下一时刻的状态由其本身、周围水源地上一时刻的状态和下一时刻的行为及它们所处的环境条件决定。并用该方法进行了多水源地水位并行预测,效果良好。     

Carbon monoxide prediction using novel intelligent network

This paper introduces a new structure in neural networks called TD-CMAC, an extension to the conventional Cerebellar Model Arithmetic Computer (CMAC), having reasonable ability in time series prediction. TD-CMAC, the conventional CMAC and a classical neural network model called Multi-Layer Perceptron (MLP) are simulated and evaluated for 1-hour-ahead prediction and 24-hour-ahead prediction of carbon monoxide as one of primary air pollutants. Carbon monoxide data used in this evaluation were recorded and averaged at Villa station in Tehran, Iran from October 3^rd. 2001 to March 14^th. 2002 at one-hour intervals. The results show that the errors made by TD-CMAC is fewer than those made by other models.