共查询到20条相似文献，搜索用时 15 毫秒
1.
Pritpal Singh 《地学前缘(英文版)》2018,9(4):1243-1257
This study presents a model to forecast the Indian summer monsoon rainfall(ISMR)(June-September)based on monthly and seasonal time scales. The ISMR time series data sets are classified into two parts for modeling purposes, viz.,(1) training data set(1871-1960), and(2) testing data set(1961-2014).Statistical analyzes reflect the dynamic nature of the ISMR, which couldn't be predicted efficiently by statistical and mathematical based models. Therefore, this study suggests the usage of three techniques,viz., fuzzy set, entropy and artificial neural network(ANN). Based on these techniques, a novel ISMR time series forecasting model is designed to deal with the dynamic nature of the ISMR. This model is verified and validated with training and testing data sets. Various statistical analyzes and comparison studies demonstrate the effectiveness of the proposed model. 相似文献
2.
This paper presents a neural network (NN) based model to assess the regional hazard degree of debris flows in Lake Qionghai
Watershed, China. The NN model was used as an alternative for the more conventional linear model MFCAM (multi-factor composite
assessment model) in order to effectively handle the nonlinearity and uncertainty inherent in the debris flow hazard analysis.
The NN model was configured using a three layer structure with eight input nodes and one output node, and the number of nodes
in the hidden layer was determined through an iterative process of varying the number of nodes in the hidden layer until an
optimal performance was achieved. The eight variables used to represent the eight input nodes include density of debris flow
gully, degree of weathering of rocks, active fault density, area percentage of slope land greater than 25° of the total land
(APL25), frequency of flooding hazards, average covariance of monthly precipitation by 10 years (ACMP10), average days with
rainfall >25 mm by 10 years (25D10Y), and percentage of cultivated land with slope land greater than 25° of the total cultivated
land (PCL25). The output node represents the hazard-degree ranks (HDR). The model was trained with the 35 sets of data obtained
from previous researches reported in literatures, and an explicit uncertainty analysis was undertaken to address the uncertainty
in model training and prediction. Before the NN model is extrapolated to Lake Qionghai Watershed, a validation case, different
from the above data, is conducted. In addition, the performances of the NN model and the MFCAM were compared. The NN model
predicted that the HDRs of the five sub-watersheds in the Lake Qionghai Watershed were IV, IV, III, III, and IV–V, indicating
that the study area covers normal hazard and severe hazard areas. Based on the NN model results, debris flow management and
economic development strategies in the study are proposed for each sub-watershed. 相似文献
3.
A comparative analysis among computational intelligence techniques for dissolved oxygen prediction in Delaware River 总被引：1，自引：0，他引：1
Most of the water quality models previously developed and used in dissolved oxygen (DO) prediction are complex. Moreover, reliable data available to develop/calibrate new DO models is scarce. Therefore, there is a need to study and develop models that can handle easily measurable parameters of a particular site, even with short length. In recent decades, computational intelligence techniques, as effective approaches for predicting complicated and significant indicator of the state of aquatic ecosystems such as DO, have created a great change in predictions. In this study, three different AI methods comprising: (1) two types of artificial neural networks (ANN) namely multi linear perceptron (MLP) and radial based function (RBF); (2) an advancement of genetic programming namely linear genetic programming (LGP); and (3) a support vector machine (SVM) technique were used for DO prediction in Delaware River located at Trenton, USA. For evaluating the performance of the proposed models, root mean square error (RMSE), Nash–Sutcliffe efficiency coefficient (NS), mean absolute relative error (MARE) and, correlation coefficient statistics (R) were used to choose the best predictive model. The comparison of estimation accuracies of various intelligence models illustrated that the SVM was able to develop the most accurate model in DO estimation in comparison to other models. Also, it was found that the LGP model performs better than the both ANNs models. For example, the determination coefficient was 0.99 for the best SVM model, while it was 0.96, 0.91 and 0.81 for the best LGP, MLP and RBF models, respectively. In general, the results indicated that an SVM model could be employed satisfactorily in DO estimation. 相似文献
4.
为克服马氏距离判别模型无法考虑指标权重的不足,引入粗糙集理论,通过分析评判方法对评价对象的支持度和重要性计算得到权重系数。将权重系数嵌入距离判别模型,构建了边坡稳定性预测的加权距离判别模型。根据边坡失稳破坏特点,选取合理的判别因子,以大量工程实例样本作为原始数据和训练样本,建立了边坡稳定性评价预测的粗糙集-距离判别模型。将边坡稳定性评价预测的粗糙集-距离判别模型评价预测结果与马氏距离判别法、支持向量机理论、Bayes判别分析等方法得到的预测结果进行了对比分析,验证了粗糙集-距离判别模型的有效性。将建立的粗糙集-距离判别模型应用于黄河中游地区某大型水利枢纽库区边坡工程,预测结果与实际情况吻合。研究结果表明,粗糙集-距离判别模型具有权重分析合理、预测准确性高等优点,是进行边坡稳定性分析预测的一种新的有效途径。 相似文献
5.
6.
Predicting groundwater level of wells in the Diyala River Basin in eastern Iraq using artificial neural network 
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下载免费PDF全文 Al-Mansourieh zone is a part of Al-Khalis City within the province of Diyala and located in the Diyala River Basin in eastern Iraq with a total area about 830 km2.Groundwater is the main water source for agriculture in this zone.Random well drilling without geological and hydraulic information has led the most of these wells to dry up quickly.Therefore,it is necessary to estimate the levels of groundwater in wells through observed data.In this study,Alyuda NeroIntelligance 2.1 software was applied to predict the groundwater levels in 244 wells using sets of measured data.These data included the coordinates of wells(x,y),elevations,well depth,discharge and groundwater levels.Three ANN structures(5-3-3-1,5-10-10-1 and 5-11-11-1)were used to predict the groundwater levels and to acquire the best matching between the measured and ANN predicted values.The coefficient of correlation,coefficient determination(R2)and sum-square error(SSE)were used to evaluate the performance of the ANN models.According to the ANN results,the model with the three structures has a good predictability and proves more effective for determining groundwater level in wells.The best predictor was achieved in the structure 5-3-3-1,with R2 about 0.92,0.89,0.84 and 0.91 in training,validation,testing and all processes respectively.The minimum average error in the best predictor is achieved in validation and testing processes at about 0.130 and 0.171 respectively.On the other hand,the results indicated that the model has the potential to determine the appropriate places for drilling the wells to obtain the highest level of groundwater. 相似文献
7.
The paper studied by Yong-chi Li and R.F. Shen (2007) includes some problems regarding the application of the proposed Neural Network (NN) approach. A very limited number of data has been used as testing set (only 3 among 52 data sets) which should be 20–30% of the database. The NN application in the study is not a function approximation problem where same input must always lead to the same output. NNs cannot be used for such an application. Moreover the generalization capability of the NN model has not been investigated. This discussion, aims to points out controversial points of the paper. 相似文献
8.
River flow is a complex dynamic system of hydraulic and sediment transport. Bed load transport have a dynamic nature in gravel bed rivers and because of the complexity of the phenomenon include uncertainties in predictions. In the present paper, two methods based on the Artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are developed by using 360 data points. Totally, 21 different combination of input parameters are used for predicting bed load transport in gravel bed rivers. In order to acquire reliable data subsets of training and testing, subset selection of maximum dissimilarity (SSMD) method, rather than classical trial and error method, is used in finding randomly manipulation of these subsets. Furthermore, uncertainty analysis of ANN and ANFIS models are determined using Monte Carlo simulation. Two uncertainty indices of d factor and 95% prediction uncertainty and uncertainty bounds in comparison with observed values show that these models have relatively large uncertainties in bed load predictions and using of them in practical problems requires considerable effort on training and developing processes. Results indicated that ANFIS and ANN are suitable models for predicting bed load transport; but there are many uncertainties in determination of bed load transport by ANFIS and ANN, especially for high sediment loads. Based on the predictions and confidence intervals, the superiority of ANFIS to those of ANN is proved. 相似文献
9.
基于遗传神经网络的瓦斯含量预测研究 总被引：7，自引：0，他引：7
瓦斯含量预测取决于多因素、非线性的函数关系的建立 ,预测模型建立的准确与否决定于各个影响因素之间的相互作用、相互耦合的特性。文中将神经网络与遗传算法有机地结合起来 ,以神经网络理论为基础 ,利用遗传算法优化隐含层神经元个数和网络中的连接权值 ,建立瓦斯含量预测模型。在实验室测试数据的基础上 ,建立遗传神经网络训练和检验样本集 ,其中包含有 38个典型样本 ,并且将检验结果分别与回归模型、标准BP神经网络、自适应BP神经网络的预测结果进行比较。结果表明 :遗传神经网络模型可靠 ,预测精度高 ,为促进软计算技术与瓦斯地质的结合奠定了基础。 相似文献
10.
This paper aims to present the usability of an adaptive neuro fuzzy inference system (ANFIS) for the prediction swelling potential
of the compacted soils that are important materials for geotechnical purposes such as engineered barriers for municipal solid
waste, earth dams, embankment and roads. In this study the swelling potential that is also one of significant parameters for
compacted soils was modeled by ANFIS. For the training and testing of ANFIS model, data sets were collected from the tests
performed on compacted soils for different geotechnical application in Nigde. Four parameters such as coarse-grained fraction
ratio (CG), fine-grained fraction ratio (FG), plasticity index (PI) and maximum dry density (MDD) were presented to ANFIS
model as inputs. The results obtained from the ANFIS models were validated with the data sets which are not used for the training
stage. The analyses revealed that the predictions from ANFIS model are in sufficient agreement with test results. 相似文献
11.
概率神经网络是一种分类准确率高、泛用性强、可以包容一定数量错误样本的人工神经网络,极其适合勘查地球化学找矿中的预测找矿靶区。本文以四川雅江县木绒锂矿为例,运用概率神经网络搭建智能找矿模型,以已知区的Li元素及与其相关性强的Rb-Cs-Al-Fe元素作为训练指标,对模型进行训练,经过多次训练后将Spread值确定为0.08,使模型在训练集和测试集的准确率均大于80%,实现非线性的指标与成矿潜力的对应,得到本矿区的PNN模型,然后对预测区的样本数据进行预测,成功圈定了1处靶区。为检验靶区准确性,以Li、Rb、Cs元素数据累计频率的80%作为异常下限,圈出的异常区域与靶区位置基本重叠。对预测区进行了实地查证工作,发现两条红柱石带,其中一条与靶区位置吻合,表明该神经网络模型准确性高,可用于矿产勘查的预测研究。 相似文献
12.
A soil deposit subjected to seismic loading can be viewed as a binary system: it will either liquefy or not liquefy. Generalized linear models are versatile tools for predicting the response of a binary system and hence potentially applicable to liquefaction prediction. In this study, the applicability of four generalized linear models (i.e., logistic, probit, log–log, and c-log–log) for liquefaction potential evaluation is assessed and compared. Eight liquefaction models based on the four generalized linear models and two sets of explanatory variables are evaluated. These models are first calibrated with past liquefaction performance data. A weighted-likelihood function method is used to consider the sampling bias in the calibration database. The predicted liquefaction probabilities from various models are then compared. When liquefaction probability is small, the predicted liquefaction probability is sensitive to the regression models used. The effect of sampling bias is more marked in the high cyclic stress ratio region. The eight models are finally ranked using a Bayesian model comparison method. For the generalized linear models examined, the logistic and c-log–log regression models are most supported by the past performance data. On the other hand, the probit and c-log–log regression models are much less applicable to liquefaction prediction. 相似文献
13.
Hazard assessment model for debris flow prediction 总被引：4，自引：3，他引：1
Debris flow disasters have plagued Taiwan in recent decades, and caused casualties and destruction of property. Several methods,
including the numerical method, statistical method, and experimental method, have been adopted in recent years to predict
debris flow, and more recently, the neural network (NN) and the genetic algorithm (GA) methods have been introduced to simulate
the occurrence of debris flows. This study proposes using the GA to weigh seven important variables according to principles
similar to natural selection. The study then simultaneously inputs these variables into a NN model to predict debris flow
occurrences based on relevant factors. There were 154 potential cases of debris flow collected from eastern Taiwan and fed
into the model for testing. The average ratio of successful prediction reached 94.94%, which demonstrates that the proposed
model can provide stable and reliable results for predicting debris flow in hazard mitigation and guard systems. 相似文献
14.
基于模糊预测对象的证据权方法及其在土地沙漠化评价中的应用 总被引：3，自引：3，他引：0
为了使在地理信息系统中被广泛用于点事件预测的证据权方法能对面事件进行评价和预测, 提出了一种新的基于模糊训练层的证据权方法.它是一种更广泛的证据权方法, 与普通证据权方法所不同的是, 它的训练层是模糊集合, 其取值是它的隶属度.通过适当的变换也可以把点训练层转换为模糊集合.因此, 该方法可以对面事件、点事件和线事件进行评价和预测.该方法可以处理训练层和证据层均为模糊集合的情况, 被称为双重模糊证据权方法.作为该方法的一个应用实例, 本文介绍毛乌素沙漠边缘的晋陕蒙地区土地沙漠化评价的应用实例. 相似文献
15.
Storm surge prediction using an artificial neural network model and cluster analysis 总被引：1，自引：1，他引：0
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. 相似文献
16.
人工神经网络模型在渭河下游洪水预报中的应用 总被引：2，自引：0，他引：2
通过对渭河下游站点的时间序列及空间分布的分析,确定出影响华县站流量的时间和空间信息,并将其引入神经网络模型;采用典型的BP神经网络,重点对网络的隐含层节点数、训练次数和学习率进行分析,构建了渭河下游华县断面流量预报的人工神经网络模型;并采用RMSE、NSC和相关系数 R作为模型效果评定标准,将其与传统多元统计回归模型进行了对比。结果表明,所建的BP神经网络模型较多元统计回归模型的预报效果有显著的提高。 相似文献
17.
A case study using support vector machines, neural networks and logistic regression in a GIS to identify wells contaminated with nitrate-N 总被引：2，自引：0，他引：2
Barnali Dixon 《Hydrogeology Journal》2009,17(6):1507-1520
Accurate and inexpensive identification of potentially contaminated wells is critical for water resources protection and management. The objectives of this study are to 1) assess the suitability of approximation tools such as neural networks (NN) and support vector machines (SVM) integrated in a geographic information system (GIS) for identifying contaminated wells and 2) use logistic regression and feature selection methods to identify significant variables for transporting contaminants in and through the soil profile to the groundwater. Fourteen GIS derived soil hydrogeologic and landuse parameters were used as initial inputs in this study. Well water quality data (nitrate-N) from 6,917 wells provided by Florida Department of Environmental Protection (USA) were used as an output target class. The use of the logistic regression and feature selection methods reduced the number of input variables to nine. Receiver operating characteristics (ROC) curves were used for evaluation of these approximation tools. Results showed superior performance with the NN as compared to SVM especially on training data while testing results were comparable. Feature selection did not improve accuracy; however, it helped increase the sensitivity or true positive rate (TPR). Thus, a higher TPR was obtainable with fewer variables. 相似文献
18.
随着土地开发建设规模不断扩大,土地利用情况也在逐年发生变化,准确预测未来土地利用的发展趋势,可以为本地区的土地利用规划提供依据,提升本地区的土地利用效率。传统方法一般采用CA_Markov、ANN以及CA_ANN模型进行预测,存在训练时间长、预测精度不足和缺乏说服力等问题。本文针对上述问题,结合元胞自动机以及人工神经网络模型,建立一种自适应可变滤镜网络模型,针对特定大小区域内的土地类别数目,创建多类数据集来训练不同参数的多个神经网络,可以成功预测未来土地变化的情况,这样就避免了训练单一网络时数据对网络权值的抵消。相比于传统模型中效果最好的CA_ANN模型,本文建立的自适应可变滤镜网络模型不仅总体精度提高了1%~3%,各种地类转化精度提高了12.82%~33.33%,模型预测时间也缩减了49.47%。 相似文献
19.
C. Kayadelen 《国际地质力学数值与分析法杂志》2008,32(9):1087-1106
Great efforts are required for determination of the effective stress parameter χ, applying the unsaturated testing procedure, since unsaturated soils that have the three‐phase system exhibit complex mechanical behavior. Therefore, it seems more reasonable to use the empirical methods for estimation of χ. The objective of this study is to investigate the practicability of using artificial neural networks (ANNs) to model the complex relationship between basic soil parameters, matric suction and the parameter χ. Five ANN models with different input parameters were developed. Feed‐forward back propagation was applied in the analyses as a learning algorithm. The data collected from the available literature were used for training and testing the ANN models. Furthermore, unsaturated triaxial tests were carried out under drained condition on compacted specimens. ANN models were validated by a part of data sets collected from the literature and data obtained from the current study, which were not included in the training phase. The analyses showed that the results obtained from ANN models are in satisfactory agreement with the experimental results and ANNs can be used as reliable tool for prediction of χ. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
20.
《International Journal of Mineral Processing》2005,75(1-2):53-68
The flow behavior in hydrocyclone is quite complex. This complexity of flow processes has led designers to rely on empirical equations for predicting the equipment performance. The publications on empirical models of the hydrocyclone far out-number few fluid-flow-modeling attempts. Empirical models correlate a classification parameter, such as the cut-size, with device dimensions and slurry properties. However, these can only be used within the extremes of the experimental data from which the model parameters were determined. On the other hand, models based on Computational Fluid Dynamics (CFD) techniques have proven to be useful in simulating fluid flow in hydrocyclones, and in predicting the separation efficiency of solid particles in the separator for a wide range of operating and design conditions. The shape and size of a hydrocyclone separator has a direct influence on the internal flow structure of the continuous phase and, thereby, the separation of the particulate phase. Hydrocylcones usually have a single inlet that distributes the feed stream near the end wall between the vortex finder and the sidewall. Effect of spigot diameter, i.e., 10 and 20 mm and inlet water velocities (5.91–12.35 m/s) on the water splits and particle classification in the hydrocyclone have been studied. The cut size of the hydrocyclone, operated at very low pulp density, has been predicted using discrete phase modeling technique. The studies revealed that with an increase in feed flow rate and decrease in spigot diameter the cyclone sharpness of separation improves. These predictions were found similar in line with the experimental observations. 相似文献