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71.
72.
Bootstrap based artificial neural network (BANN) analysis for hierarchical prediction of monthly runoff in Upper Damodar Valley Catchment 总被引:1,自引:0,他引:1
Estimation of runoff is a prerequisite for many applications involving conservation and management of water resources. This study is undertaken in the Upper Damodar Valley Catchment (UDVC) having a drainage area of 17513.08 km2 for prediction of monthly runoff. Thirty one microwatersheds and 15 sub-watersheds were selected from a total of 716 microwatersheds in the catchment area for this study. The feasibility of using different soil attributes (particle size distribution, organic matter content and apparent density), topographic attributes (primary, secondary and compound), geomorphologic attributes (basin, relief and network indices) and vegetation attribute as Normalized Difference Vegetation Index (NDVI), on prediction of monthly runoff were explored in this study. Principal Component Analysis (PCA) was applied to minimize the data redundancy of the input variables. Ten significant input variables namely; watershed length (km), elongation ratio, bifurcation ratio, area ratio, coarse sand (%), fine sand (%), elevation (m), slope (°), profile curvature (rad/m) and NDVI were selected. The selected input variables were added in hierarchy with monthly rainfall (mm) as inputs for prediction of monthly runoff (mm) using Bootstrap based artificial neural networks (BANN). The performance of the models was tested using Spearman’s correlation coefficient (r), coefficient of efficiency (COE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Best performance was observed for model with monthly rainfall, slope, coarse sand, bifurcation ratio and Normalized Difference Vegetation Index (NDVI) as inputs (r = 0.925 and COE = 0.839). Increase in number of input variables did not necessarily yield better performances of the BANN models. Selection of relevant inputs and their combinations were found to be key elements in determining the performance of BANN models. Annual runoff map was generated for all the microwatersheds utilizing the weights of the best performing BANN model. This study reveals that the specific combinations of soil, topography, geomorphology and vegetation inputs can be utilized for better prediction of monthly runoff. 相似文献
73.
Tae-Woong Kim Hosung Ahn 《Stochastic Environmental Research and Risk Assessment (SERRA)》2009,23(3):367-376
Missing data in daily rainfall records are very common in water engineering practice. However, they must be replaced by proper
estimates to be reliably used in hydrologic models. Presented herein is an effort to develop a new spatial daily rainfall
model that is specifically intended to fill in gaps in a daily rainfall dataset. The proposed model is different from a convectional
daily rainfall generation scheme in that it takes advantage of concurrent measurements at the nearby sites to increase the
accuracy of estimation. The model is based on a two-step approach to handle the occurrence and the amount of daily rainfalls
separately. This study tested four neural network classifiers for a rainfall occurrence processor, and two regression techniques
for a rainfall amount processor. The test results revealed that a probabilistic neural network approach is preferred for determining
the occurrence of daily rainfalls, and a stepwise regression with a log-transformation is recommended for estimating daily
rainfall amounts. 相似文献
74.
Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial Neural Network (ANN) 总被引:7,自引:0,他引:7
An efficient and accurate method of generating landslide susceptibility maps is very important to mitigate the loss of properties and lives caused by this type of geological hazard. This study focuses on the development of an accurate and efficient method of data integration, processing and generation of a landslide susceptibility map using an ANN and data from ASTER images. The method contains two major phases. The first phase is the data integration and analysis, and the second is the Artificial Neural Network training and mapping. The data integration and analysis phase involve GIS based statistical analysis relating landslide occurrence to geological and DEM (digital elevation model) derived geomorphological parameters. The parameters include slope, aspect, elevation, geology, density of geological boundaries and distance to the boundaries. This phase determines the geological and geomorphological factors that are significantly correlated with landslide occurrence. The second phase further relates the landslide susceptibility index to the important geological and geomorphological parameters identified in the first phase through ANN training. The trained ANN is then used to generate a landslide susceptibility map. Landslide data from the 2004 Niigata earthquake and a DEM derived from ASTER images were used. The area provided enough landslide data to check the efficiency and accuracy of the developed method. Based on the initial results of the experiment, the developed method is more than 90% accurate in determining the probability of landslide occurrence in a particular area. 相似文献
75.
76.
《Soil Dynamics and Earthquake Engineering》2000,20(1-4)
The paper deals with an application of neural networks for detection of natural periods of vibrations of prefabricated, medium height buildings. The neural network technique is also used to simulate the dynamic response at selected floor of one of the analysed buildings subject to seismic loading induced by explosives in a nearby quarry. Both the training and testing patterns were formulated on the basis of measurements performed on actual structures. The results of neural network identification of natural periods of the considered buildings obtained with different soil, geometrical and stiffness parameters are compared with the results of experiments. The application of back-propagation neural networks enables us to identify the natural periods of the buildings with accuracy quite satisfactory for engineering practice. The experimental and generated data of vibration displacements are compared and much clearer comparison is given on the phase plane: displacements versus velocities. It was stated that a good generalization takes place both with respect to displacements and velocities. 相似文献
77.
应用SVM方法进行沉积微相识别 总被引:15,自引:1,他引:14
作者针对目前沉积微相中的特征提取问题,提出了应用SVM(支持向量机)方法进行沉积微相识别的方案。该方法不是象传统方法那样首先试图将原输入空间降维(即特征选择变换),而是设法将输入空间升维,以求在高维空间中问题变得线性可分(或接近线性可分)。因为升维后只是改变了内积运算,并没有使算法复杂性随着维数的增加而增加,因此这种方法才是可行的。所以。利用该方法更能胜任实际情况。实际处理表明该方法在小样本情况下 相似文献
78.
Interpolation of wave heights 总被引:1,自引:0,他引:1
Remote sensing of waves often necessitates presentation of data in the form of wave height values grouped over large time intervals. This restricts their use to long-term applications only. This paper describes how such data can be made suitable for short-term usage in the field. Weekly mean significant wave heights were derived from their monthly mean observations with the help of different alternative techniques. These include model-free neural network schemes as well as model-based statistical and numerical methods. Superiority of neural networks was noted when the estimations were compared with corresponding observations. The network was trained using three different training algorithms, viz., error back propagation, conjugate gradient and cascade correlation. The technique of cascade correlation took minimum training time and showed better coefficient of correlation between observations and network output. 相似文献
79.
本文通过对福建及其周边地区地震活动人工神经网络模型的构建,研究了人工神经网络方法在基于该区域地震活动性指标的地震分析预报中的应用。选用含一个中章层的前向神经网络模型,并采用与之相适应的BP算法,以该地区1971~1997年的地震活动性资料为基础,用神经网络进行实际计算、分析和检验。结果表明:神经网络模型对福建及其周边地区地震震级的预测检验效果较好的,可以在一定精度范围内使震级预测的内符率达100% 相似文献