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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
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Water-removed spectra increase the retrieval accuracy when estimating savanna grass nitrogen and phosphorus concentrations 总被引：2，自引：0，他引：2
Abel Ramoelo Andrew K. SkidmoreMartin Schlerf Renaud MathieuIgnas M.A. Heitkönig 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(4):408-417
Information about the distribution of grass foliar nitrogen (N) and phosphorus (P) is important for understanding rangeland vitality and for facilitating the effective management of wildlife and livestock. Water absorption effects in the near-infrared (NIR) and shortwave-infrared (SWIR) regions pose a challenge for nutrient estimation using remote sensing. The aim of this study was to test the utility of water-removed (WR) spectra in combination with partial least-squares regression (PLSR) and stepwise multiple linear regression (SMLR) to estimate foliar N and P, compared to spectral transformation techniques such as first derivative, continuum removal and log-transformed (Log(1/R)) spectra. The study was based on a greenhouse experiment with a savanna grass species (Digitaria eriantha). Spectral measurements were made using a spectrometer. The D. eriantha was cut, dried and chemically analyzed for foliar N and P concentrations. WR spectra were determined by calculating the residual from the modelled leaf water spectra using a nonlinear spectral matching technique and observed leaf spectra. Results indicated that the WR spectra yielded a higher N retrieval accuracy than a traditional first derivative transformation (R2=0.84, RMSE = 0.28) compared to R2=0.59, RMSE = 0.45 for PLSR. Similar trends were observed for SMLR. The highest P retrieval accuracy was derived from WR spectra using SMLR (R2=0.64, RMSE = 0.067), while the traditional first derivative and continuum removal resulted in lower accuracy. Only when using PLSR did the first derivative result in a higher P retrieval accuracy (R2=0.47, RMSE = 0.07) than the WR spectra (R2=0.43, RMSE = 0.070). It was concluded that the water removal technique is a promising technique to minimize the perturbing effect of foliar water content when estimating grass nutrient concentrations. 相似文献
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2019年12月26日湖北应城发生M4.9有感地震,其震感波及武汉大部分地区。为了分析该地震的发震构造及余震活动性,本文利用波形拟合方法测定了不同速度模型下该地震的震源机制解和矩心深度,并用Bootstrapping抽样反演技术评价反演结果;此外,利用模板匹配技术匹配主震和目录余震波形,获取了更为完整的余震目录。结果显示,应城地震以走滑为主,矩心深度7.5km左右,矩震级MW4.67;应城地震有1个前震和17个余震,余震序列缺少M2~4事件,表明应城地震为孤立型地震,M2以下地震的b值为0.8。 相似文献
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P. J. G. Teunissen 《Journal of Geodesy》2001,75(5-6):267-275
The purpose of carrier phase ambiguity resolution is to improve upon the quality of the estimated global navigation satellite
system baseline by means of the integer ambiguity constraints. However, in order to evaluate the quality of the ambiguity
resolved baseline rigorously, its probability distribution is required. This baseline distribution depends on the random characteristics
of the estimated integer ambiguities, which in turn depend on the chosen integer estimator. In this contribution is presented
an exact and closed-form expression for the baseline distribution in the case that use is made of integer bootstrapping. Also
presented are the bootstrapped probability mass function and easy-to-compute measures for the bootstrapped baseline's probability
of concentration.
Received: 28 September 2000 / Accepted: 11 January 2001 相似文献
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