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1.
The 33 086 ha mixed land use Fall Creek watershed in upstate New York is part of the Great Lakes drainage system. Results from more than 3500 water samples are available in a data set that compiles flow data and measurements of various water quality analytes collected between 1972 and 1995 in all seasons and under all flow regimes in Fall Creek and its tributaries. Data is freely accessible at https://ecommons.cornell.edu/handle/1813/8148 and includes measurements of suspended solids, pH, alkalinity, calcium, magnesium, potassium, sodium, chloride, nitrate nitrogen (NO3-N), sulphate sulphur (SO4-S), phosphorus (P) fractions molybdate reactive P (MRP) and total dissolved P (TDP), percent P in sediment, and ammonium nitrogen (NH4-N). Methods, sub-watershed areas, and coordinates for sampling sites are also included. The work represented in this data set has made important scientific contributions to understanding of hydrological and biogeochemical processes that influence loading in mixed use watersheds and that have an impact on algal productivity in receiving water bodies. In addition, the work has been foundational for important regulatory and management decisions in the region. 相似文献
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为建立高精度的边坡位移预测模型,采用相空间重构(PSR)将边坡位移时间序列数据转换为多维数据,同时构造小波核函数改进的支持向量机模型,建立PSR-WSVM模型并应用于边坡位移预测。将PSR-WSVM模型预测结果与传统支持向量机(SVM)模型、小波支持向量机(WSVM)模型和基于相空间重构的支持向量机(PSR-SVM)模型预测结果进行对比,通过平均绝对误差(MAE)、平均绝对误差百分比(MAPE)和均方根误差(RMSE)3个精度评价指标验证PSR-WSVM模型的可行性。工程实例结果表明,PSR-WSVM模型预测结果的3个精度评价指标都优于另外3种模型,边坡位移预测的精度明显提升。 相似文献
3.
Determination of subcatchment and watershed boundaries in a complex and highly urbanized landscape 下载免费PDF全文
Urban development significantly alters the landscape by introducing widespread impervious surfaces, which quickly convey surface run‐off to streams via stormwater sewer networks, resulting in “flashy” hydrological responses. Here, we present the inadequacies of using raster‐based digital elevation models and flow‐direction algorithms to delineate large and highly urbanized watersheds and propose an alternative approach that accounts for the influence of anthropogenically modified land cover. We use a semi‐automated approach that incorporates conventional drainage networks into overland flow paths and define the maximal run‐off contributing area. In this approach, stormwater pipes are clustered according to their slope attributes, which define flow direction. Land areas drained by each cluster and contributing (or exporting) flow to a topographically delineated catchment were determined. These land masses were subsequently added or removed from the catchment, modifying both the shape and the size. Our results in a highly urbanized Toronto, Canada, area watershed indicate a moderate net increase in the directly connected watershed area by 3% relative to a topographically forced method; however, differences across three smaller scale subcatchments are greater. Compared to topographic delineation, the directly connected watershed areas of both the upper and middle subcatchments decrease by 5% and 8%, respectively, whereas the lower subcatchment area increases by 15%. This is directly related to subsurface storm sewer pipes that cross topographic boundaries. When directly connected subcatchment area is plotted against total streamflow and flashiness indices using this method, the coefficients of variation are greater (0.93 to 0.97) compared to the use of digital elevation model‐derived subcatchment areas (0.78 to 0.85). The accurate identification of watershed and subcatchment boundaries should incorporate ancillary data such as stormwater sewer networks and retention basin drainage areas to reduce water budget errors in urban systems. 相似文献
4.
A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted. 相似文献
5.
利用内蒙古西部12个台站的地脉动噪声数据,采用噪声谱比法研究台站的场地响应情况。分析表明,台站场地响应按曲线形态可分为3类,且可能受地形地貌、局部构造和台基状况等条件影响。对比分析噪声谱比法与Moya方法的场地响应结果发现,场地响应曲线形态基本一致,只有极少数台站存在明显差异。结果表明,内蒙古西部区域大部分台站的台基状况总体较好,场地响应曲线较为平坦,无明显频率放大点。 相似文献
6.
Peng Yue Fan Gao Boyi Shangguan Zheren Yan 《International journal of geographical information science》2020,34(11):2243-2274
ABSTRACT High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning. 相似文献
7.
《Astroparticle Physics》2002,16(4):183-386
Frequency distributions of local muon densities in high-energy extensive air showers (EAS) are presented as signature of the primary cosmic ray energy spectrum in the knee region. Together with the gross shower variables like shower core position, angle of incidence, and the shower sizes, the KASCADE experiment is able to measure local muon densities for two different muon energy thresholds. The spectra have been reconstructed for various core distances, as well as for particular subsamples, classified on the basis of the shower size ratio Nμ/Ne. The measured density spectra of the total sample exhibit clear kinks reflecting the knee of the primary energy spectrum. While relatively sharp changes of the slopes are observed in the spectrum of EAS with small values of the shower size ratio, no such feature is detected at EAS of large Nμ/Ne ratio in the energy range of 1–10 PeV. Comparing the spectra for various thresholds and core distances with detailed Monte Carlo simulations the validity of EAS simulations is discussed. 相似文献
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