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1.
Forests in the Southeastern United States are predicted to experience future changes in seasonal patterns of precipitation inputs as well as more variable precipitation events. These climate change‐induced alterations could increase drought and lower soil water availability. Drought could alter rooting patterns and increase the importance of deep roots that access subsurface water resources. To address plant response to drought in both deep rooting and soil water utilization as well as soil drainage, we utilize a throughfall reduction experiment in a loblolly pine plantation of the Southeastern United States to calibrate and validate a hydrological model. The model was accurately calibrated against field measured soil moisture data under ambient rainfall and validated using 30% throughfall reduction data. Using this model, we then tested these scenarios: (a) evenly reduced precipitation; (b) less precipitation in summer, more in winter; (c) same total amount of precipitation with less frequent but heavier storms; and (d) shallower rooting depth under the above 3 scenarios. When less precipitation was received, drainage decreased proportionally much faster than evapotranspiration implying plants will acquire water first to the detriment of drainage. When precipitation was reduced by more than 30%, plants relied on stored soil water to satisfy evapotranspiration suggesting 30% may be a threshold that if sustained over the long term would deplete plant available soil water. Under the third scenario, evapotranspiration and drainage decreased, whereas surface run‐off increased. Changes in root biomass measured before and 4 years after the throughfall reduction experiment were not detected among treatments. Model simulations, however, indicated gains in evapotranspiration with deeper roots under evenly reduced precipitation and seasonal precipitation redistribution scenarios but not when precipitation frequency was adjusted. Deep soil and deep rooting can provide an important buffer capacity when precipitation alone cannot satisfy the evapotranspirational demand of forests. How this buffering capacity will persist in the face of changing precipitation inputs, however, will depend less on seasonal redistribution than on the magnitude of reductions and changes in rainfall frequency. 相似文献
2.
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
3.
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
4.
Prediction of Stellar Atmospheric Parameters using Instance-Based Machine Learning and Genetic Algorithms 总被引:1,自引:0,他引:1
In this article we present a method for the automated prediction of stellar atmospheric parameters from spectral indices.
This method uses a genetic algorithm (GA) for the selection of relevant spectral indices and prototypical stars and predicts
their properties, using the k-nearest neighbors method (KNN). We have applied the method to predict the effective temperature,
surface gravity, metallicity, luminosity class and spectral class of stars from spectral indices. Our experimental results
show that the feature selection performed by the genetic algorithm reduces the running time of KNN up to 92%, and the predictive
accuracy error up to 35%.
This revised version was published online in July 2006 with corrections to the Cover Date. 相似文献
5.
In urban environments, one major concern with deep excavations in soft clay is the potentially large ground deformations in and around the excavation. Excessive movements can damage adjacent buildings and utilities. There are many uncertainties associated with the calculation of the ultimate or serviceability performance of a braced excavation system. These include the variabilities of the loadings, geotechnical soil properties, and engineering and geometrical properties of the wall. A risk‐based approach to serviceability performance failure is necessary to incorporate systematically the uncertainties associated with the various design parameters. This paper demonstrates the use of an integrated neural network–reliability method to assess the risk of serviceability failure through the calculation of the reliability index. By first performing a series of parametric studies using the finite element method and then approximating the non‐linear limit state surface (the boundary separating the safe and ‘failure’ domains) through a neural network model, the reliability index can be determined with the aid of a spreadsheet. Two illustrative examples are presented to show how the serviceability performance for braced excavation problems can be assessed using the reliability index. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献
6.
This study is devoted to oceanographic features of the semi-enclosed Gulf of Aqaba, Red Sea. The data were recorded in winter—spring
1999 on the R/V Meteor cruise leg 44/2. Temperature and salinity profiles were measured at six positions (I—VI). The shipboard
NarrowBand Acoustic Doppler Current Profiler (NB ADCP) 150 kHz continuously recorded current profiles down to 350 m en route.
The research revealed that the current near the Strait of Tiran front (position VI) represents a semidiurnal signal of an
internal tide wave (∼12 h period; 0.2 ms−1 amplitude) that might be generated by the barotropic tide at the sill of the Strait. A sequence of cyclonic and anticyclonic
eddy pairs is found along the axis of the Gulf of Aqaba during winter-spring seasons. These sub-mesoscale signals are dominant
above the main thermocline and might be caused by wind forcing and the narrowness of the Gulf; it might remain in other seasons
with different dimensions in relation to the depth of thermocline. The total diameter of each pair was twice the baroclinic
Rossby radius (R ≈ 10 km). A single anti-cyclonic eddy was observed in the upper 300 m in the northern tip of the Gulf with a diameter of
about 5–8 km. 相似文献
7.
本文记述了南海晚第四纪沉积物中发现的两个新种[线形辐环藻(Actinocyclus lineatus Liu,Cheng et Lan sp.nov.)、星圆筛藻(Coscinodiscus aster-aeus Cheng,Liu et Lan sp.nov.)]以及在我国首次记录的圆筛藻的7个种。 相似文献
8.
Abstract. Benthic metabolism and standing stocks were investigated in the deep Red Sea between 21o and 27o N, Activity was assessed by the determination of respiration rates with a shipboard method and by calculating oxygen consumption from the activity in the electron transport system. We attempted to compare results from different latitudes within the warm Red Sea and with data from cold Atlantic environments. Our investigations were part of an environmental risk assessment to evaluate future mining of metalliferous sediments from the Atlantis II Deep. 相似文献
9.
烟台市污水处理深海排放工程设计方案研究与选择 总被引:2,自引:0,他引:2
沿海城市污水处理深海排放工程在我国起步较晚,烟台市污水处理深海排放工程我国目前规模最大的同类工程,且已进入施工阶段,本文详细阐述了该工程及水工设计方案,在我国城市污水2深海排放工程领域作了积极有益的探索。 相似文献
10.
通过调查和比较,认为在我国渤海和北黄海南岸的套子湾八角海区是一个条件优越的深水港址,在阐述开发八角深水港的重要意义同时,根据城市布局规划和岸线及水域资源,参照国际港口未来发展趋势,结合我国对中、西部地区经济发展战略,对八角深水港的开发和布局进行了研究,为烟台市发展成为“现代化、国际型港口城市”,提出了港区未来建设的模式。 相似文献