Modelling the effect of changing precipitation inputs on deep soil water utilization

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.     

Developing constitutive models from EPR‐based self‐learning finite element analysis

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.     

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

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.     

ENVIRONMENTAL SUSTAINABILITY AND SCENARIOS OF URBANIZATION IN ARID AREA——A Case Study in Wuwei City of Gansu Province

1IN T R O D U C T IO N The western China isfacinggrowing problems of eco- logicaland economic development. The disparitiebse- tween the socio-economy of the western and eastern China were enlarging, and the major ecologicalprob- lems stilelvolve as past(H…     

青藏块体东北缘断层形变与中强地震

对祁连山－海原断裂带近期断层形变特征进行了初步研究，发现多场地，大范围的断层活动异常是中等强度地震发生的显著背景，并且往往与大陆地震活动的阶段性总体状况相呼应；区域形变存在明显的特征量，包括特征形态和特征时间，同一场地在不同地震前的异常特征具有重复性，但会受到背景差异显著的不同地震的影响，目前形变状况表明研究区仍具有发生中强地震的地壳运动背景。     

试论企业组织结构--电子商务企业与传统企业的比较

网络时代的到来，对传统企业的组织结构、运作模式、管理方法均提出了严峻的挑战。传统的企业组织结构是适应大工业生产而形成的等级组织结构形式，它有很大的局限性；电子商务企业的组织结构是适应网络社会而形成扁平化、双向多对多、以信息为基础的新型组织结构形式。电子商务企业以其组织结构扁平化和中空化，决策分散化，运作虚拟化，灵活高效为特点，它将成为传统企业在电子商务发展阶段变革的方向和目标。     

超基性岩中含铜、钴块状硫化物矿床——德尔尼铜矿成因新认识

长期以来对德尔尼铜矿的成因存在着不同认识。从矿石组成和结构、构造来看,应属典型的块状硫化物矿石,矿床亦应属于含铜黄铁矿型矿床。但从其地质产状来看又与一般的黄铁矿型矿床大不相同,与一般的岩浆熔离铜镍硫化物矿床也有较多差异。这就是本矿床类型独特之处。近年来通过工作又取得一些新资料,特别是超基性岩和矿石的同位素年龄数据。本文在综合新老资料基础上,提出新看法,认为该矿床形成于上地幔,再就位于地壳浅部。其成因类型暂定为:“深部熔离—构造侵位矿床”。鉴于本矿床与一般块状硫化物矿床相比有其独特性,建议命名为“德尔尼型”。     

Automatic Determination of Stellar Atmospheric Parameters Using Neural Networks and Instance-Based Machine Learning

In this article we show how machine learning methods can beeffectively applied to the problem of automatically predictingstellar atmospheric parameters from spectral information, a veryimportant problem in stellar astronomy. We apply feedforwardneural networks, Kohonen's self-organizing maps andlocally-weighted regression to predict the stellar atmosphericparameters effective temperature, surface gravity and metallicityfrom spectral indices. Our experimental results show that thethree methods are capable of predicting the parameters with verygood accuracy. Locally weighted regression gives slightly betterresults than the other methods using the original dataset asinput, while self-organizing maps outperform the other methods when significant amounts of noise are added. We also implemented a heterogeneous ensemble of predictors, combining the results given by the three algorithms. This ensemble yields better results than any of the three algorithms alone, using both the original and the noisy data.     

广西森林旅游资源的特色及其开发意义

广西森林旅游资源多达 816 .7万hm2 ,景点 2 80个 ,内容丰富 ,前景广阔 ,加强广西森林旅游资源特色的开发 ,有利于促进广西旅游业可持续发展 ,有利于经济、社会、环境效益的提高及人们的文化素质和生活质量的提高。     

Prediction of Stellar Atmospheric Parameters using Instance-Based Machine Learning and Genetic Algorithms

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.