Biogeography and Genetic Diversity of Pearl Millet (Pennisetum glaucum) from Sahelian Africa

Sahelian Africa makes up the native range of pearl millet and the impact of domestication on the genetic diversity of wild, intermediate, and cultivated subspecies is still poorly understood. Wild populations are known to spontaneously germinate throughout this range and hybridize with cultivated material. To investigate genetic structure, populations of pearl millet from several African countries were analyzed at the morphological and molecular levels for diversity. Results demonstrate how little differentiation there is between subspecies and populations. It appears that pearl millet throughout the Sahel remains a metapopulation with large hybrid zones. These results have important implications for conservation of pearl millet.     

A spatial genetic algorithm for automating land partitioning

Land fragmentation is a widespread situation which may often hinder agricultural development. Land consolidation is considered to be the most effective land management planning approach for controlling land fragmentation and hence improving agricultural efficiency. Land partitioning is a basic process of land consolidation that involves the subdivision of land into smaller sub-spaces subject to a number of constraints. This paper explains the development of a module called LandParcelS (Land Parcelling System) that is a part of an integrated planning and decision support system called LACONISS (LAndCONsolidation Integrated Support System) which has been developed to assist land consolidation planning in Cyprus. LandParcelS automates the land partitioning process by designing and optimising land parcels in terms of their shape, size and value. The methodology integrates geographical information systems and a genetic algorithm that has been applied to two land blocks that are part of a larger case study area in Cyprus. Partitioning is treated as either a single or multi-objective problem for various optimisation cases. The results suggest that a step forward has been made in solving this complex spatial problem, although further research is needed to improve the algorithm. This approach may have relevance to other spatial planning tasks that involve single or multi-objective optimisation problems, especially those dealing with space partitioning.     

Sediment fingerprinting in fluvial systems： review of tracers,sediment sources and mixing models

Suspended sediments in fluvial systems originate from a myriad of diffuse and point sources, with the relative contribution from each source varying over time and space. The process of sediment fingerprinting focuses on developing methods that enable discrete sediment sources to be identified from a composite sample of suspended material. This review identifies existing methodological steps for sediment fingerprinting including fluvial and source sampling, and critically compares biogeochemical and physical tracers used in fingerprinting studies. Implications of applying different mixing models to the same source data are explored using data from 41 catchments across Europe, Africa, Australia, Asia, and North and South America. The application of seven commonly used mixing models to two case studies from the US （North Fork Broad River watershed） and France （Bldone watershed） with local and global （genetic algorithm） optimization methods identified all outputs remained in the acceptable range of error defined by the original authors. We propose future sediment fingerprinting studies use models that combine the best explanatory parameters provided by the modified Collins （using correction factors） and Hughes （relying on iterations involving all data, and not only their mean values） models with optimization using genetic algorithms to best predict the relative contribution of sediment sources to fluvial systems.     

中国东南屯溪-温州宽角地震资料去广角效应成像

由于长排列广角地震采集中必定存在超临界角的广角反射信息,广角反射信息相对临界角内反射信息存在随偏移距变化的时移现象.本文研究了去广角效应下的走时反演成像,并将其应用到东南屯溪—温州的宽角地震资料.通过拾取中国东南屯溪—温州剖面宽角实际地震资料中P波垂直分量拾取到的反射P波走时,继而使用遗传反演方法进行处理,得到了该区丰乐、新安江、松阳、青田以及洞头五炮去广角效应与未考虑广角效应情况下的一维速度深度曲线,并对比了去广角效应前后的地壳结构界面形态.结果表明,广角效应去除前后影响主要体现在壳内层速度差异,地壳浅层界面深度的广角效应明显强于深层界面.     

Spatial interpolation using nonlinear mathematical programming models for estimation of missing precipitation records

Abstract

New mathematical programming models are proposed, developed and evaluated in this study for estimating missing precipitation data. These models use nonlinear and mixed integer nonlinear mathematical programming (MINLP) formulations with binary variables. They overcome the limitations associated with spatial interpolation methods relevant to the arbitrary selection of weighting parameters, the number of control points within a neighbourhood, and the size of the neighbourhood itself. The formulations are solved using genetic algorithms. Daily precipitation data obtained from 15 rain gauging stations in a temperate climatic region are used to test and derive conclusions about the efficacy of these methods. The developed methods are compared with some naïve approaches, multiple linear regression, nonlinear least-square optimization, kriging, and global and local trend surface and thin-plate spline models. The results suggest that the proposed new mathematical programming formulations are superior to those obtained from all the other spatial interpolation methods tested in this study.

Editor D. Koutsoyiannis; Associate editor S. Grimaldi

Citation Teegavarapu, R.S.V., 2012. Spatial interpolation using nonlinear mathematical programming models for estimation of missing precipitation records. Hydrological Sciences Journal, 57 (3), 383–406.     

Assessment of sediment transport approaches for sand-bed rivers by means of machine learning

Abstract

The quantification of the sediment carrying capacity of a river is a difficult task that has received much attention. For sand-bed rivers especially, several sediment transport functions have appeared in the literature based on various concepts and approaches; however, since they present a significant discrepancy in their results, none of them has become universally accepted. This paper employs three machine learning techniques, namely artificial neural networks, symbolic regression based on genetic programming and an adaptive-network-based fuzzy inference system, for the derivation of sediment transport formulae for sand-bed rivers from field and laboratory flume data. For the determination of the input parameters, some of the most prominent fundamental approaches that govern the phenomenon, such as shear stress, stream power and unit stream power, are utilized and a comparison of their efficacy is provided. The results obtained from the machine learning techniques are superior to those of the commonly-used sediment transport formulae and it is shown that each of the input combinations tested has its own merit, as they produce similarly good results with respect to the data-driven technique employed.

Editor Z.W. Kundzewicz     

Optimizing artificial neural network-based indoor positioning system using genetic algorithm

Abstract

The Global Positioning System (GPS) is expected to play an integral role in the development of digital earth; however, the GPS cannot provide positioning information in regions where a majority of the population spends their time, that is, in urban and indoor environments. Hence, alternate positioning systems that work in indoor and urban environments should be developed to achieve the vision of digital earth. Wi-Fi-based positioning systems (WPS) stand out because of the near-ubiquitous presence of the associated infrastructure and signals in indoor environments. The WPS-based fingerprinting is the most widely adopted technique for position determination, but its accuracy is lower than that of techniques such as time of arrival and angle of arrival. Improving the accuracy is still a challenging task because of the complex nature of the propagation of Wi-Fi signals. Here, a novel server-based, genetic-algorithm-optimized, cascading artificial neural network-based positioning model is presented. The model is tested in 2D and 3D indoor environments under varying conditions. The model is thoroughly investigated on a real Wi-Fi network, and its accuracy is found to be better than that of other well-known techniques. A mean accuracy of 1.9 m is achieved with 87% of the distance error within the range of 0–3 m.     

利用数值投影变换构建全球六边形离散格网

本文针对全球离散格网构建过程中从平面格网到球面格网的关键步骤进行讨论,提出了一种新型的评价球面离散格网几何属性最优化的目标函数,使用遗传算法优化,得到了球面上有限层次内最优化条件下的直接剖分格网。利用有限层次内最优化格网提供的控制点数据,结合数值投影变换理论,成功地构建了几何属性更加均匀的全球六边形离散格网系统。实验表明,相对于现有Snyder等积投影建立的全球格网,在格网单元的均匀度上更优;在运算效率方面,速度大约是Snyder投影的2.5-3倍。     

基于遗传算法的RBF神经网络在GPS高程拟合中的应用

本文针对RBF神经网络中隐含层径向基中心值的确定,利用遗传算法对其进行优化,并应用于高程拟合的实验研究中。通过将遗传算法优化的RBF神经网络与K-均值优化的RBF神经网络及标准RBF神经网络进行高程拟合的误差对比分析表明:遗传算法优化的RBF神经网络提高了拟合的稳定度,改善了精度。     

The decision of the optimal parameters in Markov random fields of images by genetic algorithm

This paper introduces the principle of genetic algorithm and the basic method of solving Markov random field parameters. Focusing on the shortcomings in present methods, a new method based on genetic algorithms is proposed to solve the parameters in the Markov random field. The detailed procedure is discussed. On the basis of the parameters solved by genetic algorithms, some experiments on classification of aerial images are given. Experimental results show that the proposed method is effective and the classification results are satisfactory.