Regional land-use allocation using a coupled MAS and GA model: from local simulation to global optimization,a case study in Caidian District,Wuhan, China     

Man Yuan  Jianhua He  Dianfeng Liu 《制图学和地理信息科学》2013,40(4):363-378

China is facing the pressures of both rapid economic development and environmental protection, and land-use allocation optimization is an important way to manage the conflicts between these pressures and to achieve sustainable development. Optimization of land-use allocation is a nonlinear multiobjective spatial optimization problem, and a purely local simulation model or global optimization model is insufficient to solve it. It is essential to bridge the gap between the two models through the combination of top-down and bottom-up approaches. This study integrates a multiagent system (MAS) that simulates the behaviors of land-use stakeholders with regard to their choices of specific locations, with a genetic algorithm (GA) that simultaneously evaluates and optimizes land-use configurations to meet various regional development objectives. The model is expected to achieve the optimization of land use in terms of the composition and spatial configuration. Caidian District, Wuhan, China, was chosen as the study area to test the model in this paper. The results show that the performance of the coupled model is superior to a pure GA model or MAS model. The optimal configuration improves on the economic output, spatial compactness, and carbon storage of the current configuration and promotes sustainable regional land-use development from the local scale to the regional scale.  相似文献   

Diary of forthcoming events     

《水文科学杂志》2013,58(3):502-506

Abstract

Currently, environmental modelling is frequently conducted with the aid of artificial neural networks (ANNs) in an effort to achieve greater accuracy in simulation and forecasting beyond that typically obtained when using solely linear models. For the design of an ANN, modellers must contend with two key issues: (a) the selection of model input and (b) the determination of the number of hidden neurons. A novel approach is introduced to address the optimal design of ANNs based on a multi-objective strategy that enables the user to find a set of feasible ANNs, determined as optimal trade-off solutions between model simplicity and accuracy. This is achieved in a multi-objective fashion by simultaneously minimizing three different cost functions: the model input dimension, the hidden neuron number and the generalization error computed on a validation set of data. The multi-objective approach is based on the Pareto dominance criterion and an evolutionary strategy has been employed to solve the combinatorial optimization problem. From a theoretical perspective, the choice of a multi-objective approach marks an attempt to account for, and overcome, the “curse of dimensionality” and to circumvent the drawbacks of “overfitting” that are inherent in ANNs. Moreover, it is demonstrated that the strategy renders the choice of the ANN more robust, as is evident by “unseen data” in the testing stage, since structure determination is not merely based on the statistical evaluation of the generalization performance. The methodology is tested and the results are reported in a case study relating groundwater level predictions to total monthly rainfall.  相似文献   

A Review of: “Remote Sensing of Soils and Vegetation in the USSR”. By P. J. CURRAN,G. M. FOODY,K. YA. KONDRATYEV,V. V. KOZODEROV and P. P. FEDCHENKO. (London: Taylor & Francis, 1990.) [Pp. 200.] Price £30·00. ISBN 085066 4020.     

PAUL MATHER 《International journal of geographical information science》2013,27(2):258-259

As they increase in popularity, social media are regarded as important sources of information on geographical phenomena. Studies have also shown that people rely on social media to communicate during disasters and emergency situation, and that the exchanged messages can be used to get an insight into the situation. Spatial data mining techniques are one way to extract relevant information from social media. In this article, our aim is to contribute to this field by investigating how graph clustering can be applied to support the detection of geo-located communities in Twitter in disaster situations. For this purpose, we have enhanced the fast-greedy optimization of modularity (FGM) clustering algorithm with semantic similarity so that it can deal with the complex social graphs extracted from Twitter. Then, we have coupled the enhanced FGM with the varied density-based spatial clustering of applications with noise spatial clustering algorithm to obtain spatial clusters at different temporal snapshots. The method was experimented with a case study on typhoon Haiyan in the Philippines, and Twitter’s different interaction modes were compared to create the graph of users and to detect communities. The experiments show that communities that are relevant to identify areas where disaster-related incidents were reported can be extracted, and that the enhanced algorithm outperforms the generic one in this task.  相似文献   

Seeking the Pareto front for multiobjective spatial optimization problems     

B. Huang  P. Fery  L. Xue  Y. Wang 《International journal of geographical information science》2013,27(5):507-526

Spatial optimization problems, such as route selection, usually involve multiple, conflicting objectives relevant to locations. An ideal approach to solving such multiobjective optimization problems (MOPs) is to find an evenly distributed set of Pareto‐optimal alternatives, which is capable of representing the possible trade‐off among different objectives. However, these MOPs are commonly solved by combining the multiple objectives into a parametric scalar objective, in the form of a weighted sum function. It has been found that this method fails to produce a set of well spread solutions by disregarding the concave part of the Pareto front. In order to overcome this ill‐behaved nature, a novel adaptive approach has been proposed in this paper. This approach seeks to provide an unbiased approximation of the Pareto front by tuning the search direction in the objective space according to the largest unexplored region until a set of well‐distributed solutions is reached. To validate the proposed methodology, a case study on multiobjective routing has been performed using the Singapore road network with the support of GIS. The experimental results confirm the effectiveness of the approach.  相似文献   

A bottom‐up approach to discover transition rules of cellular automata using ant intelligence     

Xiaoping Liu  Lin Liu  Jinqiang He  Bin Ai 《International journal of geographical information science》2013,27(11-12):1247-1269

This paper presents a new method to discover transition rules of geographical cellular automata (CA) based on a bottom‐up approach, ant colony optimization (ACO). CA are capable of simulating the evolution of complex geographical phenomena. The core of a CA model is how to define transition rules so that realistic patterns can be simulated using empirical data. Transition rules are often defined by using mathematical equations, which do not provide easily understandable explicit forms. Furthermore, it is very difficult, if not impossible, to specify equation‐based transition rules for reflecting complex geographical processes. This paper presents a method of using ant intelligence to discover explicit transition rules of urban CA to overcome these limitations. This ‘bottom‐up’ ACO approach for achieving complex task through cooperation and interaction of ants is effective for capturing complex relationships between spatial variables and urban dynamics. A discretization technique is proposed to deal with continuous spatial variables for discovering transition rules hidden in large datasets. The ACO–CA model has been used to simulate rural–urban land conversions in Guangzhou, Guangdong, China. Preliminary results suggest that this ACO–CA method can have a better performance than the decision‐tree CA method.  相似文献   

Spatial multi-objective land use optimization: extensions to the non-dominated sorting genetic algorithm-II     

Kai Cao  Michael Batty  Bo Huang  Yan Liu  Le Yu  Jiongfeng Chen 《International journal of geographical information science》2013,27(12):1949-1969

A spatial multi-objective land use optimization model defined by the acronym ‘NSGA-II-MOLU’ or the ‘non-dominated sorting genetic algorithm-II for multi-objective optimization of land use’ is proposed for searching for optimal land use scenarios which embrace multiple objectives and constraints extracted from the requirements of users, as well as providing support to the land use planning process. In this application, we took the MOLU model which was initially developed to integrate multiple objectives and coupled this with a revised version of the genetic algorithm NSGA-II which is based on specific crossover and mutation operators. The resulting NSGA-II-MOLU model is able to offer the possibility of efficiently searching over tens of thousands of solutions for trade-off sets which define non-dominated plans on the classical Pareto frontier. In this application, we chose the example of Tongzhou New Town, China, to demonstrate how the model could be employed to meet three conflicting objectives based on minimizing conversion costs, maximizing accessibility, and maximizing compatibilities between land uses. Our case study clearly shows the ability of the model to generate diversified land use planning scenarios which form the core of a land use planning support system. It also demonstrates the potential of the model to consider more complicated spatial objectives and variables with open-ended characteristics. The breakthroughs in spatial optimization that this model provides lead directly to other properties of the process in which further efficiencies in the process of optimization, more vivid visualizations, and more interactive planning support are possible. These form directions for future research.  相似文献   

A hybrid optimization approach for efficient calibration of computationally intensive hydrological models     

Pierre-Luc Huot  A. Poulin  C. Audet  S. Alarie 《水文科学杂志》2013,58(10):1204-1222

ABSTRACT

The calibration of hydrological models is formulated as a blackbox optimization problem where the only information available is the objective function value. Distributed hydrological models are generally computationally intensive, and their calibration may require several hours or days which can be an issue for many operational contexts. Different optimization algorithms have been developed over the years and exhibit different strengths when applied to the calibration of computationally intensive hydrological models. This paper shows how the dynamically dimensioned search (DDS) and the mesh adaptive direct search (MADS) algorithms can be combined to significantly reduce the computational time of calibrating distributed hydrological models while ensuring robustness and stability regarding the final objective function values. Five transitional features are described to adequately merge both algorithms. The hybrid approach is applied to the distributed and computationally intensive HYDROTEL model on three different river basins located in Québec (Canada).  相似文献   

钢筋混凝土框架结构基于性能和可靠度的抗震优化设计     

郭彤  顾羽 《地震工程与工程振动》2013,33(4):109-118

基于可靠度理论的概率极限状态设计法是结构设计的基本法则之一,而新一代"基于性能的地震工程"要求在性能化设计中尚应考虑随机因素的影响。为获得满足预设性能水准和可靠度指标的最优方案,以钢筋混凝土框架结构为例,根据其在不同性能水准下的位移需求,采用非线性随机有限元方法求解结构的抗震可靠度,并将可靠度指标作为约束条件,以总造价为优化目标,提出了一种基于性能和可靠度的抗震优化设计方法。其中,可靠度计算以OpenSees为平台,并采用基于梯度分析的FORM有限元法。优化分析以MATLAB为平台,通过程序调用,实现了与可靠度分析之间的数据通讯。算例分析表明,模拟退火算法在本问题中较遗传算法具有更高全局搜索能力和计算精度。研究成果可为新一代基于性能和可靠度的优化设计提供参考。  相似文献   

Damper placement optimization in a shear building model with discrete design variables: a mixed‐integer second‐order cone programming approach     

Yoshihiro Kanno 《地震工程与结构动力学》2013,42(11):1657-1676

Supplemental damping is known as an efficient and practical means to improve seismic response of building structures. Presented in this paper is a mixed‐integer programming approach to find the optimal placement of supplemental dampers in a given shear building model. The damping coefficients of dampers are treated as discrete design variables. It is shown that a minimization problem of the sum of the transfer function amplitudes of the interstory drifts can be formulated as a mixed‐integer second‐order cone programming problem. The global optimal solution of the optimization problem is then found by using a solver based on a branch‐and‐cut algorithm. Two numerical examples in literature are solved with discrete design variables. In one of these examples, the proposed method finds a better solution than an existing method in literature developed for the continuous optimal damper placement problem. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

浅析青海地震信息网络优化改造     

马晓兰  孙春玲  白占孝 《高原地震》2013,(4):51-53

介绍了青海地震信息网络在原有网络基础上进行优化改造的情况,详细阐述了优化改造的具体内容,以及优化改造工作对青海省地震局网络产生的作用。  相似文献