Key Laboratory of the Ministry of Land and Resources for Construction Land Transformation

Guangzhou, China

Key Laboratory of the Ministry of Land and Resources for Construction Land Transformation

Guangzhou, China

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Zhao Y.,South China Agricultural University | Zhao Y.,Guangdong Province Key Laboratory of Land Use and Consolidation | Zhao Y.,Key Laboratory of the Ministry of Land and Resources for Construction Land Transformation | Cheng J.,South China Agricultural University | And 6 more authors.
Xitong Fangzhen Xuebao / Journal of System Simulation | Year: 2015

A parallel agent-based model of Von Thünen Model was proposed driven by graphics processing units (GPUs). The Von Thünen Model often involved the simulation of large numbers of geographically located individual decision-makers and a massive number of individual-level interactions. This simulation required substantial computational power. GPU-enabled computing resources provided a massively parallel processing platform based on a fine-grained shared memory paradigm. This massively parallel processing platform held considerable promise for meeting the computing requirement of agent-based models of spatial problems. A dynamic relationship table rebuilding method was proposed to enable the use of GPUs for parallel agent-based modeling of the spatial Von Thünen Model. The key algorithm played an important role in best exploiting high-performance resources in GPUs for large-scale spatial simulation. Experiments conducted to examine computing performance show that GPUs provide a computationally efficient alternative to traditional parallel computing architectures and substantially accelerate agent-based models in large-scale spatial space. ©, 2015, Chinese Association for System Simulation. All right reserved.


Liu G.,South China Agricultural University | Liu G.,Guangdong Province Key Laboratory of Land Use and Consolidation | Liu G.,Key Laboratory of the Ministry of Land and Resources for Construction Land Transformation | Wang H.,South China Agricultural University | And 7 more authors.
Land Use Policy | Year: 2016

Since the late 1990s, massive rural out-migration has had a significant impact on the utilization of cultivated land. However, the links between rural out-migration and arable land use intensity remain ambiguous. This paper expands the current theories on the relationship between migration flows and land use intensification and explains the underlying mechanism that arable land use intensity is fluctuating upward with the increase of rural out-migration. Panel data of 5 mountain cities in Guangdong Province from 1996 to 2012 is used to empirically examine the impact of rural out-migration on arable land use intensity and analyze the influence of rural out-migration income, land scale, GDP per capita and multiple crop index on arable land use intensity. Our results show that there is an inverted N-shaped relationship between rural out-migration and arable land use intensity. The positive effect of the productivity increase caused by the Household Responsibility System (HRS) is the main reason for the increase of arable land use intensity. Labor scarcity that results from the excessive emigration of rural labors leads to a decline in arable land use intensity. The added fertilizer and pesticide inputs or changes in crop type can ultimately compensate for the negative effect of labor scarcity and promote the improvement of arable land use intensity. In addition, over-intensive use of farmland has negative impacts on the ecological environment and national food security. Based on the empirical results of this paper, some policy recommendations are suggested, such as the transformation of the agricultural development mode for a less demand in rural labor, increasing the inputs of agricultural technique and capital instead of labor, raising the comparative benefit of agriculture to attract young rural labor for farming, cultivation of professional farmers, establishment of an agricultural supporting system and developing circulative agriculture. © 2016 Elsevier Ltd


Wang J.,South China Agricultural University | Wang J.,Guangdong Province Key Laboratory of Land Use and Consolidation | Wang J.,Key Laboratory of the Ministry of Land and Resources for Construction Land Transformation | Ge A.,South China Agricultural University | And 10 more authors.
Solid Earth | Year: 2015

Traditionally, potential evaluation methods for farmland consolidation have depended mainly on the experts' experiences, statistical computations or subjective adjustments. Some biases usually exist in the results. Thus, computer-aided technology has become essential. In this study, an intelligent evaluation system based on a fuzzy decision tree was established, and this system can deal with numerical data, discrete data and symbolic data. When the original land data are input, the level of potential of the agricultural land for development will be output by this new model. The provision of objective proof for decision-making by authorities in rural management is helpful. Agricultural land data characteristically comprise large volumes, complex varieties and more indexes. In land consolidation, it is very important to construct an effective index system. A group of indexes need to be selected for land consolidation. In this article, a fuzzy measure was adopted to accomplish the selection of specific features. A fuzzy integral based on a fuzzy measure is a type of fusion tool. The optimal solution with the fewest non-zero elements was obtained for the fuzzy measure by solving a fuzzy integral. This algorithm provides a quick and optimal way to identify the land-index system when preparing to conduct land consolidation. This new research was applied to Shunde's "Three Old" consolidation project which provides the data. Our estimation system was compared with a conventional evaluation system that is still accepted by the public. Our results prove to be consistent, and the new model is more automatic and intelligent. The results of this estimation system are significant for informing decision-making in land consolidation. © Author(s) 2015.


Zhao Y.,South China Agricultural University | Zhao Y.,Guangdong Province Key Laboratory of Land Use and Consolidation | Zhao Y.,Key Laboratory of the Ministry of Land and Resources for Construction Land Transformation | Zhang X.,Sun Yat Sen University | And 7 more authors.
Journal of Information and Computational Science | Year: 2014

Because Cellular Automata (CA) is a dynamic system with inherent parallelism, many studies are focused on mapping CA to the parallel system in order to obtain high performance computing capability, such as using clusters, supercomputers and networks of computers. But the application of these systems are too expensive and difficult to use on the occasions which need convenient computing. Recent developments in the General Purpose GPU can meet the desktop computing challenge, which have high performance at low cost. This paper presents a general-purpose approach to accelerate the CA in geographical domain in case of spatial simulation. A series of experiments are launched to test the performance of proposed method. Finally, the experimental results indicate the approach in this paper can obtain high performance and computational performance data using GPU based accelerating method are fifty to sixty times faster than the identical algorithms using CPU in test environment. It is worthy of (1) reducing the communication cost between GPU and CPU is crucial when visualization and processing are equally important in real time simulation and (2) improving the parallel capability in the CA functions is essential using GPU Programming. © 2014 Binary Information Press


Zhao Y.,South China Agricultural University | Zhao Y.,Guangdong Province Key Laboratory of Land Use and Consolidation | Zhao Y.,Key Laboratory of the Ministry of Land and Resources for Construction Land Transformation | Hu X.,South China Agricultural University | And 7 more authors.
Journal of Information and Computational Science | Year: 2014

This paper presents a site selection model of Land Consolidation Project (LCP) using spatial Ant Colony Optimization (ACO) techniques. Generally, it is straightforward to use Equal Interval, Quartile, Natural Breaks and K-Means Cluster method for selecting the site of LCPs. However, the traditional analysis methods have difficulty in solving ideal solution problems because of complex spatial search, and ACO provides a useful tool for site selection. In this study, the integration of ACO with multi-way tree and GIS is proposed to solve site selection problems of LCPs. An evaluating system including eco-socio-economic and fitness function has also been established so that the model can find the suitable site which has contiguous space and high land suitability evaluation index. This model has been applied to the site selection of LCPs in Huazhou, Guangdong Province, China. The experiment showed that the proposed model had better performance in the site selection of strong advantage in land consolidation project. © 2014 Binary Information Press


Zeng Q.,South China Agricultural University | Zhu T.,South China Agricultural University | Zhuang X.,South China Agricultural University | Zheng M.,South China Agricultural University | And 3 more authors.
Multimedia Tools and Applications | Year: 2015

Plant species identification is one of the most important research branches of botanical science. In this paper, a novel shape descriptor, namely Periodic Wavelet Descriptor (PWD) of plant leaf, is firstly presented. Then based on the PWDs of the leaves of different plant species, we constructed a database of PWDs. At last, a Back Propagation Neural Network (BPNN) is trained to fulfill the experiment of plant species identification. The experimental results show that the proposed algorithm combined the PWD of plant leaf with BPNN is effective with a correct identification rate about 90 %. © 2015 Springer Science+Business Media New York


Wang J.,South China Agricultural University | Ge A.,Key Laboratory of the Ministry of Land and Resources for Construction Land Transformation | Li C.,Key Laboratory of the Ministry of Land and Resources for Construction Land Transformation | Wang W.,South China Agricultural University | Hu Y.,Key Laboratory of the Ministry of Land and Resources for Construction Land Transformation
2015 5th International Workshop on Computer Science and Engineering: Information Processing and Control Engineering, WCSE 2015-IPCE | Year: 2015

The traditional evaluation methods for land consolidation potential mainly depend on the experts' experience, statistics computations or subjective adjustments. There usually exists some bias in the results. So, the computer technology has been essential. In this study, an intelligent evaluation system based on Fuzzy Decision Tree is established, which can be deal with numerical data, discrete data and symbol data. When the land original data is input, its potential to be developed will be output by this new model. It is more helpful for authority to give out the objective proof for decision making. The project of Shunde's land consolidation provides the data support. The conventional evaluation results are compared. They are consistent roughly. The new model is more automatic and intelligent.


Wang J.,South China Agricultural University | Hu Y.,Key Laboratory of the Ministry of Land and Resources for Construction Land Transformation | Ren X.,Key Laboratory of the Ministry of Land and Resources for Construction Land Transformation | Wang W.,South China Agricultural University
2015 5th International Workshop on Computer Science and Engineering: Information Processing and Control Engineering, WCSE 2015-IPCE | Year: 2015

In land consolidation, it is very important to construct an effective index system. Land data is characteristic of big volume, complex varieties and more indexes. We need select a group of good index for some goals of land consolidation according to concrete demand. In this paper, Fuzzy Integrals is adopted to finish the feature selection. Fuzzy Integral is a kind of infusion tool based on fuzzy measure which can describe the importance of each feature or feature subset. Some researchers can obtain the optimal solution for Fuzzy measure using soft computing tools. When the Fuzzy integrals can be transformed to a linear equation, L1-norm regularization method is applied to solve the linear equation system and find a solution with the fewest nonzero values for fuzzy measure. The solution with the fewest nonzero can show the degree of contribution of some features or their combinations for decision. This method provides a quick and optimal way to determine the land index system for preparing the following land research.

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