scholarly journals Modeling the Land Use Change in an Arid Oasis Constrained by Water Resources and Environmental Policy Change Using Cellular Automata Models

2018 ◽  
Vol 10 (8) ◽  
pp. 2878 ◽  
Author(s):  
Xiaoli Hu ◽  
Xin Li ◽  
Ling Lu
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Artificial Neural Network ◽  
Sustainable Development ◽  
Environmental Policy ◽  
Water Resources ◽  
Cellular Automata ◽  
Policy Change ◽  
Arid Regions ◽  
Artificial Neural

Land use and land cover change (LUCC) is an important issue in global environmental change and sustainable development, yet spatial simulation of LUCC remains challenging due to the land use system complexity. The cellular automata (CA) model plays a crucial role in simulating LUCC processes due to its powerful spatial computing power; however, the majority of current LUCC CA models are binary-state models that cannot provide more general information about the overall spatial pattern of LUCC. Moreover, the current LUCC CA models rarely consider background artificial irrigation in arid regions. Here, a multiple logistic-regression-based Markov cellular automata (MLRMCA) model and a multiple artificial-neural-network-based Markov cellular automata (MANNMCA) model were developed and applied to simulate complex land use evolutionary processes in an arid region oasis (Zhangye Oasis), constrained by water resources and environmental policy change, during the period 2000–2011. Results indicated that the MANNMCA model was superior to the MLRMCA model in simulated accuracy. Furthermore, combining the artificial neural network with CA more effectively captured the complex relationships between LUCC and a set of spatial driving variables. Although the MLRMCA model also showed some advantages, the MANNMCA model was more appropriate for simulating complex land use dynamics. The two integrated models were reliable, and could reflect the spatial evolution of regional LUCC. These models also have potential implications for land use planning and sustainable development in arid regions.

scholarly journals GIS Cellular Automata Using Artificial Neural Network for Land Use Change Simulation of Lagos, Nigeria

2012 ◽  
Vol 4 (2) ◽  
Author(s):  
Onuwa Okwuashi ◽  
Mfon Isong ◽  
Etim Eyo ◽  
Aniekan Eyoh ◽  
Okey Nwanekezie ◽  
...  
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Artificial Neural Network ◽  
Cellular Automata ◽  
Land Use Change ◽  
Artificial Neural

Using an artificial neural network for estimating sustainable development goals index

2020 ◽  
Vol 31 (4) ◽  
pp. 1023-1037 ◽  
Author(s):  
Seyed-Hadi Mirghaderi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Sustainable Development ◽  
Artificial Neural Networks ◽  
Sustainable Development Goals ◽  
Content Type ◽  
The Sustainable Development ◽  
Development Goals ◽  
Artificial Neural

PurposeThis paper aims to develop a simple model for estimating sustainable development goals index using the capabilities of artificial neural networks.Design/methodology/approachSustainable development has three pillars, including social, economic and environmental pillars. Three clusters corresponding to the three pillars were created by extracting sub-indices of three 2018 global reports and performing cluster analysis on the correlation matrix of sub-indices. By setting the sustainable development goals index as the target variable and selecting one indicator from each cluster as input variables, 20 artificial neural networks were run 30 times.FindingsArtificial neural networks with seven nodes in one hidden layer can estimate sustainable development goals index by using just three inputs, including ecosystem vitality, human capital and gross national income per capita. There is an excellent similarity (>95%) between the results of the artificial neural network and the sustainable development goals index.Practical implicationsInstead of calculating 232 indicators for determining the value of sustainable development goals index, it is possible to use only three sub-indices, but missing 5% of precision, by using the proposed artificial neural network model.Originality/valueThe study provides additional information on the estimating of sustainable development and proposes a new simple method for estimating the sustainable development goals index. It just uses three sub-indices, which can be retrieved from three global reports.

Application of Neural Network in Urban Land Use Suitability Evaluation

2011 ◽  
Vol 474-476 ◽  
pp. 681-686
Author(s):  
Xiao Rui Zhang ◽  
Gang Chen
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Urban Land ◽  
Urban Land Use ◽  
Suitability Evaluation ◽  
Land Use Suitability ◽  
Artificial Neural

Urban land use suitability evaluation is the basic work of urban land use planning and management. The evaluation method is a core in urban land use suitability evaluation. Traditional urban land use suitability evaluation methods are GIS-based methods which often can not get satisfactory results for the complex nonlinear urban land use system. Artificial neural network is a frontier theory of complex non-linearity scientific and artificial intelligence science. It is a new method to evaluate urban land use suitability. This paper took the land use suitability evaluation of Hefei city as an example, building a back propagation neural network with 8 neurous of input layer, 5 neurons of hide layer and 3 neurons of output layer. The analysis shows: the high suitability area is 682.27 km2in Hefei city, being about 8.73% of the total study area; the middle suitability area is 5965.76 km2, or about 76.33% of the total area and the low suitability area is 1167.35 km2, or about 14.94% of the total area. The results reflect the actual situation in Hefei city. The study shows that the back propagation neural network model can overcome the shortcomings of traditional evaluation methods. It means that artificial neural network is suitable for urban land use suitability evaluation. This reflects that artificial neural network has great academic value and application prospect in urban land use suitability evaluation. It also reflects that this study can provide a new idea and method for urban land use suitability evaluation.

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