Early warning of illegal development for protected areas by integrating cellular automata with neural networks.
Ecological security has become a major issue under fast urbanization in China. As the first two cities in this country, Shenzhen and Dongguan issued the ordinance of Eco-designated Line of Control (ELC) to "wire" ecologically important areas for strict protection in 2005 and 2009 respectively. Early warning systems (EWS) are a useful tool for assisting the implementation ELC. In this study, a multi-model approach is proposed for the early warning of illegal development by integrating cellular automata (CA) and artificial neural networks (ANN). The objective is to prevent the ecological risks or catastrophe caused by such development at an early stage. The integrated model is calibrated by using the empirical information from both remote sensing and handheld GPS (global positioning systems). The MAR indicator which is the ratio of missing alarms to all the warnings is proposed for better assessment of the model performance. It is found that the fast urban development has caused significant threats to natural-area protection in the study area. The integration of CA, ANN and GPS provides a powerful tool for describing and predicting illegal development which is in highly non-linear and fragmented forms. The comparison shows that this multi-model approach has much better performances than the single-model approach for the early warning. Compared with the single models of CA and ANN, this integrated multi-model can improve the value of MAR by 65.48% and 5.17% respectively.
Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, 135 West Xingang RD., Guangzhou 510275, PR China. Electronic address: lixia@mail.sysu.edu.cn.
Xia
X
Li
Chunhua
C
Lao
Yilun
Y
Liu
Xiaoping
X
Liu
Yimin
Y
Chen
Shaoying
S
Li
Bing
B
Ai
Zijian
Z
He
D002681:China; D003247:Conservation of Natural Resources; D057217:Economic Development; D040362:Geographic Information Systems; D008962:Models, Theoretical; D016571:Neural Networks, Computer
Cellular automata; Early warning systems; Geographical information systems; Illegal development; Neural networks