A Bayesian network approach to model farmers' crop choice using socio-psychological measurements of expected benefits of ecosystem services

2014 ◽  
Vol 57 ◽  
pp. 227-234 ◽  
Author(s):  
Patrick Poppenborg ◽  
Thomas Koellner
Keyword(s):  
Ecosystem Services ◽  
Bayesian Network ◽  
Network Approach ◽  
Crop Choice ◽  
Expected Benefits

scholarly journals Construction of the Ecological Security Pattern of Urban Agglomeration under the Framework of Supply and Demand of Ecosystem Services Using Bayesian Network Machine Learning: Case Study of the Changsha–Zhuzhou–Xiangtan Urban Agglomeration, China

2019 ◽  
Vol 11 (22) ◽  
pp. 6416 ◽  
Author(s):  
Ouyang ◽  
Wang ◽  
Zhu
Keyword(s):  
Machine Learning ◽  
Ecosystem Services ◽  
Bayesian Network ◽  
Supply And Demand ◽  
Urban Agglomeration ◽  
Ecological Security ◽  
Ecological Strategy ◽  
Ecological Corridors ◽  
Ecological Corridor ◽  
Security Pattern

Coordinating ecosystem service supply and demand equilibrium and utilizing machine learning to dynamically construct an ecological security pattern (ESP) can help better understand the impact of urban development on ecological processes, which can be used as a theoretical reference in coupling economic growth and environmental protection. Here, the ESP of the Changsha–Zhuzhou–Xiangtan urban agglomeration was constructed, which made use of the Bayesian network model to dynamically identify the ecological sources. The ecological corridor and ecological strategy points were identified using the minimum cumulative resistance model and circuit theory. The ESP was constructed by combining seven ecological sources, “two horizontal and three vertical” ecological corridors, and 37 ecological strategy points. Our results found spatial decoupling between the supply and demand of ecosystem services (ES) and the degradation in areas with high demand for ES. The ecological sources and ecological corridors of the urban agglomeration were mainly situated in forestlands and water areas. The terrestrial ecological corridor was distributed along the outer periphery of the urban agglomeration, while the aquatic ecological corridor ran from north to south throughout the entire region. The ecological strategic points were mainly concentrated along the boundaries of the built-up area and the intersection between construction land and ecological land. Finally, the ecological sources were found primarily on existing ecological protection zones, which supports the usefulness of machine learning in predicting ecological sources and may provide new insights in developing urban ESP.

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