Modeling of interactions among sustainability components of an agro-ecosystem using local knowledge through cognitive mapping and fuzzy inference system

被引:46
作者
Rajaram, T. [1 ]
Das, Ashutosh [1 ]
机构
[1] SASTRA Univ, Sch Civil Engn, CARE, Thanjavur 613402, India
关键词
Agricultural sustainability; Fuzzy inference; Cognitive mapping; Participatory; Local knowledge; Coupled human-natural system; EXPERT-SYSTEM; LAND-USE; INDICATORS; LOGIC;
D O I
10.1016/j.eswa.2009.07.035
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Understanding the way the ecological and social components interact in a settlement (a village, town. city) is the key to manage effectively the coupled human-natural system and start programs for its sustainability. This paper presents the utility of a fuzzy inference system to codify the interactions among the sustainability components for effective decision making. The method uses local knowledge elicited through a participatory methodology to understand the linkages among the ecological, social, physical and financial components of sustainability in a village Considering the complexity involved in the interactions among these components, efforts are underway to devise theories, methodologies and approaches to further our understanding This paper proposes a fuzzy rule based approach to model the interactions of sustainability components in an agro-ecosystem. The approach is explained with the example of a typical agrarian village in southern India. The proposed approach relies on cognitive mapping to elicit understanding of interactions based oil local ecological knowledge. The interactions captured in the form of cognitive maps are used to derive the rules, which govern the sustainability states of the components. Fuzzy inference system is used to simulate various scenarios and to study their impact on the sustainability of livelihoods Cognitive mapping and FIS promise to be an effective combination in building participatory rule based sustainability expert systems and merits further attention. (C) 2009 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1734 / 1744
页数:11
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