Using deep-belief networks to understand propensity for livelihood change in a rural coastal community to further conservation

Conservation Biology : the Journal of the Society for Conservation Biology
Alfonso B LabaoHelen T Yap

Abstract

Overharvesting of terrestrial and marine resources may be alleviated by encouraging an alternative configuration of livelihoods, particularly in rural communities in developing countries. Typical occupations in such areas include fishing and farming, and rural households often switch livelihood activities to suit climate and economic conditions. We used a machine-learning tool, deep-belief networks (DBN), and data from surveys of a rural Philippine coastal community to examine household desire to change livelihood. This desire is affected by a variety of factors, such as income, family needs, and feelings of work satisfaction, that are interrelated in complex ways. In farming households, livelihood changes often occur to diversify resources, increase income, and lessen economic risk. The DBN, given its multilayer perceptron structure, has a capacity to model nonlinear relationships among factors while providing an acceptable degree of accuracy. Relative to a set of 34 features (e.g., education, boat ownership, and work satisfaction), we examined the binary response variables desire to change work or not to change work. The best network had a test set accuracy of 97.5%. Among the features, 7 significantly affected desire to shif...Continue Reading

References

Jun 13, 2006·Neural Computation·Geoffrey E HintonYee-Whye Teh
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Aug 1, 2009·Science·Boris WormDirk Zeller
Oct 23, 2014·Nature·Cameron M PittelkowChris van Kessel
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