Abstract
Climatic uncertainty due to global warming and continuous deforestation as a result of urbanization has contributed to the degradation of regions once ecologically rich but now becoming arid or desert. In various studies and governmental reports it has been proposed that places that have been desertified or are prone to desertification can be saved and returned their original state if artificial reforestation is undertaken with selected species. Although afforestation has been successful in many desertified regions of the world (India, Philippines, Australia), in many places it has come to represent a waste of money and the environment. This failure has been attributed to the selection of plants of poor quality, poor plant handling, out-of-season planting, insects, and other operational and climatic factors. Thus, selection of ideal plant species is important for the success of an afforestation project. In this study a methodology for the selection of species for afforestation is proposed with the help of neuro-fuzzy techniques. The novelty of the study lies in its attempt to apply neuro-fuzzy techniques in the selection of afforestation species, thus making the selection free of bias and prejudices. By selection of species we mean the suitability of a species that can be used in afforestation projects with no chance of failure.
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Majumder, M., Chackraborty, T., Datta, S., Chakraborty, R., Barman, R.N. (2013). Development of a Neuro-Fuzzy System for Selection of Tree Species for Afforestation Purpose. In: Majumder, M., Barman, R. (eds) Application of Nature Based Algorithm in Natural Resource Management. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5152-1_20
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DOI: https://doi.org/10.1007/978-94-007-5152-1_20
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