Application of Nature Based Algorithm in Natural Resource by Mrinmoy Majumder
By Mrinmoy Majumder
This publication highlights the appliance of nature-based algorithms in usual source administration. The booklet contains the methodologies to use what common plant life or fauna do to optimize their survival. a similar approach used to be used to optimize renewable power iteration from water assets, maximization of make the most of crop harvesting, wooded area source administration and decision-making experiences. those reports can be utilized for example for locating ideas of the opposite maximization or minimization difficulties that are universal in usual source management.
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Extra resources for Application of Nature Based Algorithm in Natural Resource Management
The use of the species as food or ornaments has made them vulnerable to poachers and wildlife smugglers. In certain places in South Asia, porcupines are artificially bred for their quills and meat. However, because of rapid-scale urbanization, suitable habitats of the species are constantly diminishing. Their dwindling habitat, along with the ways in which they are used, has made them increasingly vulnerable. The degradation of the species can be prevented if suitable habitats can be identified and the species is conserved in these selected locations.
In this case the food sources are compared with that region that could become a habitat for porcupines. The factors are compared with subsources of food. It can be thought of as a flowering with lots of flowers. All the flowers are a potential food source. If the nectar of the flowers attracts more bees than any other plant, then that plant is selected as the most suitable place for a porcupine population. According to the logic of food searching by bees, the worker bees at first randomly select a flower or a factor.
The absence of a large-scale data set have decreased the availability of reliable prediction models for solving such problems. The absence of a regular pattern in the related parameters within such short time domain has forced modelers to apply stochastic modeling to predict short-term rainfall patterns. Stochastic models have fewer requirements when it comes to data but are extremely vulnerable to uncertainty due to the unstable nature of the interrelationships of the variables. 1. 1 Earlier Studies on the Prediction of Short-Term Rainfall Short-term rainfall is a popular topic of research due to its importance in industry and the agricultural output of a country.