Application of Polynomial Neural Network In Assessment of Water Vulnerability in Rubber Industries Of Tripura
Ramprasad Ghosh* and Mrinmoy Majumder**
* Research Scholar of Civil Engg Dept, NIT Agartala.
** Associate Professor of Civil Engg Dept, NIT Agartala.
Rubber (Hevea brasiliensis), known to do best in tropical climates, grows well in this nation. Natural rubber is the most important cash crop in Tripura. It is recognised as one of the nation's key crops, supplying employment and revenue to smallholders, estate workers, and their families. Meteorological conditions are one of the factors that can affect rubber yield and productivity. In recent years, climate change has become a major threat and has been widely documented in the geographic distribution of many plant species. This paper analyses the impacts of climate change in the rubber production of Tripura based on three important factors as temperature, soil moisture and water availability. In this regard, a model has been developed using the Group Method of Data Handling (GMDH) to analyse the climate change impacts on rubber production of Tripura. The data has been collected from different sources in all over Tripura for Achievement of Rubber production w.r.t Area. In the meantime, Water availability data has also been collected from PWD (WR) Tripura and the unknown data has been predicted as well as determined by GMDH. During data collection, various knowledge has been gathered related to harvesting, watering and marketing. Our aim for this project is to predict whether current water availability can satisfy the water demand for rubber or not.