Prediction of Rainfall Using Data Mining Techniques

Abstract

The research for this paper focus on finding inter-relations between various climate ratio and predict rainfall therefore. In addition, since precipitation is the detectable explanation for flood, our evaluation can help immensely in anticipating flood and masterminding a fitting risk the official's structure. Being a riverine country, Flood happens in Bangladesh pretty much every other year. Anticipating flood precisely can help us in working up our economy. Our assessment shows how the climate parameters (SOFI EI Nino) are obligated for critical precipitation in Bangladesh. In spite of the fact that numerous different inquiries about on foreseeing precipitation have been directed utilizing other climate variables, the southern swaying record and the EI nino 3.4 shows. For working up a relationship among precipitation, SOI and El Nino, we have applied Data Mining strategy. The particular information calculations that we have executed in our paper are K- grouping. Decision tree and Regression model. The yields of these calculations give us a clear connection among precipitation and the information parameters. Executing our strategy on the dataset of precipitation for the recent years, our evaluated precipitation is nearly equivalent to the real ones of those years. In this way, in organizing a potential gauge model for Bangladesh, our work can accept a basic activity as a result of its high effectiveness. Foe working up a relationship among precipitation, SOI and Nino, we have applied Data mining strategy.