Supervised multilevel clustering for rainfall forecasting using meteorological data

Abstract

Atmospheric science focuses on weather processes and forecasting. Numerical and statistical analysis plays an important role in meteorological research. Meteorological data will be used to predict the changes in climatic patterns by using forecasting models and weather forecasting instruments. Data mining techniques have more scope to discover future weather patterns by analyzing past weather dimensions. In our study two techniques Multiple Linear Regression (MLR) and Expectation Maximization (EM) clustering algorithms are combined for rainfall forecasting. MLR interprets most important parameters of rainfall for clustering algorithm. EM clustering algorithm will find correctly and incorrectly clustered instances when applied on selected partitioned attributes. The model was able to forecast less rainfall, medium rainfall and high rainfall by analyzing past meteorological observations. Standard deviation is used as a measure of error correction to improve the cluster results. Data normalization helps to improve model performance. These findings are useful to determine future climate expectation.