Prediction of Rainfall Using Machine Learning Techniques

Machine Learning algorithms are mostly useful in predicting rainfall. Some of the major Machine Learning algorithms used in predicting rainfall are DFR- Decision Forest Regression, BDTR- Boosted Decision Tree Regression, NNR- Neural Network Regression, and BLR-Bayesian Linear Regression.

BDTR- Boosted Decision Tree Regression

A BDTR is a classic method to create an ensemble of regression trees where each tree is dependent on the prior tree [2]. In ensemble learning methods, the second tree rectifies the errors of the primary tree, the errors of the primary and second trees are corrected by the third tree, and so on. Predictions are made using the entire set of trees used to create the forecast. The BDTR is particularly effective at dealing with tabular data. The advantages of BDTR are it is robust to missing data and normally allocates feature significance scores.

Bayesian Linear Regression (BLR)

The Bayesian technique employs Bayesian inference [1]. To obtain parameter estimates, prior parameter information must be paired with a probability function. The forecast distribution utilises probabilities by current belief about w given data to assess the likelihood of a value y given x for a specific w. (y, X).  Finally, add up all of the potential w [2] values. BLR uses a natural mechanism to allow insufficient or incorrectly dispersed data to survive. The main benefit is that, unlike traditional regression, Bayesian processing allows you to recover the complete spectrum of inferential solutions rather than just a single estimate and a confidence interval.