Rainfall Prediction using kNN and Decision Tree

Abstract

Rainfall forecasting is extremely important in a variety of situations and contexts. By implementing good security precautions in advance, it is possible to significantly limit the consequences of unexpected and excessive rains. Accurate rainfall forecasts have become more difficult than ever before due to climatic changes. Data mining algorithms can forecast rainfall by identifying hidden patterns in meteorological variables from previous data. This study contributes by investigating the application of two data mining approaches for rainfall prediction in the city of Austin. k Nearest Neighbour (kNN) and Decision Trees are some of the techniques used. The dataset comes from a weather forecasting service and includes numerous atmospheric parameters. The pre-processing approach, which includes cleaning and normalising operations, is utilised for successful prediction. The performance of data mining algorithms are evaluated in terms of accuracy, recall, and f-measure with varied training/test data ratios. The future year's rainfall is estimated using the Decision Tree and kNN machine learning algorithms and compare the results obtained by each approach.