Predicting Rainfall using Machine Learning Techniques
This work addresses rainfall forecasting for society to reduce losses, but it is incremental as it applies existing methods to new data without novel breakthroughs.
The study tackled rainfall prediction for Australian cities using machine learning techniques, comparing methods and pre-processing to predict rain the next day, with results showing various evaluation metrics but no specific numbers provided.
Rainfall prediction is one of the challenging and uncertain tasks which has a significant impact on human society. Timely and accurate predictions can help to proactively reduce human and financial loss. This study presents a set of experiments which involve the use of prevalent machine learning techniques to build models to predict whether it is going to rain tomorrow or not based on weather data for that particular day in major cities of Australia. This comparative study is conducted concentrating on three aspects: modeling inputs, modeling methods, and pre-processing techniques. The results provide a comparison of various evaluation metrics of these machine learning techniques and their reliability to predict the rainfall by analyzing the weather data.