Rain Fall Weather Reports in Java

Precipitation or rainfall prediction is an important practical problem in meteorology research. However, a reliable forecast is difficult to achieve due to the complexity of the climate system and the huge set of available climatic data at nearby sites. Thus, in most existing precipitation prediction systems or methods, inevitable partial (selective) consideration of the types of climatic data has often led to less satisfactory prediction results. Thanks to the advent of modern sensor technology, various types of climatic data in time-series forms can be collected at observation sites or satellites. In this work, we reconstruct a climate dataset including more than 30 climatic variables measured by more than 103,473 observation sites covering the world surface from 1800 to 2017. Then, we apply state-of-the-art machine learning methods, including deep learning (CNN, RNN, and LSTM networks) and ensemble learning (Adaboost, GBDT, and XGBoost), to develop a short-term precipitation system. Experiments on real-world data show that incorporating multiple climate variables into a prediction system improves the prediction results. The best performance of the proposed method reaches an accuracy of more than 80%.