Rainfall Modeling using a Machine Learning Approach as Support for the Early Warning System for Flood Disasters in Malang Regency, Indonesia

Malang is one of the areas in East Java - Indonesia that has the potential to experience flooding caused by high rainfall. Floods have a negative impact on both agriculture and public health. Therefore, it is necessary to create a rainfall prediction model in Malang that supports the creation of an early warning system for flood disasters. In this research, a machine learning approach was used, namely the Long Short Term Memory model, to model monthly rainfall at three stations in Malang Regency, namely Abd. Saleh Airbase Station, Karangkates Station and Karangploso Station. Several inputs were tried, namely rainfall lag from the station itself and from neighboring stations as well as other weather variables, namely air humidity, air pressure and air temperature. The results obtained from this research are the best LSTM model for Abd. Saleh Airbase Station rainfall in is similar to the best LSTM model for Karangploso Station rainfall, namely a LSTM model with input rainfall lag 6 for the station itself. Meanwhile, the best model for Karangkates Station rainfall is a model with input rainfall lag 1, lag 4 and lag 6 from the three stations.. KEYWORDS :Machine learning, Long short term memory, Monthly rainfall, Modeling.