Climatological Rainfall Forecasting Using LSTM: An Analysis of Sequential Input and Data Window Input Approaches

This paper presents an analysis of two different approaches for forecasting rainfall using long short-term memory (LSTM) neural networks: sequential input and data window input. Rainfall prediction is crucial for water management, agriculture, and disaster management. However, it is a challenging task due to the complex and nonlinear nature of the precipitation process. The LSTM network has been widely used for rainfall prediction due to its ability to capture long-term dependencies in time series data. In this study, we trained and evaluated an LSTM network for rainfall prediction using the sequential input and data window input approaches. We compared the performance of these two methods using various evaluation metrics, including mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). Our results indicate that the data window input approach outperforms the sequential input approach in terms of the evaluation metrics and is able to capture the random spikes in the rainfall data. However, the sequential input model is not far behind and it is able to capture the trend in the seasonal rainfall more accurately. The findings of this study can aid in developing more accurate rainfall prediction models, which can be beneficial for water management and disaster preparedness in regions affected by heavy rainfall.