Comparing the Role of Spatially and Temporally capable Deep Learning Architectures in Rainfall Estimation: A Case Study over North East India

The northeastern states of India are faced with heavy-precipitation related disasters such as floods and landslides every monsoon. Further, the region's economy is predominantly dependent on agriculture. Thus, accurate prediction of rainfall plays a vital role in the planning and disaster management programs in the region. Existing methods used for rainfall prediction include Automatic Weather Stations that provide real-time rainfall measurements at specific locations. However, these are point-based estimates. For distributed measurements, a satellite-based estimation can be used. While these methods provide vital information on the spatial distribution of precipitation, they face the caveat that they provide only real-time estimates. Numerical weather forecast models are used for encoding forecasting capabilities by simulating the atmosphere's physical processes through data assimilation of observational data from various sources, including weather stations and satellites. However, these models are incredibly complex and require immense computational strength. The veracity of the numerical models is limited by available computing architecture. Recently, a host of data-driven models, including random forest regression, support vector machine regression and deep learning architectures, have been used to provide distributed rainfall forecasts. However, the relative performance of such models in an orographically complex terrain has not been ascertained via a disciplined study. Through this study, we aim to systematically assess the role of convolutional and recurrent neural network architectures in estimating rainfall. We have used rainfall data from the ERA5 Land reanalysis dataset and data from the following additional meteorological variables that can impact rainfall: dew point temperature, skin temperature, amount of solar radiation, wind components, surface pressure and total precipitation. The data aggregated on a daily scale and spanning three decades was selected for this study. We have used the following architectures of neural network algorithms: U-Net architecture modified for regression representing convolutional neural networks and Long Short-Term Memory (LSTM) architecture representing the recurrent neural networks. Various settings of each architecture, such as the number of layers, optimizers and initialization, are validated to assess their performance on rainfall estimation. The developed rainfall estimation models were validated and evaluated using rigorous statistical metrics, such as root mean square error (RMSE) and coefficient of determination (R-squared). The results of this research are expected to provide valuable insights for local governments, farmers, and other stakeholders in the northeastern states of India. Moreover, the study's methodology can be extended to other regions facing similar climate challenges, thus contributing to advancements in the field of rainfall estimation and climate modelling.