Estimation of location-specific precipitation using Deep Neural Networks

Abstract

This study presents a paradigm shift by using Deep Neural Networks (DNNs) demonstrating superiority over the traditional methods like Kriging for station-specific precipitation approximation. A thorough analysis of identifying the best nearest neighbour approximation and computation time is carried out to ascertain the computational and methodological supremacy. We propose two innovative NN architectures: one utilizing precipitation, elevation, and location, and the other incorporating additional meteorological parameters like humidity, temperature, and wind speed. Trained on a vast data (1980-2019), these models outperform Kriging across various evaluation metrics (correlation coefficient, root mean square error, bias, and skill score) on a five-year validation set for any given location. This compelling evidence demonstrates the transformative power of deep learning for spatial prediction, offering a robust and precise alternative for hyperlocal precipitation estimation.