Accurate precipitation estimates at individual locations are crucial for weather forecasting and spatial analysis. This study presents a paradigm shift by leveraging Deep Neural Networks (DNNs) to surpass traditional methods like Kriging for station-specific precipitation approximation. We propose two innovative NN architectures: one utilizing precipitation, elevation, and location, and another incorporating additional meteorological parameters like humidity, temperature, and wind speed. Trained on a vast dataset (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. This compelling evidence demonstrates the transformative power of deep learning for spatial prediction, offering a robust and precise alternative for station-specific precipitation estimation.
@article{arxiv.2503.14095,
title = {Towards Location-Specific Precipitation Projections Using Deep Neural Networks},
author = {Bipin Kumar and Bhvisy Kumar Yadav and Soumypdeep Mukhopadhyay and Rakshit Rohan and Bhupendra Bahadur Singh and Rajib Chattopadhyay and Nagraju Chilukoti and Atul Kumar Sahai},
journal= {arXiv preprint arXiv:2503.14095},
year = {2025}
}