Spatial Estimation of Daily Precipitation in Thailand based on Infrared Satellite Images using Artificial Neural Networks

Precipitation data highly benefits water management and flood warning. In Thailand, an agricultural country, the number of rain gauge stations is relatively small compared to the country’s area. To deal with the problem, many spatial interpolation methods have been adopted. They utilized only geological data from stations with known rainfall values to estimate values at other locations. Characteristics of clouds captured in infrared satellite images could be used to infer rainfall. In this study, infrared satellite satellite images from every 30 minutes were integrated with geological data to spatially estimate daily precipitation. Under different assumptions, we proposed three estimation models, namely AveragedIR-ANN, IRs-ANN, and LocalIRs-CNN. All three models used artificial neural networks as the estimator. The AveragedIR-ANN model used the average of 48 black body temperature (TBB) values to present IR images at the target location as an input. Meanwhile, the 48 TBB values were directly fed into the IRs-ANN. Not only the TBB values at the target location, but the LocalIRs-CNN also used the TBB values around the target location as inputs. Experiments were performed on 24 days per year between 2016 and 2019 with multiple tries. The results show that the LocalIRs-CNN outperformed by minimizing the median highest root mean square error. The model was suggested for northern, western and central Thailand.