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A Novel ANN-based Model for Short-term Rain Prediction from ST Radar Moment Data in and around the Radar Site
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 72 |
| Year of Publication: 2025 |
| Authors: Nishanta Choudhury, Diganta Kumar Sarma, Parag Kalita, Samiran Patgiri, Utpal Sarma, Mahen Konwar |
10.5120/ijca2025924579
|
Nishanta Choudhury, Diganta Kumar Sarma, Parag Kalita, Samiran Patgiri, Utpal Sarma, Mahen Konwar . A Novel ANN-based Model for Short-term Rain Prediction from ST Radar Moment Data in and around the Radar Site. International Journal of Computer Applications. 186, 72 ( Mar 2025), 52-57. DOI=10.5120/ijca2025924579
Abstract
This study deals with the development of an Artificial Neural Network (ANN) based model for short-term rain prediction using moment data from Stratosphere-Troposphere (ST) radar located at 26.14o N, 91.73o E, ~50 m above MSL. The ST radar that operates at 212.5MHz scans in five directions East, West, North, South and Zenith with a tilting angle of ~12o and collects data in various height resolutions viz. 75m, 150m & 300m. For the present study, 150m and 300m height resolution data are considered. The radar data for each of the beam directions are considered separately between the heights of 1500m to 3000m. The neural network is trained with the collocated rain rate from the Automatic Weather Station (AWS) with a 30-minute time lag between radar data and rain rate. The four moments data viz. Return power (P), Doppler shift (DS), Spectral Width (SW) and Signal-to-Noise Ratio (SNR) from ST radar are considered as input parameters to the ANN and the ~30-minute time lag of rain rate data are considered as target values. The developed model will be able to predict rain rate for a diameter of 3 km with the radar as the centre. For that purpose, the neural network is trained separately for each of the beams (East, West, North, South and Zenith). Thus, five separate models are developed for predicting rain rates in and around the radar station. Further, the rain rate from these five models can be clubbed together to get an overall picture of the rain rate over a diameter of 3km. It is observed that the correlation coefficient for the training and validation of the MLPs is more or less similar (~90 %) for all the beams. But the other errors viz. mse, mae, sse & rmse are lowest for MLPs of the zenith beam compared to the other beams. The performance of the derived model was tested with three months of independent dataset data viz. May, June & July 2024 which are not part of the training dataset. It is observed that out of five MLPs for the five beams, the zenith beam performed better than the other MLPs. Though the performance of the MLPs of other beams is less compared to the MLP of the zenith beam, they could predict the rainy situation.
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