IMPLEMENTATION OF GABELLA METHOD AND RANDOM FOREST FOR GROUND CLUTTER DETECTION IN PADANG WEATHER RADAR DATA

Abstract

Weather radar is an active remote sensing instrument for various hydrological and meteorological applications. One advantage of weather radar is its ability to detect rainfall in space and time with high spatial resolution. However, one of the issues that contaminate radar observations is ground clutter. Ground clutter is a signal or echo from non-meteorological objects on the earth’s surface that are stationary in the time domain. Detecting and mitigating clutter effects is crucial to achieve precise weather measurements. This research aims to implement the Gabella and random forest methods to detect ground clutter in Padang weather radar data and determine the optimal method between the two. The implementation of the Gabella method for detecting ground clutter in Padang weather radar data was suboptimal. This was due to the most duplicated data at the same point being only 15.97% of the total data. Meanwhile the random forest method obtained a kappa value of 92.03%. This indicates that the random forest model created using 2000 trees as the parameter performs well. Based on these results, the random forest method identified as the most optimal approach for detecting ground clutter in Padang weather radar data.