Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

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

Domain:

climategeospatial

Record type:

paper
Creator:
WilAbdullah, AliRis
Publisher:
Uni
Host:
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.

Visit

doi.org

Languages

Dinka, Northeastern

Licenses

https://creativecommons.org/licenses/by/4.0

Similar

Explainable Detection of Obfuscated Malicious PowerShell Scripts Using CodeBERT, Random Forest, and Multi-Method SHAP Explainable Detection of Obfuscated Malicious PowerShell Scripts Using CodeBERT, Random Forest, and Multi-Method SHAPDeteksi Dini Diabetes Melitus Tipe 2 Berdasarkan Data Rekam Medis Pasien RSUD Pintu Padang Kabupaten Tapsel Menggunakan Algoritma Random ForestRandom Forest Algorithm Implementation for Air Quality Classification in DKI Jakarta Based on ISPUImplementation of Isolation forest for Anomaly Detection in Hospital Management Information SystemLeveraging random forest algorithm for proactive detection of information breaches in the Nigerian oil and gas sectorComparative Evaluation of Random Forest and XGBoost for Fraud Detection in Mobile Money Payment Systems using SMOTE

Explainable Detection of Obfuscated Malicious PowerShell Scripts Using CodeBERT, Random Forest, and Multi-Method SHAP Explainable Detection of Obfuscated Malicious PowerShell Scripts Using CodeBERT, Random Forest, and Multi-Method SHAP

This dataset contains the complete supplementary materials for the paper "Explainable Detection of O

Deteksi Dini Diabetes Melitus Tipe 2 Berdasarkan Data Rekam Medis Pasien RSUD Pintu Padang Kabupaten Tapsel Menggunakan Algoritma Random Forest

Diabetes melitus tipe 2 merupakan penyakit kronis yang kasusnya terus bertambah dari tahun ke tahun,

Random Forest Algorithm Implementation for Air Quality Classification in DKI Jakarta Based on ISPU

Air quality is an essential factor that has a direct impact on human health. High concentrations of

Implementation of Isolation forest for Anomaly Detection in Hospital Management Information System

The digitization of the healthcare sector through the Hospital Management Information System (HMIS)

Leveraging random forest algorithm for proactive detection of information breaches in the Nigerian oil and gas sector

The Nigerian oil and gas sector's dependence on information and communication technologies makes it

Comparative Evaluation of Random Forest and XGBoost for Fraud Detection in Mobile Money Payment Systems using SMOTE

Mobile Money Payment Systems (MMPS) have emerged as a rapidly growing tool in the fintech space acro