Case Study: Gradient Boosting Machine vs Light GBM in Potential Landslide Detection

Djarot Hindarto

doi:10.47709/cnahpc.v6i1.3374

Authors

Djarot Hindarto Prodi Informatika, Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional Jakarta

DOI:

https://doi.org/10.47709/cnahpc.v6i1.3374

Keywords:

Gradient Boosting Machine, Light Gradient Boosting Machine, Landslides Detection, Geographical

Abstract

An increasing demand for precise forecasts concerning the likelihood of landslides served as the impetus for this investigation. Human life, infrastructure, and the environment are all profoundly affected by this natural occasion. Constructing models capable of discerning intricate patterns among diverse factors that impact the likelihood of landslide occurrences constitutes the primary obstacle in landslide detection. Predicting potential landslides requires algorithms that are both accurate and efficient in their processing of vast quantities of data encompassing a variety of geographical, environmental, and ecological characteristics. An evaluation of the efficacy of both Gradient Boosting Machine and Light Gradient Boosting Machine in identifying patterns associated with landslides is accomplished by comparing their performance on a large and complex dataset. In the realm of potential landslide detection, the primary aim of this research endeavor is to assess the predictive precision, computation duration, and generalizability of Gradient Boosting Machine and Light Gradient Boosting Machine. This research aims to enhance comprehension regarding the comparative benefits of these two approaches in surmounting the obstacles associated with risk assessment and modeling pertaining to potential landslides, with a specific emphasis on efficiency and precision. The research findings are anticipated to serve as a valuable reference in the identification of more efficient approaches to reduce the likelihood of landslide-induced natural catastrophes. The accuracy of the GBM experiment reached 82% and LGBM reached 81%.

Downloads

Download data is not yet available.

References

Achu, A. L., Aju, C. D., Di, M., Prakash, P., Gopinath, G., Shaji, E., & Chandra, V. (2023). Geoscience Frontiers Machine-learning based landslide susceptibility modelling with emphasis on uncertainty analysis. 14.

Deng, S., Su, J., Zhu, Y., Yu, Y., & Xiao, C. (2024). Forecasting carbon price trends based on an interpretable light gradient boosting machine and Bayesian optimization. 242(June 2023).

Friedman, B. J. H. (2001). Greedy function approximation: A gradient boosting machine. 29(5), 1189–1232.

Guo, Z., Guo, F., Zhang, Y., He, J., & Li, G. (2023). A python system for regional landslide susceptibility assessment by integrating machine learning models and its application. 9(October).

Hindarto, D. (2022). Perbandingan Kinerja Akurasi Klasifikasi K-NN, NB dan DT pada APK Android. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 9(1), 486–503. https://doi.org/10.35957/jatisi.v9i1.1542

Hindarto, D., & Djajadi, A. (2023). Android-manifest extraction and labeling method for malware compilation and dataset creation. 13(6), 6568–6577. https://doi.org/10.11591/ijece.v13i6.pp6568-6577

Hindarto, D., & Santoso, H. (2022). PERFORMANCE COMPARISON OF SUPERVISED LEARNING USING NON-NEURAL NETWORK AND NEURAL NETWORK. Janapati, 11, 49–62.

Mishra, D., Naik, B., Nayak, J., Souri, A., Byomakesha, P., & Vimal, S. (2023). Light gradient boosting machine with optimized hyperparameters for identi fi cation of malicious access in IoT network. 9(October 2022), 125–137.

Oluwatosin, T., Opeoluwa, D., & Gbenga, E. (2023). Healthcare Analytics A Light Gradient-Boosting Machine algorithm with Tree-Structured Parzen Estimator for breast cancer diagnosis. 4(June).

Oram, E., Byomakesha, P., Naik, B., Nayak, J., & Vimal, S. (2021). Light gradient boosting machine-based phishing webpage detection model using phisher website features of mimic URLs. 152, 100–106.

Sunaryono, D., Sarno, R., & Siswantoro, J. (2022). Gradient boosting machines fusion for automatic epilepsy detection from EEG signals based on wavelet features. 34, 9591–9607.

Tai, C., Liao, T., Chen, S., & Chung, M. (2024). Sleep stage classification using Light Gradient Boost Machine?: Exploring feature impact in depressive and healthy participants. 88(October 2023).

Thongthammachart, T., Araki, S., Shimadera, H., Matsuo, T., & Kondo, A. (2022). Incorporating Light Gradient Boosting Machine to land use regression model for estimating NO2 and PM2.5 levels in Kansai region, Japan. 155(May).

Yang, C., Liu, L., Huang, F., Huang, L., & Wang, X. (2023). Machine learning-based landslide susceptibility assessment with optimized ratio of landslide to non-landslide samples. 123, 198–216.

Yin, H., Sharma, B., Hu, H., Liu, F., Kaur, M., Cohen, G., Mcconnell, R., & Eckel, S. P. (2024). Predicting the climate impact of healthcare facilities using gradient boosting machines. 12(September 2023).

Zeng, T., Jin, B., Glade, T., Xie, Y., Li, Y., Zhu, Y., & Yin, K. (2024). Assessing the imperative of conditioning factor grading in machine learning-based landslide susceptibility modeling?: A critical inquiry. 236(November 2023).