A Quick Review of Technical Developments in Indonesian Landslides Early Warning Systems

¹ Department of Geology, Banaras Hindu University, Varanasi - 221005, India
² Centre of Tropical Geoengineering, Universiti Teknologi Malaysia dzulaika@utm.my
³ Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia bakhtiar.affandy@utm.my
⁴ RMB Consultant, 2C 183, Kalpataru Hills Ph 2, Thane 400 604 India rmbhatawdekar@gmail.com
⁵ Department of Civil Engineering, Faculty of Civil Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia amohdtaib@ukm.edu.my

^* Corresponding author: pandey.vhr@bhu.ac.in

Published online: 19 December 2025

Abstract

Increasing demand for infrastructure development, driven by population growth and urbanization, has heightened the risk of landslides, especially in regions with limited land resources. Traditional approaches to landslide hazard estimation and site-specific warning systems often rely on conventional slope stability analyses and susceptibility models. However, ensuring public safety necessitates the urgent development of comprehensive early detection and warning systems. While existing literature explores the benefits and functionality of early detection systems, detailed insights into the interplay of various factors governing their development remain underexplored. This review aims to address this gap by identifying and analysing key parameters influencing the development of early warning systems in Indonesia based on historical landslide events. Data on past landslides will be collected, categorized, and evaluated for their suitability as input factors for early detection frameworks. The study further examines existing detection and warning frameworks, offering insights for their enhancement through future research. The findings are expected to provide a comprehensive overview of critical factors influencing the design and implementation of early warning systems, emphasizing their role in mitigating the severe impacts of landslide hazards. Integrating remote sensing data, such as TRMM, GPM, and CMORPH, with advanced hydrological-geotechnical models and community-based strategies emerges as a promising approach. These innovations, coupled with IoT technologies and real-time monitoring, hold potential for developing robust, scalable, and adaptive Landslide Early Warning Systems (LEWS) for global application.