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Technology’s rapid advancement has revolutionised disaster mitigation, and AI is at the forefront of this transformation. With its unparalleled ability to process vast amounts of data and discern patterns, trends, and associations, AI significantly enhances the accuracy of predictive algorithms (Ghaffarian et al., 2023).

AI is critical in predicting disasters, mitigating casualties, and giving citizens ample time to prepare. For example, since 2006, Indonesia’s Meteorology, Climatology, and Geophysical Agency (Badan Meteorologi, Klimatologi, dan Geofisika/BMKG) has been developing a network of digital seismographs to accelerate the dissemination of earthquake and tsunami information. For example, in 2018, Banjarnegara was struck by an earthquake as powerful as a 4.4 Richter magnitude earthquake with a depth of four kilometres. To enhance information and support the emergency response process, BMKG immediately deployed to the field by installing five Portable Digital Seismographs at the epicentre (earthquake centre). BMKG has also established an earthquake information post in Kalibening District, Banjarnegara, to provide the latest information related to earthquakes and weather to National Disaster Management Agency (Badan Nasional Penanggulangan Bencana/BNPB), Regional Disaster Management Agency (Badan Penanggulangan Bencana Daerah/BPBD), Local Government, Volunteers, and the community to ensure a smooth emergency response process (BMKG, 2018). BMKG (2019) implemented the seismograph in other locations, including Pasir Jambu, Bandung, and Yogyakarta (Candi Abang), considering that the Yogyakarta region itself has earthquake potential from active faults such as the Opak thrust fault and the subduction zone (Indo-Australian and Eurasian plates) in southern Java, Mount Kucir in the Menoreh Mountains, and Kulon Progo (BMKG, 2021), and loads more.   

Data and information are the cornerstones of Adaptive Social Protection (ASP). Indonesia has established One Data Indonesia (Satu Data Indonesia/SDI) as the government’s data management policy. This initiative aims to produce accurate, up-to-date, integrated, and accountable data that is easily accessible and shared between central agencies and local areas. Social registries can serve as entry points for various programs when fully developed. By using a shared or integrated social registry, multiple programs can coordinate their efforts to effectively reach target populations (Bowen et al., 2020).

Figure 1. Gateway for social registry multiple programmes

Source: Author, 2024 (Adapted from Bowen et al., 2020 & World Bank, nd)


Communities have varying levels of vulnerability that need to be addressed, particularly in disasters and climate change. A wide range of research highlights the importance of the vulnerability assessment model (Sarachaga & Espino, 2019; Aswani et al., 2019; Hizbaron et al., 2018). This model helps to identify vulnerable communities based on economic, demographic, psychological, political, and physical factors. Integrating the vulnerability assessment model with hazard data as part of the disaster risk system ensures that data on vulnerable communities remains accurate, up-to-date, and comprehensive. The disaster risk system includes an early warning mechanism with real-time data analysis, predictive models, targeted alerts, and evacuation planning.

Regarding the implications of implementing AI in ASP, the concept itself is not new, but programme design and budget allocation will need some adjustments. Loads of factors must be considered, such as its coverage, relevance, accessibility, accuracy, and information security (Syamsulhakim, 2023). Furthermore, the dynamic nature of data for vulnerable groups, which needs to be constantly updated, poses a significant hurdle. A new financing system will also be essential in its successful implementation. Forecast-based financing disburses humanitarian funds based on forecast information from previously agreed-upon activities that reduce risk, enhance preparedness and response, and make disaster risk reduction in overall humanitarian assistance more effective, the steps developed by the International Federation of Red Cross and Red Crescent Societies (IFRC) consist of stages to implement forecast-based financing in ASP. These steps involve understanding risk scenarios, identifying available forecasts, formulating early actions, identifying danger levels, creating standard operating procedures or guidelines, and validating them with key stakeholders (German Humanitarian Assistance, 2023). 

Figure 2. Forecast-based Financing Implementation Scheme

Source: Author, 2024 (Adapted from German Humanitarian Assistance et al., 2023 & Bwire, n.d.)


To support Indonesia’s climate action, integrating AI into its systems will help mitigate risk and help citizens adapt. AI can analyse climate data to predict weather patterns, extreme conditions, and the social and economic impacts of climate change, including changes in rainfall. With this information, policymakers can stay informed about threats such as rising sea levels, earthquakes, hurricanes, temperature fluctuations, habitat destruction, and species loss, thus being able to make adjustments accordingly (Xin et al., n.d.). 

Discover more in our May Op-Ed edition! Dive into ASP Knowledge Hub Series #2: The Role of Artificial Intelligence in Adaptive Social Protection by reading our Op-Ed

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