Combining AI and MBD for flood detections in Indonesia
Indonesia, a country frequently affected by natural disasters such as floods, faces a significant challenge in reducing its population's vulnerability to these events. The existing measures for flood detection and management often lack real-time accuracy and the capability to predict flooding events effectively, making it difficult for local governments to take proactive measures to mitigate risks and protect infrastructure and human lives.
To address this critical issue, mobile operator XL Axiata, in collaboration with Jakarta's municipal government and Nodeflux, developed a proactive flood detection solution. This system utilises mobile sensor networks to monitor water levels in dams, sewers, waterways, and groundwater, integrating these data sources with AI to predict flooding events accurately. This approach offers a path for not only commercial sustainability but also substantial social and environmental impacts by improving flood resilience. With the potential to expand across Southeast Asia, the solution represents a significant advancement in disaster preparedness and risk mitigation in flood-prone areas.
This AI/MBD-based solution has the potential to significantly impact the lives and economies of high flood-risk areas in Indonesia. By providing the capacity to anticipate floods and alert citizens effectively, it can lead to more efficient emergency responses, minimizing injury, loss of life, and property damage. With the average annual cost of flooding expected to rise, this solution offers long-term human and financial benefits, not only for Indonesia but potentially across Southeast Asia.
The technological foundation of this initiative lies in the integration of sensor network data with AI analytics. The system processes real-time data from sensors, including smart cameras, capturing water levels in critical areas. Machine learning algorithms continually monitor this data, identifying increases in water levels and packaging this information to be fed into city-level data centres for flood prediction and proactive warning issuance.
The development of this flood detection system showcases a broad ecosystem involving government agencies, development agencies, mobile operators, academic and non-profit organizations, and private sector entities. This diverse collaboration highlights the importance of multi-stakeholder engagement in addressing complex environmental challenges like flood management. Key partners include government bodies like BRIN and BPPT, municipal governments, development agencies like the ADB and World Bank, and private investors.
Responsible AI by design:
Developing a sustainable business model for this solution, while ensuring privacy and compliance with regulations, is a critical aspect of this project. As the system involves collecting and processing large amounts of data, including from mobile networks, privacy considerations must be managed appropriately. The project aligns with Indonesia's Smart Cities programme and aims to balance technological innovation with social, environmental, and economic impacts.