Field-level insights from developing and deploying AI-driven system for disaster early warning in Timor-Leste. Natural Disaster, Threats, and Readiness in Timor-Leste On the 4th of April, 2021, tropical cyclone SEROJA formed over the Savu Sea (eastern Indonesia, bordering Timor-Leste), and after passing, the Government of Timor-Leste (GoTL) reported 48 fatalities and 15,876 displaced persons in the capital city (Dili) alone1. Despite availability of limited data, the impact was presumably widespread, including the loss of lives and properties in other areas of the Country2. This is not an isolated event in Timor-Leste. According to ReliefWeb3, Timor-Leste is located 1 Relief Web. 2021. https://reliefweb.int/report/timor-leste/timor-leste-floods-situation-report-no-10-18-june-2021 2 UN-News. 2021. UN steps up response, as thousands impacted by Timor-Leste floods. Available at: https://news.un.org/en/story/2021/04/1089012 3 Reliefweb, 2019. Timor-Leste: Disaster Management Reference Handbook (October 2019). https://reliefweb.int/report/timor- leste/timor-leste-disaster-management-reference-handbook-october-2019 MERCY CORPS AI for Early Warning on Natural Disasters 1 near one of the most active tectonic plate boundaries in the world, therefore, the high seismic activity and exposure to frequent earthquakes causes significant damage, including triggering landslides with devastating impact on citizen’s lives, livestock, roads, infrastructure, and property. In March 2018, the UN mission reported that the country had experienced 793 disaster events between 2002 and 2013. While the disasters are relatively small scale and have resulted in few deaths, close to 20% of the entire population of Timor-Leste has been affected (221,730 people). Although Timor-Leste has a medium exposure to hazards, it is the seventh most disaster-prone country in the world due to poor coping and adaptive strategies. The country is prone to severe and recurrent windstorms, drought, flooding, tropical cyclones, earthquakes, and tsunamis. Timor-Leste currently lacks the institutional and technological capacities to prepare for natural disaster risks or mitigate impact of disaster events. Multiple severe, isolated windstorms have occurred Figure 1: Map of Timor-Leste each year for the last three years destroying homes and entire crops of maize. Drought can be a problem during the dry season, exacerbating the country’s food security problem, as 36% of Timor-Leste’s population still faces moderate to severe chronic food insecurity and 85% of women and children still consume inadequately diversified diets. Windstorms and droughts also increase the probability of large fires, especially as farmers commonly use slash-and-burn methods. Floods have resulted in decreased agricultural production, damage to infrastructure, and, particularly in heavily flood-affected locations, displacement of communities and loss of property and livelihoods. The country experiences cyclical effects of the El Niño/Southern Oscillation (ENSO)-related weather anomalies and there is now substantial evidence to indicate that the changes in weather conditions associated with climate change will increase the likelihood and intensity of recurrent natural disasters throughout the country. There is currently no functional national-level end-to-end early warning system to help communities prepare for and respond to these frequent disasters. The collection of hydro-meteorological data is uncoordinated and sharing of data across government ministries for use in early warning system (EWS) is still a challenge. Although weather (forecast) information is available at the national level from neighboring countries and a regional hydro-meteorology service provider, it is rarely linked with preparation and response mechanisms from the national to the community level. Early warning system (EWS) and Mercy Corps’ Role A national-level early warning system is in the very early stages of development in Timor-Leste. Mercy Corps Country Team in Timor-Leste assisted GoTL, UNDP, and a private service provider, Similie, to MERCY CORPS AI for Early Warning on Natural Disasters 2 develop a standard operating procedure (SOP) and thresholds for various disaster risks which can enable the triggering of disaster alerts in Timor-Leste. The goal is to establish thresholds for communicating early actions and protocols to community disaster management officials. However, hydro- meteorological data is both spatially and temporally sporadic and it is plagued with substantial data quality issues. While efforts are underway to install additional hardware for data collection in the country, these are insufficient to fill the data gaps that is required to guide the establishment of effective thresholds. As an alternative solution, it is common practice for countries to use global models and datasets to provide alerts based on forecasted weather events. These valuable sources of data are often limited by coarseness of their resolution (or minimum size of unit land-area covered) and the lack of ground-truth data, especially in geographically small countries with highly mountainous topography and significant spatial variability of weather which are often not represented in the models. In January 2020, Mercy Corp Team envisioned and initiated a collaborative pilot project with Similie to strengthen the development of an early warning system for Timor-Leste. The goal of the project is to deploy and test state-of-the-art artificial intelligence (AI) algorithm to improve thresholds for alerts that can support Timor-Leste communities to prepare for and respond to the impacts of natural disasters. Leveraging the existing warning system, the project team envisioned a co-installation of hardware sensors to generate additional data flow that can support the selection of optimal parameters and threshold settings for early warning system alerts. The AI-based system (and technology) is expected to disrupt and empower disaster risk reduction practitioners to address the spatial and temporal paucity of historical data and poor representation of downscaled global models for early warning systems. Developing an AI-driven system: Old problem-New Solution The implementation of this project was divided into 2 stages. The first stage focused on model development and testing based on legacy/historical weather data while the second stage focused on installing a monitoring sensor to improve robustness of data flow from a major river catchment in Timor-Leste. Both stages address problems related to inexistence of best- fit predictive model and limited availability of data to advance meaningful intelligence on trends and thresholds of disaster risks. Figure 2: Simplified workflow for the development of AI-driven model and thresholds for early warning on water-related natural disaster in Timor-Leste. MERCY CORPS AI for Early Warning on Natural Disasters 3 AI Platform Built and integrated into ONE-EWS Platform Similie, a social enterprise, based in Timor-Leste, was contracted to build and deploy an AI Platform that combines data from existing weather stations with global and satellite datasets to optimize the setting of thresholds that will trigger early warning alerts to communities. The contractor followed a stepwise process that leverages contextual knowledge, legacy data, and emerging tools for high precision and predictive performance (Fig 2). ONE-EWS was developed by Similie to advance early warning surveillance and alert systems for use by public and private entities in Timor-Leste. At the onset of the project, a collaborative workshop was organized to discuss objectives, discuss priority needs with stakeholders, and map relevant entry points for access to data and relevant authorities in the Country. After initial interaction and gathering of insights and data from stakeholders, available meteorological data were synthesized and subject to various processing, including spatial kriging to generate continuous data trend across the target geography. To build the AI-enabled platform and integrate with ONE-EWS, a decision-tree-based ensemble machine learning modeling framework was adapted and deployed, which include time-series hierarchical clustering (unsupervised learning) and cluster-labeling with a gradient boosting framework (XGBoost) – a supervised machine learning approach. This modeling approach was based on Cluster days with a higher risk of flood (i.e. Time Series Hierarchical Clustering) and, predicted the likelihood of a flood within an hour interval (using the XGBoost tool). Due to the limitation of observation data points on weather and hydrology (n=1,198), it was difficult to clearly define the timing of flood occurrence or apply more complex approach, such as deep learning model. To enhance the model accuracy and account for trends and seasonality, Similie generated several lagged features for each variable that was used in the model, and calculated the average values of temperature and humidity over the previous 3, 7, 30 and 90 days, and the precipitation accumulation for the same period. The variable importance metric shows that the total amount of rain over the last week is the most significant predictor, followed by the total rain over the last three months, and by the average humidity over the previous three months highlighting the strong importance of the local climate conditions. Overall, the model’s prediction output shows an accuracy of 98%, specificity of 0.86, and sensitivity of 0.98. Based on the positive outcome of the AI