EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Forecasting impacts of tropical cyclones with machine learning : A case study in the Philippines 

Aklilu Teklesadik and Marc van den Homberg
Aklilu Teklesadik and Marc van den Homberg
  • Netherlands Redcross, 510Global, delft, Netherlands (

Due to its geographical location, the Philippines is highly exposed to Tropical Cyclones (TC). Every year at least one TC will make landfall and cause significant humanitarian impact and economic loss. To reduce the humanitarian impact of TC, the Philippine Red Cross with the German Red Cross and 510, an initiative of The Netherlands Red Cross, designed and implemented a Forecast Based Financing (FbF) system. The early actions in the FbF system are pre-identified and will be triggered when an impact-based forecasting model indicates a pre-defined danger level will be exceeded. This research develops and evaluates multiple ML algorithms for classification and regression with a lead time of 120 to 72hrs before TC landfall. The algorithms are trained on around 40 historical typhoon events and xx predictors on the hazard, vulnerability, coping capacity, and exposure are used. The classification model predicts if 10% of buildings in a municipality are completely damaged or not. The regression model gives the percentage of buildings that are completely damaged in a municipality. The RandomForest algorithm outperformed other algorithms for both classification and regression for both training and validation datasets. The ML models performed better than a baseline model (a wind-damage curve per building type) for the historical typhoon events. The Philippine Red Cross has been using the ML model since 2019, whereby actual forecast information from ECWMF replaces the historical hazard information at landfall. However, the ML impact-based forecasting model cannot be better than the hazard information that goes into it. Those typhoons that rapidly intensify cannot be captured at the cutoff of 72 hrs lead time (the minimum time required to start up early actions). But for the other typhoons, ML is very beneficial as a trigger tool for activating early actions and can support the reduction of the impact of typhoons on vulnerable communities.

How to cite: Teklesadik, A. and van den Homberg, M.: Forecasting impacts of tropical cyclones with machine learning : A case study in the Philippines , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12917,, 2022.