Improving the reliability of an impact-based forecasting model

Improving the reliability of an impact-based forecasting model: A case study for typhoons and landslides in the Philippines

Free, Renske (TU Delft Civil Engineering and Geosciences)

Bogaard, T.A. (graduation committee)
van den Homberg, Marc (graduation committee)
Sperna Weiland, Frederiek (graduation committee)
Aguilar Lopez, J.P. (graduation committee)

Delft University of Technology

Civil Engineering | Environmental Engineering

2022-01-24

Anticipatory action requires models that can accurately and reliably predict the impact of natural hazards. However, impact forecasts are often underestimated when consecutive hazards are not considered. In the Bicol region in the Philippines, typhoons trigger 90% of landslides, causing a lot of fatalities and damage to infrastructure and agriculture. The lack of information on past landslide events has hampered the construction of landslide forecasting models. Currently, a machine learning (ML) impact-based forecasting (IBF) model for typhoons is operational in the Philippines. The model was developed by 510, an initiative of the Netherlands Red Cross. The model predicts impact due to the high wind speeds associated with typhoons and includes the possible impact due to landslides only via a static landslide susceptibility map. Hence, this study focused on extending the 510 typhoon model via hybrid modeling into a multi-hazard forecasting model for both typhoons and landslides to improve the forecast by considering impact from typhoon-induced landslides. The implementation of the hybrid multi-hazard impact-based forecasting model was tested on two typhoon events in the Bicol region.

A hydrometeorological landslide IBF model was successfully created, even with the limited data on landslide occurrences and rainfall available. The newly established regional event duration threshold for Bicol was applied on the case study events with an increased impact boundary of 300 km compared to the typhoon impact boundary of 100 km. The results of the hybrid multi-hazard model showed an improved impact forecast -compared to the model considering solely static input of landslides, which underestimated impact- in both location extent of the impact forecast and in accuracy: the True Positives doubled, whereas the False Negatives reduced by half. The separate landslide IBF model as an extension of the existing ML typhoon model provided additional benefits as these models can be decoupled to optimize the performance and reliability of both. This study resulted in the prototype of an impact-based multi-hazard model for typhoons and landslides for the Philippines and demonstrated the importance of considering impact from consecutive hazards.

Landslide
Typhoon
Consecutive hazards
Impact-based forecasting
Rainfall
Machine learning
Philippines

http://resolver.tudelft.nl/uuid:64e8a6e9-e7b4-41a0-aabd-06f10625660d

Student theses

master thesis

© 2022 Renske Free