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Multi-hazard risk assessments for building portfolios exposed to earthquake shaking followed by a tsunami are usually based on empirical vulnerability models calibrated on post-event surveys of damaged buildings. The applicability of these models cannot easily be extrapolated to other regions of larger/smaller events. Moreover, the quantitative evaluation of the damages related to each of the hazard types (disaggregation) is impossible. To investigate cumulative damage on extended building portfolios, this study proposes an alternative and modular method to probabilistically integrate sets of single-hazard vulnerability models that are constantly being developed and calibrated by experts from various research fields to be used within a multi-risk context. This method is based on the proposal of state-dependent fragility functions for the triggered hazard to account for the pre-existing damage and the harmonisation of building classes and damage states through their taxonomic characterisation, which is transversal to any hazard-dependent vulnerability. This modular assemblage also allows us to separate the economic losses expected for each scenario on building portfolios subjected to cascading hazards. We demonstrate its application by assessing the economic losses expected for the residential building stock of Lima, Peru, a megacity commonly exposed to consecutive earthquake and tsunami scenarios. We show the importance of accounting for damage accumulation on extended building portfolios while observing a dependency between the earthquake magnitude and the direct economic losses derived for each hazard scenario. For the commonly exposed residential building stock of Lima exposed to both perils, we find that classical tsunami empirical fragility functions lead to underestimations of predicted losses for lower magnitudes (Mw) and large overestimations for larger Mw events in comparison to our state-dependent models and cumulative-damage method.

Este producto forma parte de una serie de infografías de divulgación científica que buscan reseñar algunas de las investigaciones más importantes en las que ha tenido participación la Universidad EAFIT, publicadas en las revistas especializadas más prestigiosas del mundo

Forecasting and early warning systems are important investments to protect lives, properties, and livelihood. While early warning systems are frequently used to predict the magnitude, location, and timing of potentially damaging events, these systems rarely provide impact estimates, such as the expected amount and distribution of physical damage, human consequences, disruption of services, or financial loss. Complementing early warning systems with impact forecasts has a twofold advantage: It would provide decision makers with richer information to take informed decisions about emergency measures and focus the attention of different disciplines on a common target. This would allow capitalizing on synergies between different disciplines and boosting the development of multihazard early warning systems. This review discusses the state of the art in impact forecasting for a wide range of natural hazards. We outline the added value of impact-based warnings compared to hazard forecasting for the emergency phase, indicate challenges and pitfalls, and synthesize the review results across hazard types most relevant for Europe.

Forecasting and early warning systems are important investments to protect lives, properties and livelihood. While early warning systems are frequently used to predict the magnitude, location and timing of potentially damaging events, these systems rarely provide impact estimates, such as the expected amount and distribution of physical damage, human consequences, disruption of services or financial loss. Complementing early warning systems with impact forecasts has a two‐fold advantage: it would provide decision makers with richer information to take informed decisions about emergency measures, and focus the attention of different disciplines on a common target. This would allow capitalizing on synergies between different disciplines and boosting the development of multi‐hazard early warning systems. This review discusses the state‐of‐the‐art in impact forecasting for a wide range of natural hazards. We outline the added value of impact‐based warnings compared to hazard forecasting for the emergency phase, indicate challenges and pitfalls, and synthesize the review results across hazard types most relevant for Europe. Plain language summary Forecasting and early warning systems are important investments to protect lives, properties and livelihood. While such systems are frequently used to predict the magnitude, location and timing of potentially damaging events, they rarely provide impact estimates, such as the expected physical damage, human consequences, disruption of services or financial loss. Extending hazard forecast systems to include impact estimates promises many benefits for the emergency phase, for instance, for organising evacuations. We review and compare the state‐of‐the‐art of impact forcasting across a wide range of natural hazards, and outline opportunities and key challenges for research and development of impact forecasting.

Forecasting and early warning systems are important investments to protect lives, properties and livelihood. While early warning systems are frequently used to predict the magnitude, location and timing of potentially damaging events, these systems rarely provide impact estimates, such as the expected amount and distribution of physical damage, human consequences, disruption of services or financial loss. Complementing early warning systems with impact forecasts has a two‐fold advantage: it would provide decision makers with richer information to take informed decisions about emergency measures, and focus the attention of different disciplines on a common target. This would allow capitalizing on synergies between different disciplines and boosting the development of multi‐hazard early warning systems. This review discusses the state‐of‐the‐art in impact forecasting for a wide range of natural hazards. We outline the added value of impact‐based warnings compared to hazard forecasting for the emergency phase, indicate challenges and pitfalls, and synthesize the review results across hazard types most relevant for Europe. Plain language summary Forecasting and early warning systems are important investments to protect lives, properties and livelihood. While such systems are frequently used to predict the magnitude, location and timing of potentially damaging events, they rarely provide impact estimates, such as the expected physical damage, human consequences, disruption of services or financial loss. Extending hazard forecast systems to include impact estimates promises many benefits for the emergency phase, for instance, for organising evacuations. We review and compare the state‐of‐the‐art of impact forcasting across a wide range of natural hazards, and outline opportunities and key challenges for research and development of impact forecasting.

Forecasting and early warning systems are important investments to protect lives, properties and livelihood. While early warning systems are frequently used to predict the magnitude, location and timing of potentially damaging events, these systems rarely provide impact estimates, such as the expected amount and distribution of physical damage, human consequences, disruption of services or financial loss. Complementing early warning systems with impact forecasts has a two‐fold advantage: it would provide decision makers with richer information to take informed decisions about emergency measures, and focus the attention of different disciplines on a common target. This would allow capitalizing on synergies between different disciplines and boosting the development of multi‐hazard early warning systems. This review discusses the state‐of‐the‐art in impact forecasting for a wide range of natural hazards. We outline the added value of impact‐based warnings compared to hazard forecasting for the emergency phase, indicate challenges and pitfalls, and synthesize the review results across hazard types most relevant for Europe. Plain language summary Forecasting and early warning systems are important investments to protect lives, properties and livelihood. While such systems are frequently used to predict the magnitude, location and timing of potentially damaging events, they rarely provide impact estimates, such as the expected physical damage, human consequences, disruption of services or financial loss. Extending hazard forecast systems to include impact estimates promises many benefits for the emergency phase, for instance, for organising evacuations. We review and compare the state‐of‐the‐art of impact forcasting across a wide range of natural hazards, and outline opportunities and key challenges for research and development of impact forecasting.

Forecasting and early warning systems are important investments to protect lives, properties and livelihood. While early warning systems are frequently used to predict the magnitude, location and timing of potentially damaging events, these systems rarely provide impact estimates, such as the expected amount and distribution of physical damage, human consequences, disruption of services or financial loss. Complementing early warning systems with impact forecasts has a two‐fold advantage: it would provide decision makers with richer information to take informed decisions about emergency measures, and focus the attention of different disciplines on a common target. This would allow capitalizing on synergies between different disciplines and boosting the development of multi‐hazard early warning systems. This review discusses the state‐of‐the‐art in impact forecasting for a wide range of natural hazards. We outline the added value of impact‐based warnings compared to hazard forecasting for the emergency phase, indicate challenges and pitfalls, and synthesize the review results across hazard types most relevant for Europe. Plain language summary Forecasting and early warning systems are important investments to protect lives, properties and livelihood. While such systems are frequently used to predict the magnitude, location and timing of potentially damaging events, they rarely provide impact estimates, such as the expected physical damage, human consequences, disruption of services or financial loss. Extending hazard forecast systems to include impact estimates promises many benefits for the emergency phase, for instance, for organising evacuations. We review and compare the state‐of‐the‐art of impact forcasting across a wide range of natural hazards, and outline opportunities and key challenges for research and development of impact forecasting.

This study evaluates the potential of object-based image analysis in combination with supervised machine learning to identify urban structure type patterns from Landsat Thematic Mapper (TM) images. The main aim is to assess the influence of several critical choices commonly made during the training stage of a learning machine on the classification performance and to give recommendations for classifier-dependent intelligent training. Particular emphasis is given to assess the influence of size and class distribution of the training data, the approach of training data sampling (user-guided or random) and the type of training samples (squares or segments) on the classification performance of a Support Vector Machine (SVM). Different feature selection algorithms are compared and segmentation and classifier parameters are dynamically tuned for the specific image scene, classification task, and training data. The performance of the classifier is measured against a set of reference data sets from manual image interpretation and furthermore compared on the basis of landscape metrics to a very high resolution reference classification derived from light detection and ranging (lidar) measurements. The study highlights the importance of a careful design of the training stage and dynamically tuned classifier parameters, especially when dealing with noisy data and small training data sets. For the given experimental set-up, the study concludes that given optimized feature space and classifier parameters, training an SVM with segment-shaped samples that were sampled in a guided manner and are balanced between the classes provided the best classification results. If square-shaped samples are used, a random sampling provided better results than a guided selection. Equally balanced sample distributions outperformed unbalanced training sets.