Marc Van Den Homberg
Biographic Data
| ID | 3430990 |
|---|---|
| NAME | Marc Van Den Homberg |
| GIVEN NAMES | Marc Van Den |
| FAMILY NAME | Homberg |
| SIGNATURE | VAN DEN HOMBERG M |
| AFFILIATIONS | Red Cross Hospital |
| ORCID | 0000-0003-1436-254X |
| VERIFIED | Yes |
| TOTAL WORKS | 14 |
| TOTAL CITATIONS | 4 |
| AUTHOR COUNT | 14 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 2 |
How to better link landslide inventory mapping with loss and damage reporting
Landslides are inherent multi-hazard events, with different types of triggering mechanisms and due to their widely varying characteristics. Landslides are very underrepresented in global disaster databases, due to their high frequency and relatively low impacts, where individual events might often not pass the threshold for inclusion in the database. As a result, their accumulated effect is underestimated. In relation to the Early Warnings for Al…
Planning ahead
Agricultural communities worldwide face escalating climate hazards that strain traditional relief schemes, underscoring the urgency of data-driven anticipatory action. We implement a spatially explicit, time-phased mixed-integer linear program that allocates anticipatory cash and in-kind items to farmers across thousands of micro-regions while respecting equity, budget, and capacity constraints. Built on an open-source pipeline combining Python, …
Framing of disaster impact in online news media
Introduction High-quality impact data is essential for several applications in disaster risk management including Early Warning Systems. Currently, most impact data have spatial and temporal gaps, especially in data-poor contexts. Local news media reporting on disasters can contain information to bridge these gaps. However, each news media outlet frames disasters differently, especially since disasters diffuse in time and space. This study addres…
Towards optimal anticipatory action
Evaluating impact-based forecasting models for tropical cyclone anticipatory action
Impact‐based Forecast (IBF) is increasingly adopted for Anticipatory Action in disaster risk management, yet systematic comparison of the diverse models in use remains limited. To address this gap, we evaluated two existing IBF models that were developed by humanitarian agencies for tropical cyclones in the Philippines and Bangladesh: a statistical machine learning model and an elementary damage curve model. These represent contrasting approaches…
Spatial econometric modeling of socioeconomic vulnerability and flood impact
As climate-related disasters escalate, particularly in vulnerable communities in the Global South effective risk management strategies become necessary. The objective of this work was to examine the spatial dependencies between socioeconomic vulnerability and flood impacts in Southern Malawi, merging geospatial methods with econometric modeling. The analysis revealed significant spatial dependencies and spillover effects from data in the Unified …
Auditing Flood Vulnerability Geo-Intelligence Workflow for Biases
Geodata, geographical information science (GISc), and GeoAI (geo-intelligence workflows) play an increasingly important role in predictive disaster risk reduction and management (DRRM), aiding decision-makers in determining where and when to allocate resources. There have been discussions on the ethical pitfalls of these predictive systems in the context of DRRM because of the documented cases of biases in AI systems in other socio-technical syst…
Why does community-based disaster risk reduction fail to learn from local knowledge? Experiences from Malawi
It is often taken as given that community-based disaster risk reduction (CBDRR) serves as a mechanism for the inclusion of local knowledge (LK) in disaster risk reduction (DRR). In this paper, through in-depth qualitative analysis of empirical data from Malawi, we investigate the extent to which CBDRR in practice really takes into account LK. This research argues that LK is underutilised in CBDRR and finds that current practice provides a limited…
External stakeholders’ attitudes towards and engagement with local knowledge in disaster risk reduction
Combining UAV Imagery, Volunteered Geographic Information, and Field Survey Data to Improve Characterization of Rural Water Points in Malawi
As the world is digitizing fast, the increase in Big and Small Data offers opportunities to enrich official statistics for reporting on Sustainable Development Goals (SDG). However, survey data coming from an increased number of organizations (Small Data) and Big Data offer challenges in terms of data heterogeneity. This paper describes a methodology for combining various data sources to create a more comprehensive dataset on SDG 6.1.1. (proporti…
Cost-benefit analysis of flood early warning system in the Karnali River Basin of Nepal
The Legitimacy, Accountability, and Ownership of an Impact-Based Forecasting Model in Disaster Governance
The global shift within disaster governance from disaster response to preparedness and risk reduction includes the emergency of novel Early Warning Systems such as impact based forecasting and forecast-based financing. In this new paradigm, funds usually reserved for response can be released before a disaster happens when an impact-based forecast—i.e., the expected humanitarian impact as a result of the forecasted weather—reaches a predefined dan…
The Changing Face of Accountability in Humanitarianism
Over the past two decades, humanitarian conduct has been drifting away from the classical paradigm. This drift is caused by the blurring of boundaries between development aid and humanitarianism and the increasing reliance on digital technologies and data. New humanitarianism, especially in the form of disaster risk reduction, involved government authorities in plans to strengthen their capacity to deal with disasters. Digital humanitarianism now…
Science for Loss and Damage. Findings and Propositions
The debate on “Loss and Damage” (L&D) has gained traction over the last few years. Supported by growing scientific evidence of anthropogenic climate change amplifying frequency, intensity and duration of climate-related hazards as well as observed increases in climate-related impacts and risks in many regions, the “Warsaw International Mechanism for Loss and Damage” was established in 2013 and further supported through the Paris Agreement in 2015…
The Legitimacy, Accountability, and Ownership of an Impact-Based Forecasting Model in Disaster Governance
The global shift within disaster governance from disaster response to preparedness and risk reduction includes the emergency of novel Early Warning Systems such as impact based forecasting and forecast-based financing. In this new paradigm, funds usually reserved for response can be released before a disaster happens when an impact-based forecast—i.e., the expected humanitarian impact as a result of the forecasted weather—reaches a predefined dan…
The Changing Face of Accountability in Humanitarianism
Over the past two decades, humanitarian conduct has been drifting away from the classical paradigm. This drift is caused by the blurring of boundaries between development aid and humanitarianism and the increasing reliance on digital technologies and data. New humanitarianism, especially in the form of disaster risk reduction, involved government authorities in plans to strengthen their capacity to deal with disasters. Digital humanitarianism now…
Science for Loss and Damage. Findings and Propositions
The debate on “Loss and Damage” (L&D) has gained traction over the last few years. Supported by growing scientific evidence of anthropogenic climate change amplifying frequency, intensity and duration of climate-related hazards as well as observed increases in climate-related impacts and risks in many regions, the “Warsaw International Mechanism for Loss and Damage” was established in 2013 and further supported through the Paris Agreement in 2015…
Combining UAV Imagery, Volunteered Geographic Information, and Field Survey Data to Improve Characterization of Rural Water Points in Malawi
As the world is digitizing fast, the increase in Big and Small Data offers opportunities to enrich official statistics for reporting on Sustainable Development Goals (SDG). However, survey data coming from an increased number of organizations (Small Data) and Big Data offer challenges in terms of data heterogeneity. This paper describes a methodology for combining various data sources to create a more comprehensive dataset on SDG 6.1.1. (proporti…
Cost-benefit analysis of flood early warning system in the Karnali River Basin of Nepal
The Legitimacy, Accountability, and Ownership of an Impact-Based Forecasting Model in Disaster Governance
The global shift within disaster governance from disaster response to preparedness and risk reduction includes the emergency of novel Early Warning Systems such as impact based forecasting and forecast-based financing. In this new paradigm, funds usually reserved for response can be released before a disaster happens when an impact-based forecast—i.e., the expected humanitarian impact as a result of the forecasted weather—reaches a predefined dan…
The Changing Face of Accountability in Humanitarianism
Over the past two decades, humanitarian conduct has been drifting away from the classical paradigm. This drift is caused by the blurring of boundaries between development aid and humanitarianism and the increasing reliance on digital technologies and data. New humanitarianism, especially in the form of disaster risk reduction, involved government authorities in plans to strengthen their capacity to deal with disasters. Digital humanitarianism now…
External stakeholders’ attitudes towards and engagement with local knowledge in disaster risk reduction
Why does community-based disaster risk reduction fail to learn from local knowledge? Experiences from Malawi
It is often taken as given that community-based disaster risk reduction (CBDRR) serves as a mechanism for the inclusion of local knowledge (LK) in disaster risk reduction (DRR). In this paper, through in-depth qualitative analysis of empirical data from Malawi, we investigate the extent to which CBDRR in practice really takes into account LK. This research argues that LK is underutilised in CBDRR and finds that current practice provides a limited…
Auditing Flood Vulnerability Geo-Intelligence Workflow for Biases
Geodata, geographical information science (GISc), and GeoAI (geo-intelligence workflows) play an increasingly important role in predictive disaster risk reduction and management (DRRM), aiding decision-makers in determining where and when to allocate resources. There have been discussions on the ethical pitfalls of these predictive systems in the context of DRRM because of the documented cases of biases in AI systems in other socio-technical syst…
Framing of disaster impact in online news media
Introduction High-quality impact data is essential for several applications in disaster risk management including Early Warning Systems. Currently, most impact data have spatial and temporal gaps, especially in data-poor contexts. Local news media reporting on disasters can contain information to bridge these gaps. However, each news media outlet frames disasters differently, especially since disasters diffuse in time and space. This study addres…
Towards optimal anticipatory action
Evaluating impact-based forecasting models for tropical cyclone anticipatory action
Impact‐based Forecast (IBF) is increasingly adopted for Anticipatory Action in disaster risk management, yet systematic comparison of the diverse models in use remains limited. To address this gap, we evaluated two existing IBF models that were developed by humanitarian agencies for tropical cyclones in the Philippines and Bangladesh: a statistical machine learning model and an elementary damage curve model. These represent contrasting approaches…
Spatial econometric modeling of socioeconomic vulnerability and flood impact
As climate-related disasters escalate, particularly in vulnerable communities in the Global South effective risk management strategies become necessary. The objective of this work was to examine the spatial dependencies between socioeconomic vulnerability and flood impacts in Southern Malawi, merging geospatial methods with econometric modeling. The analysis revealed significant spatial dependencies and spillover effects from data in the Unified …
How to better link landslide inventory mapping with loss and damage reporting
Landslides are inherent multi-hazard events, with different types of triggering mechanisms and due to their widely varying characteristics. Landslides are very underrepresented in global disaster databases, due to their high frequency and relatively low impacts, where individual events might often not pass the threshold for inclusion in the database. As a result, their accumulated effect is underestimated. In relation to the Early Warnings for Al…
Planning ahead
Agricultural communities worldwide face escalating climate hazards that strain traditional relief schemes, underscoring the urgency of data-driven anticipatory action. We implement a spatially explicit, time-phased mixed-integer linear program that allocates anticipatory cash and in-kind items to farmers across thousands of micro-regions while respecting equity, budget, and capacity constraints. Built on an open-source pipeline combining Python, …
Geography (9 works) · Business (8 works) · Disaster Management and Resilience (8 works) · Flood Risk Assessment and Management (8 works) · Computer Science (7 works) · Political science (7 works) · Flood myth (5 works) · Environmental planning (4 works) · Public relations (4 works) · Sociology (4 works)