A5051873965- Flood Risk Assessment and Management
- Tropical and Extratropical Cyclones Research
- Hydrology and Watershed Management Studies
- Climate change impacts on agriculture
- Hydrology and Drought Analysis
- Disaster Management and Resilience
- Remote Sensing and LiDAR Applications
- Automated Road and Building Extraction
- Agricultural risk and resilience
- Hydrological Forecasting Using AI
- Water resources management and optimization
- Plant Water Relations and Carbon Dynamics
- Irrigation Practices and Water Management
- Transboundary Water Resource Management
- Economic Growth and Development
- Agricultural Innovations and Practices
- Numerical Methods and Algorithms
- Sustainability and Climate Change Governance
- Rangeland Management and Livestock Ecology
- Soil Mechanics and Vehicle Dynamics
- Smart Agriculture and AI
- Landslides and related hazards
- Microfinance and Financial Inclusion
- Ionosphere and magnetosphere dynamics
- Solar Radiation and Photovoltaics
Vrije Universiteit Brussel
2017-2021
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering
2015-2016
IHE Delft Institute for Water Education
2012
Leuphana University of Lüneburg
2012
Abstract. This paper presents an approach to enhance the role of local stakeholders in dealing with urban floods. The concept is based on DIANE-CM project (Decentralised Integrated Analysis and Enhancement Awareness through Collaborative Modelling Management Flood Risk) 2nd ERANET CRUE funding initiative. main objective was develop test advanced methodology for enhancing resilience communities flooding. Through collaborative modelling, a social learning process initiated that enhances...
Food insecurity is a growing concern due to man-made conflicts, climate change, and economic downturns. Forecasting the state of food essential be able trigger early actions, for example, by humanitarian actors. To measure actual insecurity, expert consensus-based approaches surveys are currently used. Both require substantial manpower, time, budget. This paper introduces an extreme gradient-boosting machine learning model forecast monthly transitions in security Ethiopia, at spatial...
Abstract. Tropical cyclones (TCs) produce strong winds and heavy rains accompanied by consecutive events such as landslides storm surges, resulting in losses of lives livelihoods, particularly regions with high socioeconomic vulnerability. To proactively mitigate the impacts TCs, humanitarian actors implement anticipatory action. In this work, we build upon an existing action for Philippines, which uses impact-based forecasting model housing damage based on eXtreme Gradient Boosting...
Abstract. The relation between drought severity and impacts is complex relatively unexplored in the African continent. This study assesses reported impacts, indices, water scarcity aridity across several counties Kenya. monthly bulletins of National Drought Management Authority Kenya provided impact data. A random forest (RF) model was used to explore which set indices (standardized precipitation index, standardized evapotranspiration soil moisture index streamflow index) best explains on...
Abstract. Reliable information on building stock and its vulnerability is important for understanding societal exposure to floods. Unfortunately, developing countries have less access availability of this information. Therefore, calculations flood damage assessments use the scarce available, often aggregated a national or district level. This study aims improve current by extracting individual characteristics estimate based buildings' vulnerability. We carry out an object-based image...
Abstract. Reliable predictions of the impact natural hazards turning into a disaster is important for better targeting humanitarian response as well triggering early action. Open data and machine learning can be used to predict loss damage houses livelihoods affected people. This research focuses on agricultural loss, more specifically rice in Philippines due typhoons. Regression binary classification algorithms are trained using feature selection methods find most explanatory features. Both...
Abstract. The relation between drought severity, as expressed through widely used indices, and impacts is complex. In particular in water-limited regions where water scarcity prevalent, the attribution of difficult. This study assesses reported impacts, scarcity, aridity across several counties Kenya. monthly bulletins National Drought Management Authority Kenya have been to gather impact data. A Random Forest (RF) model was explore which set indices best explains on: pasture, livestock...
Abstract. Tropical cyclones (TCs) produce strong winds and heavy rains accompanied by consecutive events such as landslides storm surges, resulting in losses of lives livelihoods particularly regions where socioeconomic vulnerability is high. To proactively mitigate the impacts TCs, humanitarian actors implement anticipatory action. In this work, we build upon an existing action for Philippines, which uses impact-based forecasting model housing damage based on XGBoost to release funding...
<p><span><span>Reliable information on building stock and its vulnerability is important for understanding societal exposure to flooding other natural hazards. Unfortunately, this often lacks in developing countries, resulting flood damage assessments that use aggregated collected a national- or district level. In many instances, does not provide representation of the built environment, nor characteristics. </span>This study aims...
<p>Due to its geographical location, the Philippines is highly exposed Tropical Cyclones (TC). Every year at least one TC will make landfall and cause significant humanitarian impact economic loss. To reduce of TC, Philippine Red Cross with German 510, an initiative The Netherlands Cross, designed implemented a Forecast Based Financing (FbF) system. early actions in FbF system are pre-identified be triggered when impact-based forecasting model indicates pre-defined danger level...
The Philippines is one of the countries most at risk to natural disasters. Amongst these disasters, typhoons and its associated landslides, storm surges floods have caused largest impact. Due increased typhoon intensity, country’s high population density in coastal areas rising mean sea levels, flood only expected increase. 510 initiative Netherlands Red Cross uses an Impact Based Forecasting (IBF) model based on machine learning anticipate impact incoming set early action into...
The disaster risk community has notably shifted from a response-driven approach to making informed anticipatory action choices through impact-based forecasting (IBF). Algorithms are being developed and improved increase impact prediction abilities, allow automatic triggers reduce the reliance on human judgement. However, as complexities in modelling algorithms increase, it becomes more difficult for decision makers interpret explain results. This reduces accountability transparency, can lead...
Due to its geographical location, the Philippines is prone tropical cyclones (TC) which produce strong winds, accompanied by heavy rains and flooding of large areas, resulting in casualties human life destruction livelihoods properties. To reduce humanitarian impact TC, Philippine Red Cross with German 510, an initiative The Netherlands Cross, designed implemented a machine learning impact-based forecasting model based on XGBoost, used operationally release funding trigger early action....