Mapping Flood Hazard in Marinduque, Philippines, Using Maximum Entropy Approach

Flood is a widespread, recurring, and damaging natural hazard across the globe. In the Philippines, yearly heavy rains, monsoons, and typhoons cause flooding that affects millions of Filipinos. Mapping flooding hazards, especially in remote areas of the country, can provide essential inputs to manage and reduce the risk and impact of flooding on different infrastructures, properties, and people. Statistical and machine learning approaches have provided a faster way of producing accurate flood hazard maps without numerous data input and computational requirements from physically based hydrological models. This study uses different geo-environmental variables and maximum entropy, a machine learning approach, to map flood hazards in Marinduque, Philippines. Results showed that elevation, soil texture, drainage density, slope, and annual rainfall are the main predictors of flooding in the province. Also, approximately 4512 hectares in Marinduque were under moderate to very high flooding hazards.