Integrated disaster risk management and urban planning for flood resilience a methodology using loosely coupled machine learning and geographic information system - Cruz, Gene Oliver B.; Ballesteros, Florencio C. Jr.; Blanco, Ariel C. (Quezon City College of Engineering, University of the Philippines Diliman, 2017)
Cities are becoming more vulnerable to natural hazards due to increasing concentration of urban population and resources as well as to changing weather patterns caused by climate change. Adding to the aggravation of urban vulnerability is the socioeconomic conditions of its population. More often, the poor are those who are severely affected and have no economic means to recover. Eventually, the local government takes the responsibility of providing services to restore and rehabilitate affected communities. This impacts the cities' economic base by reducing their ability to grow and raise revenue. To minimize potential economic losses caused by a disaster, it is important to assess the communities' vulnerabilities and plan before a disaster strikes. This paper explores an integrated urban planning and disaster risk management approach using machine learning techniques loosely coupled to geographic information system (GIS). Drawing the strengths of various methodologies such genetic algorithms for optimizing resilience, random forests for formulating transition rules, and GIS for directly and vividly reflecting the changes in geospatial state over various temporal stages, the research was able demonstrate a new approach to disaster risk management that incorporates city planning intervention to evaluate past, present and future outcomes. The research concluded the added value of integrating disaster risk management with other fields to holistically understand urban risks, and identify and prioritize the type of risk mitigation measures.
Thesis (Ph.D. Environmental Engineering)--University of the Philippines Diliman
Keywords: Emergency management; City planning; Geospatial data; Resilience optimization