Urban Flood-Risk Assessment: Integration of Decision-Making and Machine Learning

Urban flood-risk mapping is an important tool for the mitigation of flooding in view continuing urbanization and climate change. However, many developing countries lack sufficiently detailed data to produce reliable risk maps with existing methods. Thus, improved methods are needed that can help managers decision makers combine more soft semi-subjective data, such as citizen observations flood-prone vulnerable areas settlements. we present innovative approach using Analytic Hierarchy Process (AHP), which integrates both subjective objective assessments, organize problem framework. This involves measuring consistency makers’ judgments, generating pairwise comparisons choosing a solution, considering criteria sub-criteria evaluate possible options. An urban map was created according vulnerabilities hazards different classification regression-tree models, serve first stage advancing approaches allocating warning forecasting systems. The findings show machine-learning efficient flood zoning. Using city Rasht Iran, it shown distance rivers, drainage density, most significant parameters influence hazards. Similarly, vulnerability, population land use, dwelling quality, household income, cultural heritage, medical centers hospitals factors. integrated technique outlined study shows credible results be obtained without complicated modeling costly field surveys. proposed method especially helpful little describe display makers.