Prediction of Flood-Prone zones based on Cellular Automata in GIS

Abstract

Abstract Due to climate change and rapid urbanization, urban flooding is on the rise, necessitating effective flood control measures in urban areas. Predicting potential flood-prone areas undergoing Land Use (LU) changes could significantly aid in planning for risk reduction and sustainable urban design. However, there's a scarcity of studies that consider both climate change and LU alterations. This research introduces a novel basin-scale framework utilizing a Future LU Simulation (FLUS) model to evaluate disaster-prone areas' risk from 20-year flood scenarios projected for 2040 and 2060. The Markov-FLUS model was developed and validated using historical data from 2000 to 2020. This model was then employed to simulate LU changes from 2020 to 2060 based on natural scenarios. Focusing on Khorasan Razavi as a case study, it investigates the potential consequences of LU transformations due to ongoing urbanization and vegetation changes in connection with predicted environmental shifts. The findings indicate an anticipated increase in accident-prone areas and constructed land in the studied area in the future. Spatially, this heightened flood risk primarily occurs on the periphery of existing developed areas or converted land. This framework's insights into future flood-prone areas' spatio-temporal characteristics offer valuable guidance for implementing rational flood mitigation measures in the most critical regions for development.