Random Forest-Based Flood Hazard Modeling: Analysis of the Impact of Population Growth and Land Cover Change in the Takkalasi Watershed, South Sulawesi Nur Dwiyanti Utari (a*), Roland Alexander Barkey (b), Andang Suryana Soma (b)
a) Department Of Regional and Development Planning, Graduate Student, Hasanuddin University.
*nurdwiyantiutari[at]gmail.com
b) Faculty of Forestry, Hasanuddin University.
Abstract
Rapid population growth has triggered massive land cover changes, particularly through the conversion of natural areas such as forests and agricultural lands into settlements. These changes significantly reduce the soil^s ability to absorb rainwater, increase surface runoff, and exacerbate flood risks. This study develops a flood vulnerability assessment model using the Random Forest algorithm, considering influencing factors such as rainfall, topography, land cover, drainage capacity, geology, and river proximity. The training data was constructed from historical flood event datasets complemented by spatial predictor variables. The model achieved an accuracy of 85% based on cross-validation, with land cover and rainfall intensity being the most significant predictors.
The objective of this study is to analyze the relationship between population growth, land cover changes, and the increased frequency and intensity of floods, using the Random Forest (RF) machine learning algorithm for flood hazard mapping in the Takkalasi Watershed, South Sulawesi, Indonesia.
The results indicate that rainfall in upstream and downstream areas, flat topography in downstream regions, inadequate drainage capacity, and land cover changes are the dominant factors determining the extent and depth of flooding. The integrated approach developed in this study offers an efficient method for flood risk mapping by utilizing available spatial data, with the potential for application in other watershed areas. In practice, these findings can support the development of risk-based spatial planning and the formulation of effective disaster mitigation strategies, particularly in regions with limited hydrological data availability.
Keywords: Flood modeling, land cover changes, Random Forest, Takkalasi Watershed
