Author(s): Del Piero R. Arana-Ruedas; Edwin Pino-Vargas; Sandra Del Aguila-Rios; Lia Ramos-Fernandez

Linked Author(s):

Keywords: I; Flood; Morphometric Parameters; Peru; SPEI; Spatial Analysis; Tropical Andes; WLC

Abstract: Global warming has altered temperature and precipitation patterns, disrupted the hydrological cycle, and increased the frequency, duration, and severity of extreme events such as droughts and floods. Floods, in particular, cause significant ecological, human, and economic losses, further exacerbated by urbanization and high population density. In the Peruvian Tropical Andes (PTA), agricultural communities rely on rainfed farming systems, making them highly vulnerable to changes in water availability. This study assesses flood susceptibility in the PTA through morphometric analysis of sub-watersheds, the Standardized Precipitation and Evapotranspiration Index (SPEI), and geospatial techniques, while also identifying potential applications of machine learning. The results indicate that several sub-watersheds exhibit high or very high flood susceptibility, highlighting the need for targeted risk mitigation strategies. The study emphasizes the importance of early warning systems, sustainable watershed management, and infrastructure planning to minimize flood-related impacts. While machine learning techniques are discussed as potential tools for predictive modeling, their effective implementation requires comprehensive river discharge datasets. Future research should focus on integrating hydrological modeling with artificial intelligence to enhance flood prediction capabilities and improve resilience in vulnerable regions.

DOI: https://doi.org/10.64697/978-90-835589-7-4_41WC-P2104-cd

Year: 2025