Enhancing flash flood risk prediction - A case study from the Assaka watershed, Guelmim Region, Southwestern Morocco.

Talha, Soukaina; Akhssas, Ahmed; Aarab, Abdellatif; Aabi, Ayoub; Berkat, Badr

Since the onset of the Industrial Revolution, significant climatic shifts have led to various environmental imbalances globally, notably increasing the frequency of flash floods, especially in vulnerable regions like the Assaka watershed in southwestern Morocco. This study aims to enhance flash flood risk prediction by integrating machine learning (ML) algorithms with geographic information system (GIS) technology. The random forest (RF) algorithm was employed to analyze over eight million data points, using fourteen predictors categorized into topographic (e.g., altitude, slope, topographic wetness index (TWI)), climatic (e.g., land surface temperature (LST), soil moisture index (SMI)), and geological factors (e.g., drainage density, soil type, lithology). These variables were derived from remotely sensed data and geospatial analyses. The RF model classified the Assaka watershed into five flood susceptibility levels: lowest, low, medium, high, and highest. The results indicated that the most vulnerable areas are near the watershed outlet and the main tributaries, Essayed and Oum Laachar Wadis. These regions are characterized by high land surface temperatures, low drainage density, poor soil moisture, and specific geological conditions, all of which contribute to heightened flood risk. The model's performance was evaluated using multiple metrics, achieving precision (0.968), recall (0.967), accuracy (0.967), F1 score (0.965), Kappa statistic (0.839), and an AUC of 1.0, highlighting its robustness and predictive capabilities. The originality of this study lies in its comprehensive integration of ML with GIS to develop a highly reliable flood susceptibility map for the Assaka watershed. This framework addresses existing gaps in flood risk assessment, offering a significant advancement over traditional methods through its use of advanced data-driven modeling techniques. The findings provide essential insights for prioritizing conservation and flood management strategies, contributing to better preparedness against flash floods in the Guelmim region and potentially other similar environments globally.

MOROCCO; GEOGRAPHIC information systems; FLOOD risk; FLOODS; LAND surface temperature; WATERSHEDS; ARTIFICIAL intelligence; GEOSPATIAL data; MACHINE learning; FLOOD forecasting; RANDOM forest algorithms

Ecological Engineering & Environmental Technology (EEET), 2025, Vol 26, Issue 1, p8

2719-7050

Academic Journal

10.12912/27197050/195227