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Tytuł artykułu

Enhancing flash flood risk prediction: A case study from the Assaka watershed, Guelmim region, Southwestern Morocco

Autorzy
Talha Soukaina , Akhssas Ahmed , Aarab Abdellatif , Aabi Ayoub , Berkat Badr
Treść / Zawartość
Pełne teksty:
2_talha_enhancing_eeet_1_2025.pdf
Identyfikatory
DOI
10.12912/27197050/195227
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Słowa kluczowe
EN
Wydawca
Lubelski Oddział Polskiego Towarzystwo Inżynierii Ekologicznej
Lublin University of Technology
WNGB Wydawnictwo Naukowe
Czasopismo
Ecological Engineering & Environmental Technology
Rocznik
2025
Tom
Vol. 26, iss. 1
Strony
8--28
Opis fizyczny
Bibliogr. 109 poz., rys.
Twórcy
autor
Talha Soukaina
soukaina_talha2@um5.ac.ma
  • Laboratory of Applied Geophysics, Geotechnics, Engineering Geology, and Environment, Mohammadia Engineering School, Mohammed V University in Rabat, 765, Av. Ibn Sina, Agdal, Rabat, Morocco
autor
Akhssas Ahmed
akhssas@emi.ac.ma
  • Laboratory of Applied Geophysics, Geotechnics, Engineering Geology, and Environment, Mohammadia Engineering School, Mohammed V University in Rabat, 765, Av. Ibn Sina, Agdal, Rabat, Morocco
autor
Aarab Abdellatif
aarababdellatif@yahoo.com
  • Laboratory for Water Analysis and Modeling of Natural Resources, Mohammadia Engineering School, Mohammed V University in Rabat, BP:765 Ibn Sina Avenue Agdal, Rabat, Morocco
autor
Aabi Ayoub
aabi.ayoub@gmail.com
  • Laboratory for Water Analysis and Modeling of Natural Resources, Mohammadia Engineering School, Mohammed V University in Rabat, BP:765 Ibn Sina Avenue Agdal, Rabat, Morocco
autor
Berkat Badr
badrbarakat05@gmail.com
  • Laboratory of Applied Geophysics, Geotechnics, Engineering Geology, and Environment, Mohammadia Engineering School, Mohammed V University in Rabat, 765, Av. Ibn Sina, Agdal, Rabat, Morocco
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