Assessing Flood and Landslide Susceptibility Using XGBoost: Case Study of the Basento River in Southern Italy  
[PERSON]  
1 Department of Engineering, University of Basililica, 85100 Potenza, Italy; [EMAIL_ADDRESS] (M.R. ); [EMAIL_ADDRESS] (M.F. ); [EMAIL_ADDRESS] (V.T. );  
[PERSON]  
2 Department for Humanistic, Scientific, and Social Innovation, University of Basilica, 75100 Matera, Italy; [EMAIL_ADDRESS] (H.H.A. ); [EMAIL_ADDRESS] (L.C. ); [PERSON]SS] (G.D.); [EMAIL_ADDRESS] (M.S.) [PERSON]  
2 Department for Humanistic, Scientific, and Social Innovation, University of Basilica, 75100 Matera, Italy; [EMAIL_ADDRESS] (H.H.A. ); [EMAIL_ADDRESS] (L.C. ); [EMAIL_ADDRESS] (G.D.); [EMAIL_ADDRESS] (M.S.) [PERSON]  
2 Department for Humanistic, Scientific, and Social Innovation, University of Basilica, 75100 Matera, Italy; [EMAIL_ADDRESS] (H.H.A. ); [EMAIL_ADDRESS] (L.C. ); [EMAIL_ADDRESS] (G.D.); [EMAIL_ADDRESS] (M.S.) [PERSON]  
2 Department for Humanistic, Scientific, and Social Innovation, University of Basilica, 75100 Matera, Italy; [EMAIL_ADDRESS] (H.H.A. ); [PERSON]S] (L.C. ); [EMAIL_ADDRESS] (G.D.); [EMAIL_ADDRESS] (M.S.) [PERSON]  
2 Department for Humanistic, Scientific, and Social Innovation, University of Basilica, 75100 Matera, Italy; [EMAIL_ADDRESS] (H.H.A. ); [EMAIL_ADDRESS] (L.C. ); [EMAIL_ADDRESS] (G.D.); [EMAIL_ADDRESS] (M.S.) [PERSON]  
2 Department for Humanistic, Scientific, and Social Innovation, University of Basilica, 75100 Matera, Italy; [EMAIL_ADDRESS] (H.H.A. ); [EMAIL_ADDRESS] (L.C. ); [EMAIL_ADDRESS] (G.D.); [EMAIL_ADDRESS] (M.S.) [PERSON]  
1 Department of Engineering, University of Basilica, 85100 Potenza, Italy; [EMAIL_ADDRESS] (M.R. ); [EMAIL_ADDRESS] (M.F. ); [EMAIL_ADDRESS] (V.T. );  
Footnote 1: email: {[PERSON], [PERSON]. Natural hazards can be categorized as geophysical, hydrological, climatological, meteorological, or biological. They may also be categorized as rapid onsets such as flash floods, landslides, earthquakes, and volcanic eruptions as well as slow onsets such as droughs, increasing temperatures, sea level rise, glacial retreat, etc. [3]. Among them, floods and landslides, recognized as rapid-onset disasters, produce the most fatalities because of the rapid accumulation of surface runoff often exacerbated by intense rainfall [4]. Floods are the costliest in terms of economic damage in Europe and one of the most frequent hazards worldwide [5; 6]. According to records from the World Bank, approximately 3.7 million square kilometers of land worldwide are vulnerable to landslides, affecting an estimated 300 million people [2; 7]. In Italy, a total of 50,593 fatalities were recorded across 2580 floods and landslide events between 1279 and 2002 based on the database of both floods and landslides [8; 9]. Floods and landslides are inevitable natural hazards that pose significant threats to human settlements, environment, and economy [7]. The susceptibility map serves as a crucial initial step to predict the potential spatial extents across various scales for adequate planning and effective risk management at mitigating damage and enhancing resilience against them [2; 10; 11]. Various methods, including qualitative, quantitative, and semi-quantitative methods, are employed to assess the susceptibility maps for floods and landslides. In the case of landslides, common methods include inventory-based analyses, physically-based slope models, statistical methods, and knowledge-based approaches [12; 13; 14]. Similarly, flood susceptibility mapping uses multi-criteria decision-making (MCDM) techniques, physically based hydrological models, statistical methods, and knowledge-based approaches [10; 15]. Recently, knowledge-based approaches, particularly machine learning models, have been increasingly applied in susceptibility mapping for both floods and landslides to enable a less complex implementation, testing, and validation with high-performance output compared to the traditional ones [10; 12; 16].