The Value of Using Multiple Hydrometeorological Variables to Predict Temporal Debris Flow Susceptibility in an Alpine Environment

Abstract. On 15–16 December 1999, heavy rainfall severely stroke Campania region (southern Italy), triggering numerous debris flows on the slopes of the San Martino Valle Caudina-Cervinara area. Soil slips originated within the weathered volcaniclastic mantle of soil cover overlying the carbonate skeleton of the massif. Debris slides turned into fast flowing mixtures of matrix and large blocks, downslope eroding the soil cover and increasing their original volume. At the base of the slopes, debris flows impacted on the urban areas, causing victims and severe destruction (Vittori et al., 2000). Starting from a recent study on landslide risk conditions in Campania, carried out by the Regional Authority (PAI –Hydrogeological setting plan, in press), an evaluation of the debris-flow susceptibility has been performed for selected areas of the above mentioned villages. According to that study, such zones would be in fact characterised by the highest risk levels within the administrative boundaries of the same villages ("HR-zones"). Our susceptibility analysis has been performed by applying SCIDDICA S3–hex – a hexagonal Cellular Automata model (von Neumann, 1966), specifically developed for simulating the spatial evolution of debris flows (Iovine et al., 2002). In order to apply the model to a given study area, detailed topographic data and a map of the erodable soil cover overlying the bedrock of the massif must be provided (as input matrices); moreover, extent and location of landslide source must also be given. Real landslides, selected among those triggered on winter 1999, have first been utilised for calibrating SCIDDICA S3–hex and for defining "optimal" values for parameters. Calibration has been carried out with a GIS tool, by quantitatively comparing simulations with actual cases: optimal values correspond to best simulations. Through geological evaluations, source locations of new phenomena have then been hypothesised within the HR-zones. Initial volume for these new cases has been estimated by considering the actual statistics of the 1999 landslides. Finally, by merging the results of simulations, a deterministic susceptibility zonation of the considered area has been obtained. In this paper, aiming at illustrating the potential for debris-flow hazard analyses of the model SCIDDICA S3–hex, a methodological example of susceptibility zonation of the Vallicelle HR-zone is presented.

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Debris flow susceptibility assessment based on an empirical approach in the central region of South Korea

Geomorphology ◽

10.1016/j.geomorph.2018.01.025 ◽

2018 ◽

Vol 308 ◽

pp. 1-12 ◽

Cited By ~ 10

Author(s):

Sinhang Kang ◽

Seung-Rae Lee

Keyword(s):

South Korea ◽

Debris Flow ◽

Central Region ◽

Empirical Approach ◽

Susceptibility Assessment ◽

Debris Flow Susceptibility

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Comparison of Different Machine Learning Methods for Debris Flow Susceptibility Mapping: A Case Study in the Sichuan Province, China

Remote Sensing ◽

10.3390/rs12020295 ◽

2020 ◽

Vol 12 (2) ◽

pp. 295 ◽

Cited By ~ 6

Author(s):

Ke Xiong ◽

Basanta Raj Adhikari ◽

Constantine A. Stamatopoulos ◽

Yu Zhan ◽

Shaolin Wu ◽

...

Keyword(s):

Machine Learning ◽

Debris Flow ◽

Sichuan Province ◽

Susceptibility Mapping ◽

Boosted Regression Trees ◽

Support Vector ◽

Learning Methods ◽

Machine Learning Methods ◽

Susceptibility Maps ◽

Debris Flow Susceptibility

Debris flow susceptibility mapping is considered to be useful for hazard prevention and mitigation. As a frequent debris flow area, many hazardous events have occurred annually and caused a lot of damage in the Sichuan Province, China. Therefore, this study attempted to evaluate and compare the performance of four state-of-the-art machine-learning methods, namely Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF), and Boosted Regression Trees (BRT), for debris flow susceptibility mapping in this region. Four models were constructed based on the debris flow inventory and a range of causal factors. A variety of datasets was obtained through the combined application of remote sensing (RS) and geographic information system (GIS). The mean altitude, altitude difference, aridity index, and groove gradient played the most important role in the assessment. The performance of these modes was evaluated using predictive accuracy (ACC) and the area under the receiver operating characteristic curve (AUC). The results of this study showed that all four models were capable of producing accurate and robust debris flow susceptibility maps (ACC and AUC values were well above 0.75 and 0.80 separately). With an excellent spatial prediction capability and strong robustness, the BRT model (ACC = 0.781, AUC = 0.852) outperformed other models and was the ideal choice. Our results also exhibited the importance of selecting suitable mapping units and optimal predictors. Furthermore, the debris flow susceptibility maps of the Sichuan Province were produced, which can provide helpful data for assessing and mitigating debris flow hazards.

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