Modeling of individual debris flows based on DEMNAS using Flow-R: A case study in Sigi, Central Sulawesi

Abstract

On 2018 September 28, 18:03 a local time (10:03 am UTC), the Mw 7.5 earthquake with a focal depth of about 20 km devastated the Palu region in Central Sulawesi, Indonesia resulting in a catastrophic disaster and many casualties. The Palu earthquakes triggered widespread landslides upstream, contributing to the sizeable material volume accumulated in rivers and mountain slopes. After the Palu earthquake, from September 28, 2018, until December 2021, at least 24 events of debris floods have occurred, which have spread to 15 villages. As of late, the empirical debris flow model Flow-R, software for susceptibility mapping of debris flows at a regional scale, was published. While Flow-R's applicability on a regional scale has been confirmed in several studies, the calibrated case using back-analysis of individual debris flow events in Indonesia based on DEMNAS with a spatial resolution of 8.3 m has never been conducted. Local debris flows modeling using Flow-R was evaluated with three well-documented debris flow events on the break slopes on the west and east sides of the Palu Valley. Quantitative analysis was carried out in this study to assess the accuracy, positive predictive value, and negative predictive value of models. First, the result shows the individual back-analysis model of debris flows found good agreement between debris-flow paths predicted and documented debris flow path extent. However, the parameters for rheological properties and erosion rate required in the software are limited. Second, the quantitative analysis shows accuracy, positive, and negative predictive value, which varies considerably. Based on the study, Flow-R is not suitable for comprehensive hazard mapping but provides a direct information about possible run-outdebris flow paths. Furthermore, lateral spreading and friction of Flow-R model results can be used to calibrate the process with rheological properties and erosion rate in other numerical modeling software, either for forward or back analysis.

Modeling Shallow Landslide Runout Distance in Eocene Flysch Facies Using Empirical–Statistical Models (Western Black Sea Region of Türkiye)

“…Since low-resolution data were used, the modified Holmgren algorithm [42] was used in this study. Putra et al [53] stated that they also chose this algorithm because it is less sensitive to the DEM resolution. The SFLM is the second key part of the propagation stage.…”

“…User-defined initiations considering flow accumulation and slope [46], landslide susceptibility map [48], and Sentinel images [53]…”

“…The multiple flow direction [40], Holmgren [41], modified Holmgren [42], cellular automata [43], and random walk [44,45] models provide opportunities to simulate possible runout paths. A combination of the empirical-statistical simplified friction-limited model (SFLM), which considers the reach angle, and flow direction algorithms, enables us to observe details such as feasible runout distances and paths using Flow-R software [42], as shown in recent studies [39,[46][47][48][49][50][51][52][53][54]. Owing to the development of computer technology, recent studies on landslide runout distance prepared with software go well beyond the conventional techniques.…”

“…Flow-R software, which was developed at the University of Lausanne [42], was used to empirically examine shallow landslide runout scenarios in this study. This software offers researchers the opportunity to try a variety of landslide runout simulations [39,[46][47][48][49][50][51][52][53][54][60][61][62][63] to obtain runout distances by considering different parameter configurations. Table 1 provides a summary of previous Flow-R studies.…”

Machine Learning for Landslide Susceptibility Mapping Using Phyton in Sigi Biromaru Area (Near Palu), Central Sulawesi, Indonesia

“…The Mw 7.5 'supershear' earthquake attacked Palu-Central Sulawesi and its surroundings on 28 September 2018 [1], causing a catastrophic disaster [2]. The earthquake was associated with massive tsunami and liquefaction [1], and it triggered landslides and debris flows [2], [3] in seventy-seven hilly areas in Palu, Donggala, and Sigi [3]. This study focused on landslide study, especially in landslide susceptibility mapping (LSM).…”