Rainfall-Induced Landslide Prediction Using Machine Learning Models: The Case of Ngororero District, Rwanda

Kuradusenge, Martin; Kumaran, Santhi; Zennaro, Marco

doi:10.3390/ijerph17114147

Cited by 51 publications

(20 citation statements)

References 37 publications

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“…The reservoir water level and rainfall are the main external factors inducing landslide deformation (Chen et al, 2018). The infiltration of rainfall will destroy the structure of the landslide, reduce the stability of the landslide, change the gravity field of the landslide, soften the soil and cause the deformation of the landslide surface, and affect the stability of the landslide (Jeonget al, 2017;Kuradusenge et al, 2020); the fluctuation of reservoir water level will reduce the shear strength of the landslide and also lead the deformation of the landslide (Liao et al, 2019). The influence of rainfall and reservoir water level on slope body is slow and continuous.…”

Section: Prediction Of Fluctuation Term Displacement 421 Determinatio...mentioning

confidence: 99%

Landslide displacement prediction based on CEEMDAN and grey wolf optimized-support vector regression model

et al. 2022

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Landslide prediction is very important and challenging for reducing geological hazards. In the Three Gorges Reservoir area, landslides show stepped deformation due to seasonal rainfall and periodic fluctuation of reservoir water level. The purpose of this study is to use complete ensemble empirical mode decomposition with adaptive noise and grey wolf optimization to support the vector regression method for displacement prediction. Firstly, the cumulative displacement is decomposed by CEEMDAN to obtain both trend term and fluctuation term displacement. Secondly, according to the cumulative displacement, rainfall, and reservoir water level data, the influencing factors related to the displacement of the trend term and the fluctuation term are determined. Then, the GWO-SVR model is used to predict the trend and fluctuation displacement. The final prediction result is obtained by adding the calculated predicted displacement values of each component. The Shuizhuyuan landslide in the Three Gorges Reservoir area, China, was taken as an example, and the long-term displacement data of monitoring point SZY-03 were selected for analysis. The results show that the root mean square error (RMSE) and coefficient of determination (R2) between the measured displacement values and the prediction values were 0.9845 and 0.9964, respectively. The trained model has high computational accuracy, which proves that the GWO-SVR model can be used for displacement prediction of this type of landslide in the Three Gorges Reservoir area.

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Section: Prediction Of Fluctuation Term Displacement 421 Determinatio...mentioning

confidence: 99%

Landslide displacement prediction based on CEEMDAN and grey wolf optimized-support vector regression model

et al. 2022

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“…Steep slopes in several regions characterize Gakenke district where the slope angle can be more than 45 degrees. Other studies such as [51,63] used 5 slope classes, whereas in this study, we grouped the first two classes because landslide cases are very few in areas with slopes less than 15%. According to the data source [71,72], the slopes are categorized in four classes as indicated by Figure 3(c).…”

Section: Slopementioning

confidence: 99%

“…The activity took different durations depending on the site. The limit of rainfall simulation and duration was based on the rainfall events that induced landslides recently (Figure 4) or in the past [51]. Out of twentynine (29) experimental tests carried out on 11 plots, sixteen of them (55.5%) resulted in slope failure (or crack) as shown in Table 4.…”

Section: Simulationmentioning

confidence: 99%

“…Others used empirical field tests [39,40] or numerical modelling [3,41], and various studies conducted on LEWS have generally used historical rainfall data together with landslide occurrence records to determine the rainfall threshold inducing landslides [34,[42][43][44][45][46][47][48]. The studies conducted in [22,[49][50][51][52][53][54][55][56] have shown that they have used antecedent rainfall as a key parameter that has a great impact on landslides' occurrence. In this research paper, the parameters considered are rainfall intensity together with soil moisture content (SMC) that can help to achieve a better landslide prediction modelling tool that might be used for LEWS.…”

Section: Introductionmentioning

confidence: 99%

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Experimental Study of Site-Specific Soil Water Content and Rainfall Inducing Shallow Landslides: Case of Gakenke District, Rwanda

et al. 2021

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Shallow landslides are among the natural threats causing death and damage. They are mostly triggered by rainfall in mountainous areas where precipitation used to be abundant. The amount of rainfall inducing this natural threat differs from one site to another based on the geographical characteristics of that area. In addition to the rainfall depth, the determination of soil water content in a specific zone has a major contribution to the landslide prediction and early warning systems. Rwanda being a country with hilly terrains, some areas are susceptible to both rainfall and soil water content inducing landslides. But an analytical study of the physical threshold determination of both rainfall and soil water content inducing landslides is lacking. Therefore, this experimental study is conducted to determine the rainfall and soil water content threshold that can be fed in to the landslide early warning system (LEWS) for alert messages using the Internet of Things (IoT) technology. Various experiments have been conducted for the real-time monitoring of slope failure using the toolset composed of a rain gauge, soil moisture sensors, and a rainfall simulating tool. The results obtained show that the threshold for landslide occurrence does not solely correlate with the total rainfall amount (or intensity) or soil moisture, but also influenced by internal (geological, morphological) and environmental factors. Among the sampled sites, the sites covered by forest indicated no sign of slope failure, whereas sites with crops could slip. The experiments revealed that for a specific site, the minimum duration to induce slope failure was 8 hours, 41 minutes with the rainfall intensity of 8 mm/hour, and the soil moisture was above 90% for deeper sensors. These values are used as thresholds for LEWS for that specific site to improve predictions.

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“…In many regions, antecedent precipitation is an important aspect in the triggering of landslides, e.g., by increasing the soil moisture and therefore, reducing soil strength. Since no detailed information on antecedent periods for landslide-triggering events in Austria are available, a common length for an antecedent period for Central European regions has been applied from literature (Jemec and Komac 2012, Rahardjo et al 2019, Kuradusenge et al 2020). Based on these studies, a threshold of 5 days has been de ned and implemented in this analysis (BMLRT 2019).…”

Section: Merging the Torrential Event Database The Meteorological Database And The Lithological Databasementioning

confidence: 99%

The Importance of Hail Events for Landslides and Bedload Transport in Austrian Alpine Torrents

Schritter

Glade

2021

Preprint

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Landslides and bedload transport can be a threat to people, infrastructure, and vegetation. Many detailed hydrometeorological trigger mechanisms of such natural hazards are still poorly understood. This is in particular valid concerning hail as a trigger of these processes. Therefore, this study aims to determine the influence of hail on landslides and bedload transport in alpine torrents. Based on a generated table from an event register of mountain processes maintained by the Avalanche and Torrent Control Unit (WLV) and weather data provided by the Centre for Meteorology and Geodynamics (ZAMG), 1,573 observed events between 1980 and 2019 in 79 Austrian alpine sites are analysed. Thiessen polygons are used to regionalise local weather data to adjacent regions. The spatial extend of these regions are merged with the registered torrential events. As a result of a stepwise filtering of the used data, the final inventory was created.The results show that 95.1% of the investigated torrential processes triggered by hailstorms are debris flows or debris flow-like transports. Within the study period, a peak of hail-triggered landslides and bedload transport can be recognised in the first 10 days of August in all 39 years. Furthermore, the results suggest that hail is rather a direct than an indirect trigger for landslides and bedload transport.Overall, we conclude that the influence of hail on landslides and bedload transport is significant. Respective hydrometeorological triggering conditions should be included in any regions. Further research for this topic is required to explore the process dynamics in greater detail.

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Rainfall-Induced Landslide Prediction Using Machine Learning Models: The Case of Ngororero District, Rwanda

Cited by 51 publications

References 37 publications

Landslide displacement prediction based on CEEMDAN and grey wolf optimized-support vector regression model

Landslide displacement prediction based on CEEMDAN and grey wolf optimized-support vector regression model

Experimental Study of Site-Specific Soil Water Content and Rainfall Inducing Shallow Landslides: Case of Gakenke District, Rwanda

The Importance of Hail Events for Landslides and Bedload Transport in Austrian Alpine Torrents

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