Runout modeling and calibration of friction parameters of Kurichermala debris flow, India

Abraham, Minu Treesa; Satyam, Neelima; Reddy, Sai Kumar Peddholla; Pradhan, Biswajeet

doi:10.1007/s10346-020-01540-1

Cited by 36 publications

(13 citation statements)

References 53 publications

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“…The forest zones are covered by forest soil with rich organic content, and the riverbanks are formed by thick alluvial deposits. The larger regolith thickness often leads to the bed erosion and bulking of landslides, which increases the landslide volume and destruction potential [16]. The district is highly affected by geohazards such as landslides and floods due to its topographic and geomorphological conditions.…”

Section: Study Areamentioning

confidence: 99%

“…The developed landslide susceptibility map thus provides the probability of occurrence of any of these landslide typologies in the region, and it is not specific for any single landslide typology. The debris flows have very long runout distances [16], and even the locations which are a few kilometers away from the crown points, with entirely different LCFs, were also affected. Hence, using point data for the training and testing of the model might ignore the probability of the occurrence of hazards in the runout zones.…”

Section: Data Collection and Sampling Strategiesmentioning

confidence: 99%

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Factors Affecting Landslide Susceptibility Mapping: Assessing the Influence of Different Machine Learning Approaches, Sampling Strategies and Data Splitting

Abraham

Satyam

Lokesh

et al. 2021

Land

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Data driven methods are widely used for the development of Landslide Susceptibility Mapping (LSM). The results of these methods are sensitive to different factors, such as the quality of input data, choice of algorithm, sampling strategies, and data splitting ratios. In this study, five different Machine Learning (ML) algorithms are used for LSM for the Wayanad district in Kerala, India, using two different sampling strategies and nine different train to test ratios in cross validation. The results show that Random Forest (RF), K Nearest Neighbors (KNN), and Support Vector Machine (SVM) algorithms provide better results than Naïve Bayes (NB) and Logistic Regression (LR) for the study area. NB and LR algorithms are less sensitive to the sampling strategy and data splitting, while the performance of the other three algorithms is considerably influenced by the sampling strategy. From the results, both the choice of algorithm and sampling strategy are critical in obtaining the best suited landslide susceptibility map for a region. The accuracies of KNN, RF, and SVM algorithms have increased by 10.51%, 10.02%, and 4.98% with the use of polygon landslide inventory data, while for NB and LR algorithms, the performance was slightly reduced with the use of polygon data. Thus, the sampling strategy and data splitting ratio are less consequential with NB and algorithms, while more data points provide better results for KNN, RF, and SVM algorithms.

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Section: Study Areamentioning

confidence: 99%

Section: Data Collection and Sampling Strategiesmentioning

confidence: 99%

Factors Affecting Landslide Susceptibility Mapping: Assessing the Influence of Different Machine Learning Approaches, Sampling Strategies and Data Splitting

Abraham

Satyam

Lokesh

et al. 2021

Land

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“…However, they are not beneficial to study governing mechanisms of initiation and failure time of debris flows (Van Asch et al 2014, 2018. Previous modeling efforts, i.e., Kuriakose, Luna, Portugues & Van Westen (2009), Abraham et al (2021), , do not relate the rainfall with debris flow triggering and are not helpful to predict the ID thresholds. To address these research gaps, we modify the modeling framework State of the art studies in the past decades gives an understanding of initiation mechanisms of debris flows.…”

Section: Introductionmentioning

confidence: 99%

“…Earlier, Kuriakose, Luna, Portugues & Van Westen (2009) performed runout modeling of the 2001 debris-flow event in the Kottayam district of Kerala, India, using the DAN3D program (Hungr 1995, Hungr & McDougall 2009. Abraham et al (2021), using the RAMMS program (Christen et al 2010), performed runout modeling of the 2018 Kurichermala debris flows in the Wayanad district of Kerala, India. studied the runout dynamics of the 2013 Tangni debris flow that occurred in Garhwal derive ID thresholds for the Pettimudi debris.…”

Section: Introductionmentioning

confidence: 99%

Mechanisms of Initiation, Runout, and Rainfall Thresholds of Extreme-Precipitation-Induced Debris Flows

Subramanian

Yunus

Jasin

et al. 2021

Preprint

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The frequency of unprecedented extreme precipitation events is increasing, and consequently, catastrophic debris flows occur in regions worldwide. Rapid velocity and long-runout distances of debris flow induce massive loss of life and damage to infrastructure. Despite extensive research, understanding the initiation mechanisms and defining early warning thresholds for extreme-precipitation-induced debris flows remain a challenge. Due to the nonavailability of extreme events in the past, statistical models cannot determine thresholds from historical datasets. Here, we develop a numerical model to analyze the initiation and runout of extreme-precipitation-induced runoff-generated debris flows and derive the Intensity-Duration (ID) rainfall threshold. We choose the catastrophic debris flow on 6 August 2020 in Pettimudi, Kerala, India, for our analysis. Our model satisfactorily predicts the accumulation thickness (7 m to 8 m) and occurrence time of debris flow compared to the benchmark. Results reveal that the debris flow was rapid, traveling with a maximum velocity of 9 m/s for more than 9 minutes. The ID rainfall threshold defined for the event suggests earlier thresholds are not valid for debris flow triggered by extreme precipitation. The methodology we develop in this study is helpful to derive ID rainfall thresholds for debris flows without historical data.

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“…In India, the Himalayan belt [4,[22][23][24] and the Western Ghats [25,26] are highly prone to landslides.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Landslides Using Mobility Functions: A Case Study from Idukki District, India

2021

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Catastrophic landslides and associated destructions are increasing every year, because of the change in climatic conditions and land use patterns. The ecologically sensitive zones of Western Ghats are highly susceptible to landslides and require scientific attention in developing an efficient early warning system.Definition of empirical rainfall thresholds on local, regional or global scales is the most commonly followed method of forecasting rainfall induced landslides. The limitations associated with such thresholds demands for better forecasting performance, incorporating the effect of physical processes in the initiation of landslides. This study is an attempt to forecast landslides in Idukki district, using mobility functions. The function separates the impossible and certain mobilisation parts and forecasts whether landslides can occur or not. Based on the critical value of mobility function, two different warning levels are proposed for four different reference areas in the district. The study shows that the model is 97 % efficient in smaller areas with uniform topographical and geological conditions, and the performance is reduced as the area becomes larger, with varying topographical and geological properties. The model proves to be an effective landslide forecasting tool that can be integrated with a rainfall forecasting system, to develop an early warning system for the region.

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Runout modeling and calibration of friction parameters of Kurichermala debris flow, India

Cited by 36 publications

References 53 publications

Factors Affecting Landslide Susceptibility Mapping: Assessing the Influence of Different Machine Learning Approaches, Sampling Strategies and Data Splitting

Factors Affecting Landslide Susceptibility Mapping: Assessing the Influence of Different Machine Learning Approaches, Sampling Strategies and Data Splitting

Mechanisms of Initiation, Runout, and Rainfall Thresholds of Extreme-Precipitation-Induced Debris Flows

Forecasting Landslides Using Mobility Functions: A Case Study from Idukki District, India

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