Experimental investigation of a catastrophic landslide in northern Pakistan

Riaz, Saima; Wang, Gonghui; Basharat, Muhammad; Takara, Kaoru

doi:10.1007/s10346-019-01216-5

Cited by 19 publications

(4 citation statements)

References 19 publications

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“…The district Muzaffarabad is located in the NW Himalayas, Pakistan, and natural hazards have severely impacted the area (e.g. landslides), which have been exacerbated by climate change and human activity, as well as earthquakes (Riaz et al, 2019). The region has a humid to subtropical highland climate, with minimum and maximum temperatures around 23°C and 35°C in July and 3°C and 16°C in January, respectively.…”

Section: Study Areamentioning

confidence: 99%

Assessing the effectiveness of alternative landslide partitioning in machine learning methods for landslide prediction in the complex Himalayan terrain

Riaz

Basharat

Brunetti

2022

Progress in Physical Geography: Earth and Environment

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Several devastating landslides have occurred in the NW Himalayas, which has prompted several researchers to strive for improvement in landslide susceptibility modelling (LSM) methodologies. This research analyzes the effectiveness of alternative landslide partitioning techniques on LSM in the landslide-prone district, Muzaffarabad, Pakistan. We developed a landslide inventory of 961 landslides and then traditionally divided it into training (672; 70%) and testing (289; 30%) samples. These training samples (672) are processed by the Average Nearest Neighbour Index (ANNI) method to estimate the spatial pattern of landslides in nature. The results provide an ANNI ratio of 0.672 confirming that the landslides distribution pattern is cluster in the complex Himalayan terrain of Muzaffarabad. Among 672, the majority of landslides (529; 79%) depict cluster behaviour, while 189 landslides (21%) depict random behaviour. To evaluate the effectiveness of landslide cluster samples in prediction, five machine learning algorithms (MLAs), that is, K-Nearest Neighbour (KNN), Naïve Bayes (NB), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Logistic Regression (LR) using proposed cluster (529) and traditional (672) random training samples along with 17 geo-environmental factors are executed. However, testing samples (289; 30%) separated at the initial stage remained the same to check the model’s effectiveness. The areas under the curve (AUC-ROC), sensitivity, specificity, Kappa index and accuracy (ACC) have been used to evaluate the MLA’s performances. An alternative partitioning technique (cluster) shows the highest predictive power with AUC-ROC values ranging from 0.96 to 0.86, Kappa index ranges from 0.76 to 0.60 and ACC ranges from 0.90 to 0.83. Conversely, the random partitioning approach performs less well with AUC-ROC values ranging from 0.95 to 0.83, Kappa index ranges from 0.70 to 0.49 and ACC ranges from 0.87 to 0.80. In comparison, the RF cluster sampling-based model outperforms the other models and their counterparts. The RF model achieved the highest accuracy (0.902), highest AUC (0.962) and highest Kappa index (0.755) followed by XGboost having ACC (0.885), AUC (0.95) and Kappa index (0.724) employing proposed cluster training samples. However, traditional random training samples yield comparatively low ACC of RF (0.868) and XGboost (0.862). These results confirm that cluster training sampling performs well in obtaining reliable and precise LSMs for complex Himalayan terrain. Although random landslide partitioning for training datasets is seldom utilized in LSM, this study highlights that cluster partitioning for landslide training datasets might be a realistic and reliable approach.

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Section: Study Areamentioning

confidence: 99%

Assessing the effectiveness of alternative landslide partitioning in machine learning methods for landslide prediction in the complex Himalayan terrain

Riaz

Basharat

Brunetti

2022

Progress in Physical Geography: Earth and Environment

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“…Lithological units and structure of F I G U R E 3 Three-dimensional representations of electrical resistivity tomography profiles the region impart a significant contribution to landsliding (Riaz, Basharat, Hameed, Shafique, & Luo, 2018). The prolonged monsoon rainy season causes weathering process, weakening of soil structure, and slope failure Riaz, Wang, Basharat, & Takara, 2019). Khattak, Owen, Kamp, and Harp (2010) reported that most landslides are distributed along roads and rivers, and Riaz et al (2019) concluded that the primary cause is rainfall, which triggered landslides damaging roads, caused road closures, and destruction to infrastructure and loss of human lives.…”

Section: Tectonic and Geological Settingsmentioning

confidence: 99%

“…The prolonged monsoon rainy season causes weathering process, weakening of soil structure, and slope failure Riaz, Wang, Basharat, & Takara, 2019). Khattak, Owen, Kamp, and Harp (2010) reported that most landslides are distributed along roads and rivers, and Riaz et al (2019) concluded that the primary cause is rainfall, which triggered landslides damaging roads, caused road closures, and destruction to infrastructure and loss of human lives. A small syncline is also present at the toe of the landslide.…”

Section: Tectonic and Geological Settingsmentioning

confidence: 99%

An integrated geotechnical and geophysical investigation of a catastrophic landslide in the Northeast Himalayas of Pakistan

et al. 2021

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Landslides are a complex geohazard, and the key parameters that have a prominent effect on their nature are variations in slope angle and fluctuations in groundwater. The focus of this research was to analyse the prevailing conditions of the Danna‐Sahotar landslide in the south of Muzaffarabad district, Sub‐Himalayas, Pakistan, and evaluating how different conditions effect the stability of the landslide with the integration of geotechnical, geophysical, and slope stability analysis. The geotechnical investigations revealed that the surface layer is mainly sandy with clayey and silty content. The two‐dimensional (2D) electrical resistivity tomography (ERT) demarcated the presence of a possible slip surface situated in the portion where the resistivity values clearly change. The ERT of the crown is characterized by low resistivity due to the presence of water seepages, which increase the degree of saturation and pore water pressure hence, leading to the slope failure. It was proved that the presence of high moisture content has played an influential role in triggering the landslide. Thorough analysis of integrated research yielded reliable and accurate information on the geotechnical and geological aspects of the landslide. The outcomes were used in slope stability limit equilibrium method (LEM) analysis using GeoStudio software. The slip surface predicted from the LEM was almost circular, which was confirmed by the geotechnical and ERT method. The factor of safety Fs (0.830) along the longitudinal profile of the landslide was calculated, indicating that the slopes along the profile are unstable. The geotechnical survey in combination with ERT and slope stability analysis helped to understand the failure mechanism of the landslide and to improve the layout of the future monitoring experiment, which may be used as a key for the observation of similar slope investigation.

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“…1) constitutes a region of particular geomorphological relevance in terms of landsliding. The rugged topography, combined with extreme climatic events (Riaz et al, 2019), strong seismicity (Ray et al, 2009;Shafique et al, 2016) and anthropogenic influence (Rahman et al, 2014) result in a unique landscape prone to landslides. This is also confirmed in terms of raw numbers, as 30% of the world's landslides occur in this region (Atta-ur- Rahman et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Standing on the shoulder of a giant landslide: an InSAR look at a slow-moving hillslope under melting glaciers in the western Karakoram

Ahmad¹,

Sãdhasivam²,

Lisa³

et al. 2022

Preprint

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Understanding the cascading effects of glacier melting in terms of large slope deformation in high mountainous areas could come from the use of Interferometric Synthetic Aperture Radar (InSAR) techniques. In this work, we investigate a slow moving, extremely large landslide (~20 km2) in the Chitral region in Northern Pakistan, which threatens several villages. Our InSAR analyses, using Sentinel-1 data which span a period of six years, allowed us to retrieve the spatio-temporal pattern of downslope deformation for both ascending and descending orbits. The results highlight a worrying situation where the crown of the landslide is moving relatively fast (from 36 to 80 mm/yr). Several sackung-type of movements and other signs of instability were observed in many locations over the crown. As for the toe of the large landslide, a western sliding sector offers a different mechanism than its eastern counterpart where the deformation appears to accumulate through time. This brief description has two implications. One from the most practical perspective, as it calls for further studies and attention from local administrations. And, it also scientifically highlights the strength of InSAR when it comes to unveiling slow deformation regimes, which may be invisible through the eye of other techniques, although they may still lead to catastrophic failures. Such considerations can be even framed beyond the scale explored in this manuscript. In ither words, the same mechanism and threat to local communities can be present across the whole Hindukush-Himalayan-Karakoram range, where glaciers are widely receding due to climate change.

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Experimental investigation of a catastrophic landslide in northern Pakistan

Cited by 19 publications

References 19 publications

Assessing the effectiveness of alternative landslide partitioning in machine learning methods for landslide prediction in the complex Himalayan terrain

Assessing the effectiveness of alternative landslide partitioning in machine learning methods for landslide prediction in the complex Himalayan terrain

An integrated geotechnical and geophysical investigation of a catastrophic landslide in the Northeast Himalayas of Pakistan

Standing on the shoulder of a giant landslide: an InSAR look at a slow-moving hillslope under melting glaciers in the western Karakoram

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