A review of landslides related to the 2005 Kashmir Earthquake: implication and future challenges

Basharat, Muhammad; Riaz, Muhammad Tayyib; Jan, M. Qasim; Xu, Chong; Riaz, Saima

doi:10.1007/s11069-021-04688-8

Cited by 40 publications

(18 citation statements)

References 116 publications

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“…The mechanical behaviour and intrinsic deformity of distinct lithological formations profoundly affect landslide occurrence. According to Basharat et al (2021), lithological units (Figure 5(q)) are influential in triggering landslides in the region.…”

Section: ⅲ Materials and Methodsmentioning

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: ⅲ Materials and Methodsmentioning

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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Section: The Case Of the 2005 Earthquake 21 Overviewmentioning

confidence: 99%

“…Of the earthquakes in the modern era, the earthquake of October 2005 was beyond doubt the most devastating (Basharat et al , 2016; Haris et al , 2019; Naeem et al , 2005; Rehmani et al , 2018). This deadly earthquake occurred on 8 October 2005 in northern parts of Pakistan and Azad Kashmir and measured 7.6 mV on the Richter scale (Basharat et al , 2021; Khan et al , 2021; Tareen et al , 2019). The focal depth of the earthquake was 10 km, and its epicenter was in the north of Muzaffarabad (Azad Kashmir), about 1,190 km north of Islamabad, Pakistan (ADB, 2015; Arshad and Athar, 2013).…”

Section: The Case Of the 2005 Earthquakementioning

confidence: 99%

“…Of the earthquakes in the modern era, the earthquake of October 2005 was beyond doubt the most devastating (Basharat et al, 2016;Haris et al, 2019;Naeem et al, 2005;Rehmani et al, 2018). This deadly earthquake occurred on 8 October 2005 in northern parts of Pakistan and Azad Kashmir and measured 7.6 mV on the Richter scale (Basharat et al, 2021;Khan et The region affected was Khyber Pakhtunkhwa (formerly known as Northern West Frontier Province), and the Northern Punjab region of Pakistan and Kashmir. It affected a 30,000 sq.…”

Section: The Case Of the 2005 Earthquake 21 Overviewmentioning

confidence: 99%

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The role of disaster knowledge management in improving housing reconstruction outcomes: with particular reference to Postearthquake reconstruction in Pakistan

Shaikh

Brown

Enegbuma

2022

IJDRBE

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Purpose Rural building practices, especially in developing communities, are often plagued by inadequate local construction knowledge and a limited understanding of the best building practice guidelines. This has contributed significantly to compounding the effect of significant catastrophic events. The purpose of this paper is to examine the potential impact of disaster knowledge management (DKM) on improving housing resilience and makes particular reference to the 2005 earthquake in rural Pakistan. Design/methodology/approach Our research uses a comprehensive literature review that involves a qualitative approach to research aimed at understanding the 2005 earthquakes, their impacts, reconstruction challenges and DKM. Conventional published journals, articles, previous case studies and books were included. But importantly, to take in relevant local information, the review also took in published government reports, disaster mitigation policy documents, national and international NGOs publications, conference proceedings and news articles. More than 80 research papers and conference proceedings over 21 years, from 2001 to 2021, were analyzed in eight major online databases. These include Google Scholar, Science Direct, Research Gate, Scopus, Jstor, Springer, Emerald and Semantic Scholar. Findings The investigation identified that DKM has an important role to play in capacity building and technical knowledge transmission relating to seismic guidelines aimed at improving housing resilience. Consequently, a theoretical framework was developed, focused primarily on the post-2005 rural reconstruction mechanism and the identification of key challenges to disseminating seismic guidelines effectively in relation to rural construction practices. Originality/value This paper makes an original contribution by developing a DKM framework via the identification of key challenges that need to be addressed, in relation to rural construction practices, generally, but particularly in the Pakistan context.

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“…Due to the complexity of landslide occurrence in terms of the geological conditions, genesis mechanism, and external influencing factors, problems related to accurate landslide warnings cannot be solved fundamentally by purely relying on deformation monitoring [14][15][16]. Numerical simulation techniques have become an effective means to solve these problems.…”

Section: Introductionmentioning

confidence: 99%

Determination of Landslide Displacement Warning Thresholds by Applying DBA-LSTM and Numerical Simulation Algorithms

Dai

et al. 2022

Applied Sciences

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Numerical simulation has emerged as a powerful technique for landslide failure mechanism analysis and accurate stability assessment. However, due to the bias of simplified numerical models and the uncertainty of geomechanical parameters, simulation results often differ greatly from the actual situation. Therefore, in order to ensure the accuracy and rationality of numerical simulation results, and to improve landslide hazard warning capability, techniques and methods such as displacement back-analysis, machine learning, and numerical simulation are combined to create a novel landslide warning method based on DBA-LSTM (displacement back-analysis based on long short-term memory networks), and a numerical simulation algorithm is proposed, i.e., the DBA-LSTM algorithm is used to invert the equivalent physical and mechanical parameters of the numerical model, and the modified numerical model is used for stability analysis and failure simulation. Taking the Shangtan landslide as an example, the deformation mechanism of the landslide was analyzed based on the field monitoring data, and subsequently, the superiority of the DBA-LSTM algorithm was verified by comparing it with DBA-BPNN (displacement back-analysis based on back-propagation neural network); finally, the stability of the landslide was analyzed and evaluated a posteriori using the warning threshold calculated by the proposed method. The analytical results show that the displacement back-analysis based on the machine learning (DBA-ML) algorithm can achieve more than 95% accuracy, and the deep learning algorithm exemplified by LSTM had higher accuracy compared to the classical BPNN algorithm, meaning that it can be used to further improve the existing intelligent inversion theory and method. The proposed method calculates the landslide’s factor of safety (FOS) before the accelerated deformation to be 1.38 and predicts that the landslide is in a metastable state after accelerated deformation rather than in failure. Compared to traditional empirical warning models, our method can avoid false warnings and can provide a new reference for research on landslide hazard warnings.

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A review of landslides related to the 2005 Kashmir Earthquake: implication and future challenges

Cited by 40 publications

References 116 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

The role of disaster knowledge management in improving housing reconstruction outcomes: with particular reference to Postearthquake reconstruction in Pakistan

Determination of Landslide Displacement Warning Thresholds by Applying DBA-LSTM and Numerical Simulation Algorithms

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