Geological Exploration, Landslide Characterization and Susceptibility Mapping at the Boundary between Two Crystalline Bodies in Jajarkot, Nepal

Shahi, Yubraj Bikram; Kadel, Sushma; Dangi, Harish; Adhikari, Ganesh Prasad; KC, Diwakar; Paudyal, Kabi Raj

doi:10.3390/geotechnics2040050

Cited by 6 publications

(6 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The areas around the fault are more prone to landslides as the rocks in the proximity are disturbed and are generally weak. Faults also play a role in the movement of ground water, playing an important role in landslide occurrences 7 . During an earthquake, a fault may act as a barrier for the propagation of seismic waves.…”

Section: Methodsmentioning

confidence: 99%

“…The stability of slopes is directly affected by anthropogenic factors such as road excavations 59 . Due to unplanned and nonengineered road construction, the mountainous regions of Nepal have experienced many landslides 7 . A significant number of landslide occurrences around roads after the 2015 Gorkha earthquake were reported by Mcadoo et al 60 .…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing co-seismic landslide susceptibility, building exposure, and risk analysis through machine learning

Pyakurel,

K.C.,

Dahal

2024

Sci Rep

View full text Add to dashboard Cite

Landslides are devastating natural disasters that generally occur on fragile slopes. Landslides are influenced by many factors, such as geology, topography, natural drainage, land cover, rainfall and earthquakes, although the underlying mechanism is too complex and very difficult to explain in detail. In this study, the susceptibility mapping of co-seismic landslides is carried out using a machine learning approach, considering six districts covering an area of 12,887 km2 in Nepal. Landslide inventory map is prepared by taking 23,164 post seismic landslide data points that occurred after the 7.8 MW 2015 Gorkha earthquake. Twelve causative factors, including distance from the rupture plane, peak ground acceleration and distance from the fault, are considered input parameters. The overall accuracy of the model is 87.2%, the area under the ROC curve is 0.94, the Kappa coefficient is 0.744 and the RMSE value is 0.358, which indicates that the performance of the model is excellent with the causative factors considered. The susceptibility thus developed shows that Sindhupalchowk district has the largest percentage of area under high and very high susceptibility classes, and the most susceptible local unit in Sindhupalchowk is the Barhabise municipality, with 19.98% and 20.34% of its area under high and very high susceptibility classes, respectively. For the analysis of building exposure to co-seismic landslide susceptibility, a building footprint map is developed and overlaid on the co-seismic landslide susceptibility map. The results show that the Sindhupalchowk and Dhading districts have the largest and smallest number of houses exposed to co-seismic landslide susceptibility. Additionally, when conducting a risk analysis based on susceptibility mapping, as well as considering socio-economic and structural vulnerability in Barhabise municipality, revealed that only 106 (1.1%) of the total 9591 households, were found to be at high risk. As this is the first study of co-seismic landslide risk study carried out in Nepal and covers a regional to the municipal level, this can be a reference for future studies in Nepal and other parts of the world and can be helpful in planning development activities for government bodies.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Enhancing co-seismic landslide susceptibility, building exposure, and risk analysis through machine learning

Pyakurel,

K.C.,

Dahal

2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Road distance is also one of the important causative factors considered in the occurrences of landslides. The field study revealed that haphazard road excavation without the consideration of geological and geotechnical features has caused shallow landslides (Budha et al 2016, Shahi et al 2022. The haphazard and unmanaged constructions of the roads built on the slopes cause the loss of support and bring changes in topography causing loss of support which ultimately leads to the increase of strain behind the slope and the development of cracks.…”

Section: Distance From Roadmentioning

confidence: 99%

Landslide susceptibility analysis using frequency ratio and weight of evidence approaches along the Lakhandehi Khola watershed in the Sarlahi District, southern Nepal

et al. 2023

View full text Add to dashboard Cite

Landslide susceptibility maps are considered as one of the most important keys to limiting and dodging potential landslide consequences worldwide. In the present study, landslide susceptibility maps are prepared using bivariate models: frequency ratio and weight of evidence approaches. At first, randomly selected 80% of landslides i.e.,one hundred eighty landslides are used as training data for the preparation of the model, and the rest 20% of landslides i.e.,forty-five landslides for its validation. Similarly,thematic layers of nine causative factors of landslides such as slope, aspect, curvature, stream density, TWI(Topographic Wetness Index), land use, geology, distance from river and distance from the road have been analyzed for the modeling in ArcGIS. Finally, prepared landslide susceptibility maps are classified into five classes from Very Low to Very High from both methods. The area of Low, Moderate, High, and Very High susceptible classes is also nearly equal. The success rate curve of FR (Frequency Ratio), and WOE (Weight of the Evidence), show accuracy of 71.09%, and 75.62% respectively. Likewise, the prediction rate curve shows 72.87% and 76.66% accuracy on FR and WOE methods respectively. Since the susceptibility maps prepared through both approaches show an accuracy of >70%, the result is deliberated as fair. These maps are useful to all the stakeholders for land use planning and developing mitigation strategies against the consequences of increasing landslides in the Siwalik Hills of Nepal.

show abstract

“…The number of causative factors considered in this study is comparatively lower, but the success rates obtained are fine. Increasing the number of causative factors considered does not necessarily increase the model success rate significantly [69,70]. Currently, this paper presents four different models, namely the AHP, MLP, AHP-VW, and MLP-VW models, in which the single model AHP and MLP represent heuristic and statistical models, respectively.…”

Section: Comparison Of Modelsmentioning

confidence: 99%

A Hybrid Variable Weight Theory Approach of Hierarchical Analysis and Multi-Layer Perceptron for Landslide Susceptibility Evaluation: A Case Study in Luanchuan County, China

Guo

Luo

et al. 2023

Sustainability

View full text Add to dashboard Cite

Landslides, which can cause significant losses of lives or property damages, result from several different environmental factors whose influences are very complex. Thus, the statistical multi-layer perceptron (MLP) and heuristic analytical hierarchy process (AHP) are employed in the evaluation of landslide susceptibility. However, the landslide susceptibility maps drawn by these two methods are always affected by subjectivity and randomness. In the present study, we introduce variable weight theory (VW) to improve the MLP and AHP methods, and two novel hybrid models, AHP-VW and MLP-VW, are respectively proposed. VW theory is used to redistribute the weights of the factors in the two constant weight evaluations. This is so that the weights of the factors change with different evaluation units, thereby eliminating the subjectivity and randomness problems. The landslide susceptibility maps of the study area were categorized into very low, low, moderate, high, and very high susceptibility grades. The landslide susceptibility maps of the four models are validated by the receiver operating characteristic (ROC) curve. The area under the curve (AUC) is 0.825 for the AHP model, 0.879 for the MLP model, 0.873 for the AHP-VW model, and 0.915 for the MLP-VW model. The results show that the landslide susceptibility map drawn by statistical MLP is better than that drawn by heuristic AHP, which is consistent with many other current research results. Furthermore, VW can significantly improve the performance of constant-weight single models. Landslide susceptibility maps drawn by the statistical MLP model hybrid VW can be used for regional land use planning and landslide hazard mitigation purposes.

show abstract

Geological Exploration, Landslide Characterization and Susceptibility Mapping at the Boundary between Two Crystalline Bodies in Jajarkot, Nepal

Cited by 6 publications

References 56 publications

Enhancing co-seismic landslide susceptibility, building exposure, and risk analysis through machine learning

Enhancing co-seismic landslide susceptibility, building exposure, and risk analysis through machine learning

Landslide susceptibility analysis using frequency ratio and weight of evidence approaches along the Lakhandehi Khola watershed in the Sarlahi District, southern Nepal

A Hybrid Variable Weight Theory Approach of Hierarchical Analysis and Multi-Layer Perceptron for Landslide Susceptibility Evaluation: A Case Study in Luanchuan County, China

Contact Info

Product

Resources

About