Landslide Susceptibility Mapping Using Machine Learning Algorithms for Nainital, India

Rane, Pranav; Vincent, Shweta

doi:10.30919/es8d600

Cited by 11 publications

(6 citation statements)

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“…Nowadays, material characterization methods under a big data environment and several diseases such as Parkinson’s disease, cardiovascular disease, or coronavirus infection have been analyzed by making use of machine learning through artificial intelligence. , Similarly, by implementing computational science and making use of mathematical models, one can solve any scientific problems easily and also can prove or support an experimental result . Using DFT calculations, some of our experimental findings have also been investigated by computational methods.…”

Section: Results and Discussionmentioning

confidence: 99%

Sugar-Based Phase-Selective Supramolecular Self-Assembly System for Dye Removal and Selective Detection of Cu²⁺ Ions

et al. 2022

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Simple, effective, and eco-friendly sugar-based phase-selective gelators were synthesized at a low cost. They showed high gelling ability toward a wide range of solvents at lower concentrations (minimum gelation concentration ∼0.3%). Preliminary tests reveal that these low molecular weight organogelators can immediately and phase-selectively gel benzene, toluene, petrol, and kerosene in water at room temperature. We also identified G13 in toluene as the good gelator, and the corresponding organogel proficiently removes water-soluble dyes from their concentrated aqueous solutions. This efficient removal of toxic organic solvents and dyes from water suggests promising applications in removing organic substances from contaminated water resources. The thermoreversible gel exhibits effective rechargeability up to five cycles of burning and gelation, which imply the flame stability of the gel. Interestingly, these compounds had a high detection ability toward Cu2+ ions with a state change from gel to the solution. The physical justification for gelation mechanisms and the molecular interaction with metal ions were further confirmed by computational studies.

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Section: Results and Discussionmentioning

confidence: 99%

Sugar-Based Phase-Selective Supramolecular Self-Assembly System for Dye Removal and Selective Detection of Cu²⁺ Ions

et al. 2022

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“…Elevation is one of the major factors controlling the rainfall distribution, vegetation cover, ground water and weathering of rocks 43 . These factors directly or indirectly influence the occurrence of landslides.…”

Section: Methodsmentioning

confidence: 99%

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

Pyakurel,

K.C.,

Dahal

2024

Sci Rep

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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.

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“…In addition, different lithological formations have various shear stresses, water transmission patterns, and other susceptibility properties. Therefore, lithology has frequently been used in landslide susceptibility studies (Rane, Vincent 2022;Yalcin 2008;Yong et al 2022;Zhou et al 2018).…”

Section: Lithologymentioning

confidence: 99%

A comparison of different machine learning models for landslide susceptibility mapping in Rize (Türkiye)

Bilgilioğlu

2023

Baltica

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The main purpose of this study was to compare the performance and validation of six machine learning models (extreme gradient boosting, random forest, artificial neural network, support vector machine, C4.5 decision tree, and naive Bayes) in landslide susceptibility modelling. The province of Rize, which has the highest rate of landslide events in Türkiye, was chosen as the study area. The conditioning factors (distance to roads, lithology, drainage density, slope, topographic wetness index (TWI), soil depth, distance to rivers, land use, NDVI, plan curvature, elevation, aspect, profile curvature) affecting the landslide were determined using the ReliefF method. A total of 516 landslides were identified for creating models, comparing performance, and validating results. The performance and validation of the models were determined by the receiver operating characteristics (ROC), sensitivity, specificity, accuracy, and kappa index. The results show that the XGBoost model outperforms the other five machine learning models in terms of accuracy and performance and is the most effective model for generating landslide susceptibility maps in Rize (Türkiye).

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Landslide Susceptibility Mapping Using Machine Learning Algorithms for Nainital, India

Cited by 11 publications

References 0 publications

Sugar-Based Phase-Selective Supramolecular Self-Assembly System for Dye Removal and Selective Detection of Cu²⁺ Ions

Sugar-Based Phase-Selective Supramolecular Self-Assembly System for Dye Removal and Selective Detection of Cu²⁺ Ions

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

A comparison of different machine learning models for landslide susceptibility mapping in Rize (Türkiye)

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Landslide Susceptibility Mapping Using Machine Learning Algorithms for Nainital, India

Cited by 11 publications

References 0 publications

Sugar-Based Phase-Selective Supramolecular Self-Assembly System for Dye Removal and Selective Detection of Cu2+ Ions

Sugar-Based Phase-Selective Supramolecular Self-Assembly System for Dye Removal and Selective Detection of Cu2+ Ions

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

A comparison of different machine learning models for landslide susceptibility mapping in Rize (Türkiye)

Contact Info

Product

Resources

About

Sugar-Based Phase-Selective Supramolecular Self-Assembly System for Dye Removal and Selective Detection of Cu²⁺ Ions

Sugar-Based Phase-Selective Supramolecular Self-Assembly System for Dye Removal and Selective Detection of Cu²⁺ Ions