Earthquake and Rainfall Induced Landslide Hazard Assessment of Kutupalong Rohingya Camp Using Meteorological and Geological Information

Samm-A, Anika; Kamal, A. S. M. Maksud; Rahman, Md. Zillur

doi:10.21203/rs.3.rs-1818132/v1

Cited by 1 publication

(2 citation statements)

References 13 publications

(1 reference statement)

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“…Das et al ( 2023) [8] demonstrated that experts conducted landslide susceptibility zonation (LSZ) assessment for several regions of this seismically active high mountainous terrain utilizing a variety of methodologies, mostly spanning from conventional subjective views to sophisticated machine learning models. [30] all perform additional study on LSZ . The map combination strategy and the AHP method were consistently used with various area and mapping unit sizes, it has been observed.…”

Section: Related Workmentioning

confidence: 99%

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Prediction of earthquake magnitude and seismic vulnerability mapping using artificial intelligence techniques: a case study of Turkey

Biswas

Kumar

Bera

2023

Preprint

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Earthquake threats can result in fatalities, property destruction, and other cascading effects. Since it is nearly impossible to prevent earthquakes, anticipating the location of future earthquakes and figuring out their likelihood could be very helpful in reducing the seismic threat. In this work, seismic hazard prediction is executed to forecast adverse results using a range of potential artificial intelligence (AI) techniques, including ML and ANN. In the case study, we have looked at Turkey, which was recently and badly damaged by two earthquakes in February 2023. To predict earthquake magnitude, this study used a variety of regression algorithms, including Decision Tree Regressor, Extra-Trees Regressor, Random Forest Regressor, Bayesian Ridge Regressor, and advanced gradient boosting decision tree (GBDT) algorithms such as XGBoost, LightGBM, and CatBoost, as well as three artificial neural networks (ANN). The predicted magnitude and risk zone of an earthquake are mapped using a geographic information system (GIS), and the maps performed well in terms of prediction. The generated maps is showing the expected earthquake risk based on historical data using the statistical computations. The ANN models perform exceptionally well, with R2 scores of 0.99 and 0.98 for training and case study data, respectively, and low values for MSE, MAE, and RMSE. ML models have demonstrated an exceptional ability to properly generalize from a single dataset, which implies they can accurately anticipates results for new and untested data. The results would be helpful to many local emergency preparedness and infrastructure planning organizations.

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Section: Related Workmentioning

confidence: 99%

“…According to the investigations, the more points that lie along the x=y (red line), the more accurate the model is (in Figure [29]). Figure [30] displays the error function profile (Predicted value vs Actual value) for ANN-3. This graph is useful in determining the profile of the error function.…”

Section: Evaluation Of Ann-1mentioning

confidence: 99%