NASA Logo

NTRS

NTRS - NASA Technical Reports Server

Back to Results
Landslide Likelihood Prediction using Machine Learning Algorithms
The supply of electricity via power plants is criticalto the operation of many critical infrastructure systems in mod-ern society. Natural hazards can disrupt the power supply, causepower outages that can halt economic growth, and impede emer-gency response until power is restored. The proposed work aimsto predict the landslides likelihood in these critical infrastructurelocations in the Northeastern USA using integrated databases ofexplanatory variables and machine learning algorithms. First,data related to landslides are obtained and merged, includingtopographic, soil moisture, and precipitation-related data. Fiveregression algorithms, namely: Random Forest, Extreme Gradi-ent Boosting (XGBoost), K-Nearest Neighbor regression (KNN),Linear Support Vector Regressor (SVR), and Linear regression,are utilized to predict the landslide probability and evaluatedon the dataset. The accuracy of the models is assessed by usingstatistical metrics such as mean absolute error (MAE), meansquared error (MSE), and root mean squared error (RMSE).The study results show that Random Forest outperformed othermodels with the mutual information feature selection method.It achieved an MSE of 0.0011 with mutual information-basedfeature selection and an MSE of 0.00157 without feature selection.KNN regressor outperformed the other models with an MSEof 0.00139 with correlation-based information selection. Theproposed landslide identification model with Random Forestalgorithm shows outstanding robustness and great potential intackling the landslide likelihood prediction by employing MLalgorithms.
Document ID
20220017729
Document Type
Conference Paper
Authors
Vasundhara Acharya
(Rensselaer Polytechnic Institute Troy, New York, United States)
Anindita Ghosh
(Rensselaer Polytechnic Institute Troy, New York, United States)
Inwon Kang
(Rensselaer Polytechnic Institute Troy, New York, United States)
Thilanka Munasinghe
(Rensselaer Polytechnic Institute Troy, New York, United States)
Binita Kc
(Adnet Systems (United States) Bethesda, Maryland, United States)
Date Acquired
November 23, 2022
Publication Date
December 17, 2022
Publication Information
Publication:
Subject Category
Earth Resources and Remote Sensing
Meeting Information
Meeting: 2022 IEEE International Conference on Big Data
Location: Osaka
Country: JP
Start Date: December 17, 2022
End Date: December 20, 2022
Sponsors: Institute of Electrical and Electronics Engineers
Funding Number(s)
CONTRACT_GRANT: 80GSFC17C0003
Distribution Limits
Public
Copyright
Portions of document may include copyright protected material.
Technical Review
NASA Peer Committee
No Preview Available