Performance Analysis of Machine Learning Algorithms for Landslide Prediction
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-3250-4_18
Reference22 articles.
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2. Juyal A, Sharma S (2021) A study of landslide susceptibility mapping using machine learning approach. In: Proceedings of the 3rd international conference on intelligent communication technologies and virtual mobile networks, ICICV 2021, February. Institute of Electrical and Electronics Engineers Inc., pp 1523–1528. https://doi.org/10.1109/ICICV50876.2021.9388379
3. Hartomo KD, Yulianto S, Maruf J (2017) Spatial model design of landslide vulnerability early detection with exponential smoothing method using Google API. In: Proceedings - 2017 international conference on soft computing, intelligent system and information technology: building intelligence through IOT and big data. ICSIIT 2017 2018-January (July). Institute of Electrical and Electronics Engineers Inc., pp 102–106. https://doi.org/10.1109/ICSIIT.2017.37
4. Sun Q, Zhang L, Ding XL, Hu J, Li ZW, Zhu JJ (2015) Slope deformation prior to Zhouqu, China landslide from InSAR time series analysis. Remote Sens Environ 156(1):45–57. https://doi.org/10.1016/j.rse.2014.09.029
5. Ayalew L, Yamagishi H, Ugawa N (2004) Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides 1(1):73–81. https://doi.org/10.1007/s10346-003-0006-9
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