Concepts for Improving Machine Learning Based Landslide Assessment

Објеката

Тип
Поглавље у монографији
Верзија рада
објављена
Језик
енглески
Креатор
Miloš Marjanović, Mileva Samardžić Petrović, Biljana Abolmasov, Uroš Đurić
Извор
Natural Hazards GIS-based Spatial Modeling Using Data Mining Techniques, Advances in Natural and Technological Hazards Research, volume 48
Уредник
H. R. Pourghasemi and M. Rossi
Издавач
Springer Nature Switzerland AG 2019
Датум издавања
2019
Опис
The main idea of this chapter is to address some of the key issues that were recognized in Machine Learning (ML) based Landslide Assessment Modeling (LAM). Through the experience of the authors, elaborated in several case studies, including the City of Belgrade in Serbia, the City of Tuzla in Bosnia and Herzegovina, Ljubovija Municipality in Serbia, and Halenkovice area in Czech Republic, eight key issues were identified, and appropriate options, solutions, and some new concepts for overcoming them were introduced. The following issues were addressed: Landslide inventory enhancements (overcoming small number of landslide instances), Choice of attributes (which attributes are appropriate and pros and cons on attribute selection/extraction), Classification versus regression (which type of task is more appropriate in particular cases), Choice of ML technique (discussion of most popular ML techniques), Sampling strategy (overcoming the overfit by choosing training instances wisely), Cross-scaling (a new concept for improving the algorithm’s learning capacity), Quasi-hazard concept (introducing artificial temporal base for upgrading from susceptibility to hazard assessment), and Objective model evaluation (the best practice for validating resulting models against the existing inventory). All of them are followed by appropriate practical examples from one of abovementioned case studies. The ultimate objective is to provide guidance and inspire LAM community for a more innovative approach in modeling.
почетак странице
27
крај странице
58
doi
10.1007/978-3-319-73383-8_2
isbn
978-3-319-73382-1
Просторно покривање
Srbija
Subject
Katastar klizišta, Podložnost, Hazard, Mašinsko učenje, Uzorkovanje, Validacija
Landslide inventory, Susceptibility, Hazard, Machine learning, Sampling, Validation, Cross-scaling
Шира категорија рада
M10
Ужа категорија рада
М14
Је дио
TR36009
Права
Затворени приступ
Лиценца
All rights reserved
Формат
.pdf

Miloš Marjanović, Mileva Samardžić Petrović, Biljana Abolmasov, Uroš Đurić. "Concepts for Improving Machine Learning Based Landslide Assessment" in Natural Hazards GIS-based Spatial Modeling Using Data Mining Techniques, Advances in Natural and Technological Hazards Research, volume 48, Springer Nature Switzerland AG 2019 (2019). https://doi.org/10.1007/978-3-319-73383-8_2

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