Optimization of causative factors using logistic regression and artificial neural network models for landslide susceptibility assessment in Ujung Loe Watershed, South Sulawesi Indonesia

Andang Suryana Soma, Tetsuya Kubota, Hideaki Mizuno

研究成果: ジャーナルへの寄稿記事

1 引用 (Scopus)

抄録

Landslide susceptibility maps (LSMs) play a vital role in assisting land use planning and risk mitigation. This study aims to optimize causative factors using logistic regression (LR) and an artificial neural network (ANN) to produce a LSM. The LSM is produced with 11 causative factors and then optimized using forward-stepwise LR (FSLR), ANN, and their combination (FSLR-ANN) until eight causative factors were found for each method. The ANN method produced superior validation results compared with LR. The ROC values for the training data set ranges between 0.8 and 0.9. On the other hand, validation with the percentage of landslide fall into LSM class high and very high, ANN method was higher (92.59%) than LR (82.12%). FSLR-ANN with nine causative factors gave the best validation results with respect to area under curve (AUC) values, and validation with the percentage of landslide fall into LSM class high and very high. In conclusion, ANN was found to be better than LR when producing LSMs. The best Optimization was combination of FSLR -ANN with nine causative factors and AUC success rate 0.847, predictive rate 0.844 and validation with landslide fall into high and very high class with 91.30%. It is an encouraging preliminary model towards a systematic introduction of FSLR-ANN model for optimization causative factors in landslide susceptibility assessment in the mountainous area of Ujung Loe Watershed.

元の言語英語
ページ(範囲)383-401
ページ数19
ジャーナルJournal of Mountain Science
16
発行部数2
DOI
出版物ステータス出版済み - 2 1 2019

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neural network
artificial neural network
Indonesia
landslide
logistics
watershed
regression
land use planning
Values
land use
mitigation
planning

All Science Journal Classification (ASJC) codes

  • Global and Planetary Change
  • Geography, Planning and Development
  • Geology
  • Earth-Surface Processes
  • Nature and Landscape Conservation

これを引用

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title = "Optimization of causative factors using logistic regression and artificial neural network models for landslide susceptibility assessment in Ujung Loe Watershed, South Sulawesi Indonesia",
abstract = "Landslide susceptibility maps (LSMs) play a vital role in assisting land use planning and risk mitigation. This study aims to optimize causative factors using logistic regression (LR) and an artificial neural network (ANN) to produce a LSM. The LSM is produced with 11 causative factors and then optimized using forward-stepwise LR (FSLR), ANN, and their combination (FSLR-ANN) until eight causative factors were found for each method. The ANN method produced superior validation results compared with LR. The ROC values for the training data set ranges between 0.8 and 0.9. On the other hand, validation with the percentage of landslide fall into LSM class high and very high, ANN method was higher (92.59{\%}) than LR (82.12{\%}). FSLR-ANN with nine causative factors gave the best validation results with respect to area under curve (AUC) values, and validation with the percentage of landslide fall into LSM class high and very high. In conclusion, ANN was found to be better than LR when producing LSMs. The best Optimization was combination of FSLR -ANN with nine causative factors and AUC success rate 0.847, predictive rate 0.844 and validation with landslide fall into high and very high class with 91.30{\%}. It is an encouraging preliminary model towards a systematic introduction of FSLR-ANN model for optimization causative factors in landslide susceptibility assessment in the mountainous area of Ujung Loe Watershed.",
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