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Journal of Mountain Science

, Volume 16, Issue 2, pp 383–401 | Cite as

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 SomaEmail author
  • Tetsuya Kubota
  • Hideaki Mizuno
Article

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.

Keywords

Optimized causative factor Landslide Logistic Regression Artificial neural network Indonesia Notation 

Notation

P

probability of landslide occurrence

Z

value of landslide causative factor

C0

intercept

C1, C2, Cn

coefficient, which measures the contribution of independent factors

CF1, CF2,..., CFn

variation of landslide causative factor

xi'

normalized input

xi, xmin, xmax

the actual input data, minimum and maximum input data

xi

input

wi

weight of input

yi

result of artificial neuron network

G

activation functions

b1

bias vector 1

W1

weight matrices 1

b2

bias vector 2

W2

weight matrices 2

Zseventh

value of landslide causative factor on test seventh

LS ANN

the final landslide susceptibility map calculated for each pixel

fwi

weight of each causative factor

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Notes

Acknowledgements

The authors thank Esri Indonesia for supporting the ARC GIS 10.3 in collaboration with Hasanuddin University, Indonesia.

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Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of ForestryHasanuddin UniversityMakassarIndonesia
  2. 2.Faculty of AgricultureKyushu UniversityFukuokaJapan

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